Image segmentation method and system for liquid crystal display

Abstract

The present invention relates to the field of machine vision technology, and in particular to an image segmentation method and system for liquid crystal display (LCD) screens. The method comprises the following steps: acquiring basic LCD screen data; performing screen response characteristic analysis based on the basic LCD screen data to obtain screen response characteristic data; performing multi-scale image acquisition based on the basic LCD screen data to generate multi-scale image data; classifying LCD screen characteristics based on the screen response characteristic data, and performing edge compensation analysis on the multi-scale image data to obtain edge feature compensation data; acquiring real-time touch screen data; and performing touch interference recognition analysis on the real-time touch screen data based on the edge feature compensation data to generate touch interference mask data. By analyzing the screen response characteristics and performing edge compensation analysis, the present invention reduces edge blurring caused by the limited response time of the LCD, thereby improving the accuracy of edge detection.

Description

Technical Field

[0001] The present invention relates to the field of machine vision technology, and in particular to an image segmentation method and system for liquid crystal display. Background Art

[0002] With the increasing performance of LCD display devices, especially the prevalence of high-resolution, large-size, and high-refresh-rate screens, accurately and efficiently processing and segmenting screen content has become a significant technical challenge. Image segmentation for LCD displays involves analyzing and processing images on the screen to extract the target region from the background or other irrelevant information. This technology plays a crucial role in computer vision, augmented reality, touchscreen devices, and smart device displays. However, traditional image segmentation methods for LCD displays often suffer from the following issues: When displaying fast-moving objects, the response time limitations of the liquid crystal can lead to blurred edges, affecting the effectiveness of edge detection-based segmentation methods. It is necessary to consider the physical properties of LCD screens and develop segmentation algorithms that can handle dynamic scenes. On touchscreen devices, user touch operations can interfere with the image segmentation process. Region growing-based methods, in particular, require segmentation algorithms that can distinguish and filter out touch interference. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide an image segmentation method and system for liquid crystal display to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for image segmentation for liquid crystal display includes the following steps:

[0005] Step S1: acquiring basic data of the LCD screen; performing screen response characteristic analysis based on the basic data of the LCD screen to obtain screen response characteristic data; performing multi-scale image acquisition based on the basic data of the LCD screen to generate multi-scale image data; classifying the characteristics of the LCD screen based on the screen response characteristic data, and performing edge compensation analysis on the multi-scale image data to obtain edge feature compensation data;

[0006] Step S2: Acquire real-time data of the touch screen; perform touch interference recognition analysis on the real-time data of the touch screen according to the edge feature compensation data to generate touch interference mask data, wherein the touch interference includes pressure deformation interference, electric field interference, refresh rate interference, and timing interference;

[0007] Step S3: performing edge feature correction processing on the edge feature compensation data according to the touch interference mask data, and performing adaptive threshold segmentation to obtain preliminary image segmentation data; performing temporal consistency constraint processing on the preliminary image segmentation data according to the screen response characteristic data, thereby generating segmentation strategy data;

[0008] Step S4: real-time image data acquisition is performed on the LCD screen, and moving target detection is performed based on the real-time image data to obtain moving target data; region growing processing is performed based on the segmentation strategy data and the moving target data to obtain preliminary segmentation region data;

[0009] Step S5: Perform edge refinement processing on the preliminary segmented area data, evaluate the segmentation quality, and optimize the segmentation results to obtain the final segmentation result data.

[0010] The present invention obtains the basic data of the LCD screen to perform response characteristic analysis, and combines it with multi-scale image acquisition to generate data. Through response characteristic analysis, the dynamic behavior of the screen can be deeply understood, including its refresh rate, response time, etc., which lays the foundation for subsequent edge feature compensation. Multi-scale image acquisition can capture the performance of the LCD screen at different resolutions, which helps to more comprehensively analyze and deal with edge blur problems, especially when dealing with complex image structures. The acquisition of real-time data from the touch screen and interference identification are key steps to ensure the quality of image segmentation; by generating a touch interference mask, common interferences such as pressure deformation and electric field interference can be effectively identified and eliminated, which greatly improves the accuracy of image segmentation. Especially in scenarios with high refresh rates and complex touch operations, the impact of timing interference on image segmentation may be significant, so this step helps to shield external interference and ensure the authenticity and consistency of the data. Based on the touch interference mask data, the edge features are corrected and adaptive threshold segmentation is performed, which can make image segmentation more accurate and reduce the impact of edge blur. Temporal consistency constraints enable the segmentation strategy to consider not only spatial features but also the temporal response behavior of the LCD screen, ensuring consistent image segmentation across consecutive frames and avoiding image drift or edge distortion caused by screen response time. Real-time acquisition of LCD screen image data, combined with motion target detection, effectively distinguishes between static and dynamic objects. The inclusion of motion target data makes image segmentation more dynamically adaptable, making it particularly suitable for scenes displaying moving images on LCD screens. Region growing creates a more natural and smooth segmentation, reducing discontinuities caused by screen characteristics or touch interference. Final edge refinement is a key step in ensuring high-quality segmentation results. Refining the initial segmented regions not only improves edge clarity but also effectively reduces artifacts and noise. Through quality assessment and optimization, the final segmentation results are more accurate, especially when processing complex scenes and highly dynamic images. The optimized segmentation results better meet practical application requirements. Through multi-level analysis and processing, this method combines the response characteristics of LCD screens with touch interference recognition and image segmentation, effectively improving the accuracy and stability of segmentation. It is particularly suitable for LCD screen image segmentation in complex scenes and dynamic images. This not only reduces the segmentation deviation caused by the characteristics of LCD screens, but also improves the segmentation robustness under touch operations.

[0011] The present invention further provides an image segmentation system for liquid crystal display, which is used to execute the image segmentation method for liquid crystal display as described above. The image segmentation system for liquid crystal display comprises:

[0012] The screen characteristic analysis module is used to obtain basic data of the LCD screen; perform screen response characteristic analysis based on the basic data of the LCD screen to obtain screen response characteristic data; perform multi-scale image acquisition based on the basic data of the LCD screen to generate multi-scale image data; classify the characteristics of the LCD screen based on the screen response characteristic data, and perform edge compensation analysis on the multi-scale image data to obtain edge feature compensation data;

[0013] A touch interference recognition module is used to obtain real-time data from the touch screen; perform touch interference recognition analysis on the real-time data of the touch screen based on the edge feature compensation data, and generate touch interference mask data, where touch interference includes pressure deformation interference, electric field interference, refresh rate interference, and timing interference;

[0014] A preliminary segmentation module is used to perform edge feature correction processing on the edge feature compensation data based on the touch interference mask data, and perform adaptive threshold segmentation to obtain preliminary image segmentation data; and perform temporal consistency constraint processing on the preliminary image segmentation data based on the screen response characteristic data to generate segmentation strategy data;

[0015] The moving target detection module is used to collect real-time image data of the LCD screen and perform moving target detection based on the real-time image data to obtain moving target data; perform region growing processing based on the segmentation strategy data and the moving target data to obtain preliminary segmentation region data;

[0016] The edge refinement module is used to perform edge refinement processing on the preliminary segmentation area data, evaluate the segmentation quality, and optimize the segmentation results to obtain the final segmentation result data.

[0017] The present invention acquires basic LCD screen data and performs screen response characteristic analysis, multi-scale image acquisition, characteristic classification, and edge compensation analysis. The acquisition of basic data provides the foundation for subsequent analysis, ensuring accuracy. By analyzing the screen response characteristics, LCD screen performance indicators such as response time and contrast can be evaluated, helping to identify the quality of the display. Image data at different scales is generated, facilitating subsequent detailed analysis and processing, particularly for objects of varying sizes and levels of detail. By classifying screen characteristics, appropriate compensation methods can be selected to improve edge processing, enhance final image quality, and reduce distortion caused by screen characteristic differences. Real-time touchscreen data is acquired, touch interference identification and analysis performed, and touch interference mask data generated. Timely monitoring of the touchscreen status effectively identifies any potential interference sources. Identifying various interference factors, such as pressure deformation, electric field, refresh rate, and timing effects, allows precise location and classification of interference types, facilitating subsequent compensation and adjustment. The generated touch interference mask data provides a basis for subsequent processing, enabling the touch control system to more accurately filter interference and enhance the touch experience. Edge feature compensation data is corrected, adaptive threshold segmentation is performed, and segmentation strategy data is generated. By correcting edges, the accuracy of segmented regions is improved and segmentation errors are reduced. Dynamically adjusting the threshold based on image content enables more precise segmentation, improving segmentation between different backgrounds and foregrounds. This ensures temporal consistency in segmentation results, particularly when processing dynamic scenes, reducing errors and improving segmentation stability. Real-time image data acquisition and moving object detection are combined with region growing. Real-time image acquisition enables rapid identification and tracking of moving objects, adapting to rapidly changing scenes. The ability to accurately detect moving objects enables rapid response, making the system suitable for a variety of applications, such as security monitoring and automated inspection. Region growing techniques effectively extract target regions, ensuring the integrity and accuracy of moving objects and optimizing segmentation results. Edge refinement and segmentation quality assessment are performed on the initially segmented regions to optimize the segmentation results. This improves edge clarity in the segmented regions, ensures the accuracy of target shapes, and reduces blur and irregularities. Segmentation quality assessment allows for rapid identification of substandard areas, guiding subsequent optimization and improving overall segmentation quality. The optimized segmentation results better meet actual application requirements, improving system reliability and accuracy. These modules work together to improve the performance of LCD and touchscreen systems through efficient data acquisition, interference identification, feature analysis, and dynamic processing. The system design ensures that each link can respond to changes accurately and promptly, ultimately achieving high-quality image segmentation and accurate moving object detection for a variety of practical application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0019] Figure 1 Schematic diagram of the steps of the image segmentation method for liquid crystal display of the present invention;

[0020] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0021] Figure 3 for Figure 1 Detailed step flow chart of step S2 in FIG. DETAILED DESCRIPTION

[0022] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0023] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0024] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0025] To achieve this, please refer to Figures 1 to 3 The present invention provides an image segmentation method for liquid crystal display, the method comprising the following steps:

[0026] Step S1: acquiring basic data of the LCD screen; performing screen response characteristic analysis based on the basic data of the LCD screen to obtain screen response characteristic data; performing multi-scale image acquisition based on the basic data of the LCD screen to generate multi-scale image data; classifying the characteristics of the LCD screen based on the screen response characteristic data, and performing edge compensation analysis on the multi-scale image data to obtain edge feature compensation data;

[0027] Step S2: Acquire real-time data of the touch screen; perform touch interference recognition analysis on the real-time data of the touch screen according to the edge feature compensation data to generate touch interference mask data, wherein the touch interference includes pressure deformation interference, electric field interference, refresh rate interference, and timing interference;

[0028] Step S3: performing edge feature correction processing on the edge feature compensation data according to the touch interference mask data, and performing adaptive threshold segmentation to obtain preliminary image segmentation data; performing temporal consistency constraint processing on the preliminary image segmentation data according to the screen response characteristic data, thereby generating segmentation strategy data;

[0029] Step S4: real-time image data acquisition is performed on the LCD screen, and moving target detection is performed based on the real-time image data to obtain moving target data; region growing processing is performed based on the segmentation strategy data and the moving target data to obtain preliminary segmentation region data;

[0030] Step S5: Perform edge refinement processing on the preliminary segmented area data, evaluate the segmentation quality, and optimize the segmentation results to obtain the final segmentation result data.

[0031] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of an image segmentation method for liquid crystal display according to the present invention. In this example, the image segmentation method for liquid crystal display includes the following steps:

[0032] Step S1: acquiring basic data of the LCD screen; performing screen response characteristic analysis based on the basic data of the LCD screen to obtain screen response characteristic data; performing multi-scale image acquisition based on the basic data of the LCD screen to generate multi-scale image data; classifying the characteristics of the LCD screen based on the screen response characteristic data, and performing edge compensation analysis on the multi-scale image data to obtain edge feature compensation data;

[0033] In this embodiment of the present invention, a sensor module connected to an LCD screen collects basic data, including screen size (e.g., 65 inches), resolution (e.g., 1920x1080), and pixel density (e.g., 100 PPI). This data is standardized through a preprocessing algorithm to ensure consistency. Next, based on the LCD screen specifications, the system runs a screen response characteristic analysis algorithm, using historical data and experimental results to generate response characteristic data such as response time (e.g., 5ms) and dynamic contrast ratio (e.g., 1000:1). At the same time, the system sets up a multi-scale image acquisition scheme, capturing images of different resolutions (e.g., 480p, 720p, and 1080p) in layers by adjusting the lens focal length and exposure time, and storing them as multi-scale image data. Finally, the system uses the response characteristic data to classify the LCD screen characteristics, uses a decision tree algorithm to analyze each classification feature, and applies edge detection technology (e.g., the Sobel algorithm) to perform edge compensation on the multi-scale image data, ultimately outputting edge feature compensation data.

[0034] Step S2: Acquire real-time data of the touch screen; perform touch interference recognition analysis on the real-time data of the touch screen according to the edge feature compensation data to generate touch interference mask data, wherein the touch interference includes pressure deformation interference, electric field interference, refresh rate interference, and timing interference;

[0035] The embodiment of the present invention collects touch coordinates, pressure value (e.g. 5N) and touch area (e.g. 10cm) in real time through the touch screen sensor. 2 ) to form real-time data of the touch screen. The system then uses edge feature compensation data to implement a touch interference recognition algorithm. Specific operations include comparing real-time data with a preset interference model to identify pressure deformation (such as false touches caused by screen bending), electric field interference (such as static electricity), refresh rate effects (such as blur caused by low refresh rates), and timing effects (such as delays). After recognition, the system generates touch interference mask data, marks the interference area, and outputs it for subsequent processing.

[0036] Step S3: performing edge feature correction processing on the edge feature compensation data according to the touch interference mask data, and performing adaptive threshold segmentation to obtain preliminary image segmentation data; performing temporal consistency constraint processing on the preliminary image segmentation data according to the screen response characteristic data, thereby generating segmentation strategy data;

[0037] In an embodiment of the present invention, the system performs edge feature correction processing on the edge feature compensation data based on the touch interference mask data. Specifically, the system uses a region-based edge adjustment algorithm to perform local corrections on the disturbed area and uses an interpolation method to fill in the missing edge data. After the correction is completed, the system applies an adaptive threshold segmentation algorithm to automatically determine the segmentation threshold based on the local brightness and contrast of the image to obtain preliminary image segmentation data. Next, the system uses temporal consistency constraint processing and a Kalman filter to smooth the preliminary image segmentation data to ensure segmentation consistency in dynamic scenes, and finally generates segmentation strategy data.

[0038] Step S4: real-time image data acquisition is performed on the LCD screen, and moving target detection is performed based on the real-time image data to obtain moving target data; region growing processing is performed based on the segmentation strategy data and the moving target data to obtain preliminary segmentation region data;

[0039] The embodiment of the present invention performs real-time image data acquisition on the LCD screen, adopting high-frequency sampling (e.g., 60fps) to capture dynamic display content. Subsequently, the system applies a foreground extraction algorithm, and utilizes optical flow analysis and inter-frame difference technology to identify and extract moving target data. Specific operations include calculating the pixel changes between each frame, finding the moving area, and marking it. Then, based on the segmentation strategy data and the moving target data, the system implements region growing processing. By setting a seed point (e.g., the center position of the moving target) and using a regional similarity metric, the identification area is gradually expanded to obtain preliminary segmentation region data, ensuring that the boundary of the target is accurately captured.

[0040] Step S5: Perform edge refinement processing on the preliminary segmented area data, evaluate the segmentation quality, and optimize the segmentation results to obtain the final segmentation result data.

[0041] When the embodiment of the present invention performs edge refinement processing on the preliminary segmented area data, the system uses a high-precision edge detection algorithm, such as the Canny algorithm, to extract the edge information of the segmented area. In order to improve the accuracy, the system will perform sub-pixel refinement processing on the edge data and use the quadratic interpolation method to fine-tune the edge position. Next, the system analyzes the accuracy of the segmentation results through the segmentation quality assessment algorithm, and identifies areas with quality below the preset threshold (such as areas with blurred edges) and marks them as areas to be optimized. Finally, the system applies the optimization strategy of local threshold adjustment and region merging and splitting operations to reprocess the area to be optimized, thereby obtaining the final segmentation result data to ensure the accuracy and completeness of the segmentation.

[0042] The present invention obtains the basic data of the LCD screen to perform response characteristic analysis, and combines it with multi-scale image acquisition to generate data. Through response characteristic analysis, the dynamic behavior of the screen can be deeply understood, including its refresh rate, response time, etc., which lays the foundation for subsequent edge feature compensation. Multi-scale image acquisition can capture the performance of the LCD screen at different resolutions, which helps to more comprehensively analyze and deal with edge blur problems, especially when dealing with complex image structures. The acquisition of real-time data from the touch screen and interference identification are key steps to ensure the quality of image segmentation; by generating a touch interference mask, common interferences such as pressure deformation and electric field interference can be effectively identified and eliminated, which greatly improves the accuracy of image segmentation. Especially in scenarios with high refresh rates and complex touch operations, the impact of timing interference on image segmentation may be significant, so this step helps to shield external interference and ensure the authenticity and consistency of the data. Based on the touch interference mask data, the edge features are corrected and adaptive threshold segmentation is performed, which can make image segmentation more accurate and reduce the impact of edge blur. Temporal consistency constraints enable the segmentation strategy to consider not only spatial features but also the temporal response behavior of the LCD screen, ensuring consistent image segmentation across consecutive frames and avoiding image drift or edge distortion caused by screen response time. Real-time acquisition of LCD screen image data, combined with motion target detection, effectively distinguishes between static and dynamic objects. The inclusion of motion target data makes image segmentation more dynamically adaptable, making it particularly suitable for scenes displaying moving images on LCD screens. Region growing creates a more natural and smooth segmentation, reducing discontinuities caused by screen characteristics or touch interference. Final edge refinement is a key step in ensuring high-quality segmentation results. Refining the initial segmented regions not only improves edge clarity but also effectively reduces artifacts and noise. Through quality assessment and optimization, the final segmentation results are more accurate, especially when processing complex scenes and highly dynamic images. The optimized segmentation results better meet practical application requirements. Through multi-level analysis and processing, this method combines the response characteristics of LCD screens with touch interference recognition and image segmentation, effectively improving the accuracy and stability of segmentation. It is particularly suitable for LCD screen image segmentation in complex scenes and dynamic images. This not only reduces the segmentation deviation caused by the characteristics of LCD screens, but also improves the segmentation robustness under touch operations.

[0043] Preferably, step S1 includes the following steps:

[0044] Step S11: collecting information on the size, resolution, and pixel density of the LCD screen to obtain screen physical property data;

[0045] Step S12: acquiring electrical parameters of the liquid crystal display screen based on the driving voltage and refresh rate, thereby obtaining screen electrical characteristic data;

[0046] Step S13: merging the screen electrical characteristic data and the screen physical characteristic data into LCD basic data;

[0047] Step S14: performing screen response characteristic analysis based on the basic data of the LCD screen to obtain screen response characteristic data;

[0048] Step S15: designing a multi-scale sampling scheme based on the screen physical characteristic data, and using the sampling parameter data to perform multi-scale image acquisition on the LCD display content to generate multi-scale image data;

[0049] Step S16: classifying the characteristics of the liquid crystal screen according to the screen response characteristic data, and performing dynamic edge compensation analysis on the multi-scale image data to obtain edge feature compensation data.

[0050] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S1 in the embodiment of the present invention, step S1 includes the following steps:

[0051] Step S11: collecting information on the size, resolution, and pixel density of the LCD screen to obtain screen physical property data;

[0052] In this embodiment of the present invention, the system collects the physical parameters of the LCD screen, including size (e.g., 65 inches), resolution (e.g., 1920x1080 pixels), and pixel density (e.g., 100 PPI), through sensors and measurement equipment connected to the LCD screen. This data is standardized through a data processing module to ensure format consistency and prepare for subsequent analysis. The system records this information as screen physical characteristic data, forming the first part of the basic data set.

[0053] Step S12: acquiring electrical parameters of the liquid crystal display based on the driving voltage and refresh rate, thereby obtaining screen electrical characteristic data;

[0054] In this embodiment of the present invention, electrical measuring instruments are used to obtain the electrical parameters of the LCD display, specifically the drive voltage (e.g., 5V) and refresh rate (e.g., 60Hz). Using an oscilloscope and voltage probe, the system monitors the LCD's electrical signals in real time, recording its electrical characteristics under different operating conditions. The system then analyzes this data and extracts the electrical characteristic data as another component of the LCD's basic data.

[0055] Step S13: merging the screen electrical characteristic data and the screen physical characteristic data into LCD basic data;

[0056] This embodiment of the present invention combines the screen physical and electrical characteristic data acquired in steps S11 and S12 to form a complete LCD basic data set. Specifically, a data fusion algorithm is used to match the two sets of data by attribute and store them in a database. This ensures that all characteristics of each screen can be referenced in subsequent analysis, providing a comprehensive information foundation for screen response characteristic analysis.

[0057] Step S14: performing screen response characteristic analysis based on the basic data of the LCD screen to obtain screen response characteristic data;

[0058] This embodiment of the present invention systematically analyzes screen response characteristics based on the combined basic LCD screen data. Using data mining and statistical analysis methods, it quantitatively evaluates screen characteristics such as response time, dynamic contrast, and brightness uniformity. This involves establishing a mathematical model, comparing current data with historical experimental data, and ultimately generating screen response characteristic data that reflects the screen's performance under different operating conditions.

[0059] Step S15: designing a multi-scale sampling scheme based on the screen physical characteristic data, and using the sampling parameter data to perform multi-scale image acquisition on the LCD display content to generate multi-scale image data;

[0060] In this embodiment of the present invention, the system designs a multi-scale sampling scheme based on the screen's physical property data. This scheme includes determining the sampling level (e.g., Full HD and Ultra HD) and the sampling frequency (e.g., 30 frames per second). The system automatically adjusts camera parameters to accommodate different resolutions by controlling the signal generation module, capturing the corresponding LCD display content. Ultimately, the system-generated multi-scale image data is stored for subsequent analysis.

[0061] Step S16: classifying the characteristics of the liquid crystal screen according to the screen response characteristic data, and performing dynamic edge compensation analysis on the multi-scale image data to obtain edge feature compensation data.

[0062] In this embodiment of the present invention, the system classifies LCD screens based on their response characteristics. Machine learning algorithms (such as support vector machines) are used to train and classify screens to identify different screen types (e.g., high-contrast screens, fast-response screens, etc.). For each screen type, the system then performs dynamic edge compensation analysis on multi-scale image data, applying edge detection algorithms (such as the Canny algorithm) to obtain corresponding edge feature compensation data, thereby improving the accuracy of subsequent processing.

[0063] This invention accurately characterizes the fundamental physical properties of LCD screens. These data directly impact the screen's display quality, particularly in applications involving high-precision displays and large screen sizes. These physical properties provide a foundational reference for the design of multi-scale sampling schemes in subsequent steps, facilitating more accurate image processing. By gaining a deeper understanding of the screen's electrical characteristics, it is possible to better predict and adjust the impact of factors such as the LCD's response time, brightness variations, and current fluctuations on image display quality. These parameters are crucial for handling high refresh rate displays and addressing response delays, helping to ensure display stability. Combining physical and electrical properties enables a comprehensive description of the LCD screen's overall characteristics. By integrating these two types of data, the system can more accurately simulate the LCD's response behavior under different conditions, providing a more accurate basis for subsequent analysis of screen response characteristics and image processing. Based on a comprehensive analysis of physical and electrical parameters, it is possible to effectively predict screen characteristics such as response speed, refresh rate fluctuation, and color rendering under different operating conditions. These response characteristics are key factors affecting image display quality and edge definition, and this analysis can better guide subsequent edge feature compensation. Through multi-scale sampling, the system can acquire rich image data at varying resolutions and scales. Multi-scale images help capture details in LCD displays while adapting to image performance across different screen sizes and pixel densities, offering particular advantages when processing complex images or large displays. Dynamic edge compensation analysis adjusts image edge processing based on the LCD's response characteristics, reducing edge blurring caused by response delays, refresh rate fluctuations, and other factors. By processing the characteristics within each category separately, edge compensation can be precisely optimized for different situations, ensuring a clearer and more stable final display. The entire process comprehensively captures the characteristics of LCD screens through a combination of physical and electrical characterization. Multi-scale acquisition and dynamic edge compensation enhance the flexibility and accuracy of image processing. Physical characteristic data guides image acquisition design, while electrical characteristic analysis helps better understand the screen's response behavior. This integrated approach ensures high-quality segmentation and edge processing for LCD images, demonstrating superior performance at high refresh rates and in complex scenes.

[0064] Preferably, step S14 includes the following steps:

[0065] Step S141: performing a grayscale switching response time test according to basic data of the LCD screen, thereby obtaining grayscale response time data;

[0066] The embodiment of the present invention first prepares a standard LCD screen test device that can quickly switch grayscale values. Before the test begins, the system records the current LCD screen settings, including brightness and contrast. The device then switches between different grayscale values (such as from black to white) at a preset frequency, while using a high-precision photoelectric sensor to measure the response time required by the LCD screen for each switch. The data is recorded and stored in real time. After multiple tests, the system calculates the average response time and standard deviation, ultimately generating grayscale response time data for subsequent analysis.

[0067] Step S142: performing dynamic contrast analysis based on the grayscale response time data to generate dynamic contrast characteristic data;

[0068] In this embodiment of the present invention, the system uses the grayscale response time data obtained in step S141 to perform dynamic contrast analysis. During operation, the system creates a database containing different grayscale values and their corresponding response times. Then, a curve-fitting algorithm is applied to analyze the relationship between response time and contrast using linear or polynomial regression to determine the dynamic contrast trend. The resulting dynamic contrast characteristic data is stored to evaluate the LCD screen's performance in rapidly changing images.

[0069] Step S143: testing and analyzing pixel overshoot and undershoot according to the screen electrical characteristic data to obtain pixel stability data;

[0070] The system in this embodiment of the present invention uses the screen's electrical characteristic data to test each pixel for overshoot and undershoot. Specifically, the system applies rapid voltage pulses to the LCD screen via a signal generator and then uses an oscilloscope to monitor the response waveforms of each pixel. By analyzing the captured waveforms, the system identifies the pixel's overshoot and undershoot characteristics and calculates a response stability index for each pixel. Ultimately, this data is collated to generate pixel stability data, which can be used to evaluate the LCD screen's stability during dynamic image display.

[0071] Step S144: constructing a motion blur characteristic model according to the grayscale response time data and the dynamic contrast characteristic data, thereby generating motion blur characteristic data;

[0072] This embodiment of the present invention constructs a motion blur characteristic model based on grayscale response time data and dynamic contrast characteristic data. During operation, the system uses multiple regression analysis, using grayscale response time and dynamic contrast as input variables, to establish a mathematical model that predicts the degree of motion blur. By comparing the blur levels of real and moving images, the system adjusts the model parameters to optimize the accuracy of the motion blur characteristic data, ensuring that the model effectively describes the blur effect at different motion speeds.

[0073] Step S145: performing inter-frame interpolation capability analysis based on the refresh rate information in the screen electrical characteristic data, thereby obtaining frame interpolation characteristic data;

[0074] The system of the embodiment of the present invention performs inter-frame interpolation capability analysis based on the refresh rate information in the screen electrical characteristic data. The specific operation includes collecting the display content of multiple consecutive frames and comparing it with the current refresh rate to evaluate its impact on image smoothness. Using an interpolation algorithm (such as bilinear interpolation or cubic interpolation), the system calculates the smooth transition between each frame and generates frame interpolation characteristic data to characterize the smoothness of the LCD screen in dynamic scenes.

[0075] Step S146: performing data integration based on high-dimensional feature fusion on the pixel stability data, the frame interpolation characteristic data, the motion blur characteristic data, and the grayscale response time data, thereby obtaining screen response characteristic data.

[0076] The system of this embodiment of the present invention performs high-dimensional feature fusion on the pixel stability data, frame interpolation characteristic data, motion blur characteristic data, and grayscale response time data obtained in steps S143, S144, and S145. Specifically, dimensionality reduction techniques such as principal component analysis (PCA) are applied to integrate this multidimensional data into a comprehensive dataset, extracting key features to form screen response characteristic data. This dataset is then used to evaluate the overall performance of the LCD screen and provide data support for subsequent optimization.

[0077] The grayscale response time of the present invention is a key indicator for measuring the time required for the liquid crystal screen to switch from one grayscale (brightness level) to another. By accurately measuring the grayscale switching time, the response speed of the liquid crystal screen can be understood, especially its performance in high dynamic scenes. The grayscale switching speed directly affects the smoothness and image quality of the image display. A faster grayscale response can reduce ghosting and blurring, and improve the visual experience. Dynamic contrast refers to the brightness ratio of the liquid crystal screen under different brightness conditions. By analyzing the grayscale response time, the dynamic changes in the contrast of the screen can be evaluated, and the performance of the liquid crystal screen in scenes with drastic changes in light and dark can be better optimized, ensuring that the dark details are as clear as the highlights, thereby improving the image quality. The improvement of dynamic contrast can effectively enhance visual impact, especially in the application of high dynamic range (HDR) display technology. Overshoot and undershoot phenomena will cause inaccurate brightness performance of the image, affecting the overall image quality. By testing these phenomena, it can be ensured that each pixel can accurately reach the expected value when responding, thereby improving the stability and consistency of the display, reducing image distortion, and improving the viewing experience. By building a motion blur model, the system can predict and correct blur caused by variations in response time and contrast, helping to improve image clarity and sharpness, which is particularly crucial for high-dynamic scenes such as gaming and sports. Interframe interpolation, a technique that generates transition frames between two frames, improves image smoothness and fluidity. Analyzing frame interpolation capabilities can effectively reduce stuttering and judder in low-frame-rate displays or scenes with complex motion, enhancing the overall viewing experience. Especially for LCD screens with high refresh rates (such as 120Hz and 240Hz), good frame interpolation performance helps to achieve smoother and more natural motion. By fusing multidimensional data, a comprehensive screen response characteristic data model is formed, fully characterizing the LCD screen's performance under different conditions. This high-dimensional feature fusion more accurately reflects the LCD screen's dynamic response capabilities, especially in rapidly changing scenes. This integrated data can help optimize the LCD screen's overall display quality. The entire process builds a highly accurate LCD screen response characteristic model by comprehensively analyzing and integrating grayscale response time, dynamic contrast, pixel stability, frame interpolation capabilities, and motion blur characteristics. Based on grayscale response time testing, dynamic contrast analysis enhances detail in brightness changes, pixel overshoot and undershoot testing ensures display accuracy, motion blur modeling reduces blur in high-speed motion, and frame interpolation analysis ensures smooth viewing. Ultimately, through high-dimensional feature fusion, this systematic analysis ensures that LCD screens deliver excellent display quality in a variety of complex scenarios.

[0078] Preferably, step S16 includes the following steps:

[0079] Step S161: establishing a screen response characteristic classification model according to the grayscale response time data, dynamic contrast characteristic data, and pixel stability data in the screen response characteristic data;

[0080] This embodiment of the present invention collects data on grayscale response time, dynamic contrast characteristics, and pixel stability of LCD screens. This data is obtained through experimental measurements and then used to build a classification model using machine learning algorithms such as linear regression, decision trees, or support vector machines. The model aims to classify LCD screens based on their response characteristics. The specific steps include data preprocessing, feature selection, and model training, ultimately generating a model that can be used to classify screen response characteristics.

[0081] Step S162: Identifying the type of the LCD screen according to the screen response characteristic classification model, thereby generating screen type identification data, wherein the screen type identification data includes a high grayscale response time type, a high dynamic contrast type, an unstable pixel type, and a standard response type;

[0082] The screen response characteristic classification model established in this embodiment of the present invention identifies LCD screen types. The input is previously collected screen response characteristic data, and the model outputs identifications of different screen types. Specifically, the input data is normalized and predicted using the classification model to generate screen type identification data. Based on the model results, the identification data can be categorized as high grayscale response time, high dynamic contrast, unstable pixel, and standard response, facilitating subsequent processing.

[0083] Step S163: selecting an edge detection algorithm based on the screen type identification data, and extracting edge features from the multi-scale image data using the selected edge detection algorithm, thereby obtaining preliminary edge feature data. Specifically, the algorithm selected is: for a screen with a high grayscale response time, the Canny edge detection algorithm is selected; for a screen with a high dynamic contrast ratio, the Sobel algorithm is selected; for a screen with an unstable pixel type, the Laplacian algorithm is selected; and for a screen with a standard response type, the Prewitt algorithm is selected.

[0084] This embodiment of the present invention selects an appropriate edge detection algorithm based on the screen type identification data generated in step S162. Specifically, the screen type is first determined, and then a corresponding edge detection algorithm is assigned to each type: the Canny algorithm is used for high grayscale response time types, the Sobel algorithm is used for high dynamic contrast types, the Laplacian algorithm is used for pixel instability types, and the Prewitt algorithm is used for standard response types. After selecting the algorithm, the selected edge detection algorithm is used to process the multi-scale image data and extract preliminary edge feature data, ensuring that the extraction results reflect the edge characteristics of different screen types.

[0085] Step S164: constructing a dynamic edge blur compensation model according to the screen response characteristic data, and performing edge compensation analysis on the preliminary edge feature data using the dynamic edge blur compensation model, thereby obtaining edge feature compensation data.

[0086] This embodiment of the present invention utilizes screen response characteristic data to construct a dynamic edge blur compensation model. Initial edge feature data is first analyzed to determine the characteristics and influencing factors of the blur phenomenon. Based on these characteristics, a model is designed. Signal processing techniques, such as Fourier transforms or wavelet transforms, are then used to analyze the impact of edge blur on image quality. Once the model is established, it is applied to the initial edge feature data for edge compensation analysis, generating edge feature compensation data to improve image clarity and edge accuracy.

[0087] The present invention classifies and models the key response characteristics of LCD screens, and can accurately classify them according to the actual performance characteristics of different LCD screens. This classification model provides a customized basis for subsequent image processing and algorithm selection, especially for different types of LCD screens, it has a significant optimization effect when processing image features. For example, some LCD screens may respond slowly to grayscale switching, while others may exhibit higher dynamic contrast, so classifying them helps to optimize subsequent steps for different problems. The classification model identifies specific types of LCD screens, such as those with high grayscale response time, high dynamic contrast, and unstable pixels, and generates corresponding type identification data. This enables the system to perform targeted image processing and optimization based on the recognition results, thereby improving the flexibility and adaptability of the overall processing flow. For different types of screens, the use of corresponding optimization strategies can effectively improve the image processing effect and avoid unnecessary losses caused by blindly adopting a unified processing method. The system automatically selects the most appropriate edge detection algorithm based on different LCD screen types. For example, the Canny algorithm is selected for screens with high grayscale response times to more effectively address edge blurring caused by slow grayscale response. For screens with high dynamic contrast, the Sobel algorithm better captures edge variations caused by brightness differences. For screens with unstable pixels, the Laplacian algorithm is used to better eliminate detail loss caused by overshoot and undershoot. For screens with standard response, the Prewitt algorithm is used for stable edge detection. This differentiated algorithm selection improves edge detection accuracy, ensuring optimal edge detection results for different screen characteristics. Using a dynamic edge blur compensation model, the system dynamically optimizes edge detection results to compensate for edge blurring caused by factors such as LCD screen response time, contrast, and pixel instability. This compensation model is particularly effective when processing moving images or images with high refresh rates, effectively improving edge clarity and stability, reducing blur and aliasing, and enhancing display quality. By using compensation strategies tailored to different screen types, high-precision edge features are ensured, ultimately improving image segmentation quality. The entire process establishes a classification model for LCD screen response characteristics, selects appropriate edge detection algorithms based on different screen types, and combines this with a dynamic edge blur compensation model to form a highly efficient edge detection and compensation mechanism. The classification model enables the system to precisely select an optimization solution based on screen characteristics. The edge detection algorithm selection improves processing performance, while the compensation model ensures the clarity of edge features. This approach enables the entire system to adapt to different LCD screen types and achieve precise edge detection and compensation, improving the quality and robustness of image processing.

[0088] Preferably, step S2 includes the following steps:

[0089] Step S21: collecting touch coordinates, pressure values, and touch area information of the touch screen in real time, thereby obtaining real-time data of the touch screen;

[0090] Step S22: Identifying and extracting the pressure deformation interference area based on the real-time data of the touch screen, thereby generating pressure deformation interference data;

[0091] Step S23: analyzing the electric field changes around the touch point based on the touch coordinate information in the real-time data of the touch screen, thereby obtaining electric field interference data;

[0092] Step S24: Analyzing the timing relationship between touch operation and screen refresh using the refresh rate information in the basic data of the LCD screen, thereby generating refresh rate impact interference data;

[0093] Step S25: analyzing the response delay of the touch operation based on the real-time data of the touch screen and the edge feature compensation data, thereby obtaining response delay interference data;

[0094] Step S26: analyzing the afterimage effect caused by the touch operation on the touch screen real-time data and the screen response characteristic data, thereby generating afterimage effect interference data;

[0095] Step S27: performing spatial superposition and temporal correlation analysis on the pressure deformation interference data, the electric field interference data, the refresh rate interference data, the response delay interference data, and the afterimage effect interference data, thereby obtaining comprehensive touch interference data;

[0096] Step S28: establishing a touch interference space-time model based on the comprehensive touch interference data, and generating preliminary touch interference mask data;

[0097] Step S29: performing edge alignment and refinement processing on the preliminary touch interference mask data using the edge feature compensation data, thereby obtaining touch interference mask data.

[0098] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 1 Detailed step flow diagram of step S2 in the embodiment of the present invention, step S2 includes the following steps:

[0099] Step S21: collecting touch coordinates, pressure values, and touch area information of the touch screen in real time, thereby obtaining real-time data of the touch screen;

[0100] The embodiments of the present invention collect touch coordinates, pressure values, and touch area information of the touch screen in real time, using a high-frequency sampling method to continuously monitor touch events on the touch screen, typically within millisecond time intervals. When a user performs a touch operation, the system uses the touch control chip to obtain the coordinate position (X, Y coordinates) of the touch point, the pressure value applied to the screen surface (typically measured using pressure sensing technology), and the touch area (calculated by contact area). This data can be updated in real time and serves as the basic input data for subsequent interference identification, resulting in real-time touch screen data.

[0101] Step S22: Identifying and extracting the pressure deformation interference area based on the real-time data of the touch screen, thereby generating pressure deformation interference data;

[0102] The embodiments of the present invention analyze the pressure deformation at and around the touch point based on the collected touch pressure values, combined with the screen's physical characteristics (such as material properties and thickness), by presetting pressure thresholds. Finite element analysis is then used to simulate the deformation area around the touch point to determine the extent of the pressure deformation. By comparing the actual pressure distribution with a model of normal touch operation, deformation areas caused by excessive or uneven pressure can be extracted and marked as pressure deformation interference areas, thereby generating pressure deformation interference data.

[0103] Step S23: analyzing the electric field changes around the touch point based on the touch coordinate information in the real-time data of the touch screen, thereby obtaining electric field interference data;

[0104] During a touch operation, the embodiments of the present invention utilize electric field sensing technology to analyze changes in the electric field around the touch point based on touch coordinate information and the characteristics of the capacitive screen. By measuring the change in capacitance around the touch point, the electric field disturbance caused by the user's finger or other object on the screen surface is inferred. When the electric field change exceeds a preset threshold, it is marked as an electric field interference area. At the same time, the electric field interference range is adjusted based on basic screen data (such as screen size and resolution), ultimately obtaining electric field interference data.

[0105] Step S24: Analyzing the timing relationship between touch operation and screen refresh using the refresh rate information in the basic data of the LCD screen, thereby generating refresh rate impact interference data;

[0106] The embodiment of the present invention uses the refresh rate information of the liquid crystal screen to compare and analyze the timestamp of the touch operation with the screen refresh cycle to determine the time synchronization between the touch event and the screen refresh event. By calculating the time difference between the time point when the touch event occurs and the screen refresh moment, it is identified whether the touch event has interfered with the screen display. If the touch event occurs at a critical moment of screen refresh (such as frame switching or refresh), it may cause display instability. In this case, the area is marked as a refresh rate interference area, thereby generating refresh rate interference data.

[0107] Step S25: analyzing the response delay of the touch operation based on the real-time data of the touch screen and the edge feature compensation data, thereby obtaining response delay interference data;

[0108] The present invention analyzes the delay between touch operations and screen responses based on touch coordinates and screen response characteristics in real-time touchscreen data. By calculating the difference between the moment a touch event occurs and the actual response time displayed on the screen, it is possible to identify significant response delays. When the delay exceeds a preset threshold (e.g., the response time is greater than a certain number of milliseconds), the area is marked as a response delay interference region, generating response delay interference data for subsequent edge feature correction.

[0109] Step S26: analyzing the afterimage effect caused by the touch operation on the touch screen real-time data and the screen response characteristic data, thereby generating afterimage effect interference data;

[0110] After a touch operation, this embodiment of the present invention uses LCD screen response characteristic data to analyze whether an afterimage effect has occurred on the screen. By comparing the screen images before and after the touch operation, combined with the LCD screen's response time and grayscale conversion characteristics, it detects whether any residual image information has not completely disappeared from the screen. Grayscale difference analysis can determine the extent and intensity of the afterimage effect, marking the affected areas and generating afterimage effect interference data.

[0111] Step S27: performing spatial superposition and temporal correlation analysis on the pressure deformation interference data, the electric field interference data, the refresh rate interference data, the response delay interference data, and the afterimage effect interference data, thereby obtaining comprehensive touch interference data;

[0112] This embodiment of the present invention performs a spatial overlay analysis on the pressure deformation interference data, electric field interference data, refresh rate interference data, response delay interference data, and afterimage effect interference data obtained above. By calculating the overlapping portions of each interference area, a comprehensive interference area is identified. Simultaneously, a temporal correlation analysis is performed to arrange the interference data in chronological order, identify which interferences occurred within the same time period, and analyze their interactions. This method can generate more accurate comprehensive touch interference data for building a comprehensive interference model.

[0113] Step S28: establishing a touch interference space-time model based on the comprehensive touch interference data, and generating preliminary touch interference mask data;

[0114] The present invention utilizes a space-time model to model touch interference based on comprehensive touch interference data. This model combines spatial location and temporal variation to analyze the impact of touch events at different times and within different regions. Spatial and temporal interpolation methods can be used to generate more refined touch interference region boundaries, and morphological operations can be used to expand or contract the interference region. Ultimately, the model outputs preliminary touch interference mask data, marking image regions that may be affected by touch.

[0115] Step S29: performing edge alignment and refinement processing on the preliminary touch interference mask data using the edge feature compensation data, thereby obtaining touch interference mask data.

[0116] This embodiment of the present invention utilizes edge feature compensation data to perform edge alignment and refinement on the preliminary touch interference mask data. First, based on the compensated edge features, the boundaries of the touch interference area are aligned to align with the true edges of the image. Then, through techniques such as morphological filtering and refinement algorithms, noise or glitches in the mask are removed to ensure smooth and accurate edges. Ultimately, high-precision touch interference mask data is generated for interference removal during subsequent image segmentation.

[0117] The present invention can obtain physical information of touch operations in real time, capturing key data such as touch position, force, and contact area. Through this information, the system can accurately identify the user's touch behavior and ensure the accuracy of touch interference analysis in subsequent steps. Especially in multi-touch scenarios, real-time data collection lays the foundation for accurate touch interference identification. By analyzing the pressure value, screen deformation interference caused by excessive pressure can be identified. Pressure deformation may cause image distortion or operation errors. By identifying these interference areas, interference between touch operations and image processing can be avoided, improving the accuracy of image display and the precision of touch response. The operating principle of the touch screen relies on electric field induction. Touch operations may cause electric field changes, which in turn affect image display or other touch operations. By analyzing electric field changes, potential electric field interference can be identified, touch recognition accuracy can be optimized, and the occurrence of false touches or touch failures can be reduced. The refresh rate of the LCD screen will affect the timing response of the touch. Especially on screens with high refresh rates, the incoordination between touch operations and screen refresh may cause image lag or frame skipping. By analyzing this timing relationship, we can reduce interference caused by touch and refresh rate mismatches, ensure that touch operations are synchronized with image refresh, and improve the responsiveness and smoothness of touch operations. We can also identify delays in touch operations, which can impact the user experience, especially in highly interactive applications, where delays can lead to delayed or erroneous responses. By analyzing delays, we can identify and mitigate delay interference, optimize system response time, and provide more timely feedback. By analyzing the afterimage effect, we can identify and address the afterimage interference caused by touch operations, reducing image blur and ensuring image clarity, especially in high-speed scenarios. Through spatial and temporal cross-comparisons, we form a global interference identification model. Comprehensive analysis helps capture the overlapping of multiple interferences in complex scenarios, enabling more comprehensive identification of touch interference and improving the accuracy and precision of interference assessment. Data from different types and dimensions of touch interference is integrated into a unified model to generate a touch interference mask. This mask masks the interfering portions of touch operations, preventing them from affecting image processing or display, thereby improving overall system stability and display accuracy. By combining it with edge feature compensation data, the touch interference mask is precisely aligned and compensated to ensure that the edges of the mask area match the actual interference area. Refined processing can improve the accuracy of the mask, avoid misidentification or omission of interference areas, thereby ensuring the final touch interference shielding effect and improving the robustness of image processing. The entire process uses multi-dimensional analysis of real-time touch screen data to identify interference such as pressure deformation, electric field changes, refresh rate effects, response delays, and afterimage effects, and ultimately generates touch interference mask data. The effect of these steps is reflected in improving the response accuracy of touch operations, reducing the impact of interference on image processing, and ensuring that the system can maintain efficient and accurate image processing capabilities in complex touch operation scenarios.Through comprehensive analysis of various interference factors and refined edge processing, we ultimately achieved effective shielding and optimization of touch interference.

[0118] Preferably, step S3 includes the following steps:

[0119] Step S31: performing mask processing on the edge feature compensation data according to the touch interference mask data, thereby obtaining preliminary edge feature correction data;

[0120] The embodiment of the present invention performs mask processing on the edge feature compensation data according to the touch interference mask data. First, the mask processing is performed through Boolean operations at the pixel level: the marked area in the touch interference mask data is bitwise operated with the edge feature compensation data. For the area marked as interference in the mask, the corresponding edge feature compensation data is set to zero or the intensity is reduced proportionally (usually weight attenuation) to eliminate the influence of touch interference. Thereafter, for areas with broken or distorted edges, the edge data of the interfered area is smoothed using interpolation algorithms such as bilinear interpolation or Gaussian smoothing to make the edge feature transition natural and maintain continuity, thereby obtaining preliminary edge feature correction data.

[0121] Step S32: performing time domain compensation on the preliminary edge feature correction data using the grayscale response time data in the screen response characteristic data, thereby obtaining time-compensated edge feature data;

[0122] An embodiment of the present invention utilizes the grayscale response time data in the screen response characteristic data to perform time domain compensation on the preliminary edge feature correction data. First, based on the grayscale response time in the screen response characteristics, the response characteristics of each pixel are analyzed to identify pixel regions with longer response times. Then, in the time domain, the edge data of these regions are accumulated, and a time-series weighted average method is used to smooth and correct the edges of delayed responses. By analyzing the changes in edge features of the previous and next frames, the influence of the tailing effect is smoothed in time. The pixel regions with slower responses are subjected to time-weighted filtering to reduce edge jitter or blurring, thereby generating time-compensated edge feature data.

[0123] Step S33: performing contrast enhancement processing on the time-compensated edge feature data and performing noise suppression processing according to the pixel stability data, thereby obtaining final edge feature correction data;

[0124] The embodiment of the present invention performs contrast enhancement processing on time-compensated edge feature data. First, a histogram stretching technique is used to analyze the grayscale distribution of the edge feature data in the current image and enhance the contrast of the grayscale values. The specific operation is to stretch the low-contrast grayscale values in the image to a wider range, thereby making the subtle edge features more prominent. Then, based on the pixel stability data, an adaptive median filtering method is used to suppress the noise area. During the processing, filtering is applied to the pixel area with poor stability to eliminate random noise, and the edge data is retained and enhanced, ultimately generating clearer edge feature data and forming the final edge feature correction data.

[0125] Step S34: constructing an adaptive threshold model based on local image features and global brightness distribution according to the final edge feature correction data, and performing segmentation processing on the final edge feature correction data according to the adaptive threshold model, thereby obtaining preliminary image segmentation data;

[0126] The embodiment of the present invention constructs an adaptive threshold model based on the final edge feature correction data. First, the image is divided into several local areas, and the grayscale histogram of each area is calculated. The local grayscale distribution characteristics are analyzed to generate a local adaptive threshold. Then, combined with the global brightness distribution, the global threshold of the image is calculated to ensure that the edge segmentation effect of different brightness areas is consistent. The specific threshold generation uses a weighted average algorithm to combine the local threshold with the global threshold. Using this adaptive threshold model, a threshold judgment is performed on each pixel, and pixels with grayscale values exceeding the threshold are marked as edge pixels, thereby segmenting the final edge feature correction data and generating preliminary image segmentation data.

[0127] Step S35: performing temporal consistency constraint processing on the preliminary image segmentation data according to the screen response characteristic data, thereby generating segmentation strategy data.

[0128] The embodiment of the present invention performs temporal consistency constraint processing on the preliminary image segmentation data based on the screen response characteristic data. First, a temporal consistency constraint model is constructed using the time response data in the screen response characteristics. In this process, the edge change trend of the moving target is analyzed by comparing the segmentation results of multiple consecutive frames. The similar edge areas segmented in adjacent frames are matched and analyzed, and the discontinuous short-time noise or false edges are eliminated by the temporal filtering algorithm to ensure the temporal continuity of the moving target. At the same time, the Kalman filter and other timing analysis techniques are used to predict and correct the rapidly changing edge areas to ensure the stability of edge segmentation in dynamic scenes. Finally, the segmentation strategy data is generated through the temporal consistency constraint to guide the subsequent segmentation processing.

[0129] The present invention uses a touch interference mask to shield interference areas caused by touch operations, preventing these interferences from affecting the accuracy of edge feature analysis. Masking can eliminate irrelevant or interfering areas, thereby making edge correction more accurate and ensuring that subsequent edge feature correction steps focus on the true image features. Grayscale response time data reflects the differences in the LCD screen's response speed when switching between different grayscale levels, which affects the dynamic changes of edge features. Time domain compensation adjusts for edge blur or delay caused by inconsistent response times, improving the temporal accuracy of edge features and reducing blurring effects in high-speed scenes. Contrast enhancement makes edge features more distinct in the image, especially enhancing edge clarity in complex image scenes. Simultaneously, noise suppression removes random noise from the image using pixel stability data, ensuring the purity and consistency of edge features. Ultimately, the edge feature correction data is more accurate and clear, laying a solid foundation for subsequent image segmentation. By constructing an adaptive threshold model, the system can dynamically adjust the segmentation threshold based on the local image details and global brightness distribution. The adaptive threshold model makes image segmentation more flexible, adapting to different lighting conditions and changes in image details, ensuring that the segmentation process can distinguish edge areas from background areas, improving segmentation accuracy. By constraining the screen response characteristic data, the segmentation results are prevented from jumping or being inconsistent in the time series, ensuring that the segmentation results of each frame remain consistent with the previous and next frames. Doing so can significantly improve the stability of image segmentation, making the segmentation strategy smoother and more reliable throughout the entire operation. The effect of the entire chain of steps is that through multiple processing methods such as mask processing, time domain compensation, contrast enhancement and noise suppression, the edge feature data is made more accurate and clear. Subsequently, the segmentation processing based on the adaptive threshold model adapts to the local and global characteristics of the image, while the temporal consistency constraint ensures the stability of the segmentation results in the time dimension; these steps ultimately improve the accuracy, stability and consistency of image segmentation, and are particularly suitable for LCD screen image processing in complex scenes.

[0130] Preferably, step S35 includes the following steps:

[0131] Step S351: establishing a temporal consistency constraint model according to the dynamic contrast characteristic data and the motion blur characteristic data in the screen response characteristic data;

[0132] An embodiment of the present invention establishes a timing consistency constraint model based on the dynamic contrast characteristic data and motion blur characteristic data in the screen response characteristic data. First, the dynamic contrast change data and motion blur characteristic data are extracted from the screen response characteristic data, and the contrast adjustment and motion blur characteristics of the screen in different scenes are analyzed. These data are used to construct a timing consistency constraint model, which ensures that the boundaries segmented in the motion scene have timing consistency by comparing the dynamic contrast changes and motion blur effects of consecutive frames. The model comprehensively considers the response time and contrast changes, so that the edges can maintain a consistent visual effect between different frames, forming a basic timing constraint model.

[0133] Step S352: using a temporal consistency constraint model to perform consistency analysis on the preliminary image segmentation data across frames, thereby obtaining temporal corrected segmentation data;

[0134] The embodiment of the present invention utilizes a temporal consistency constraint model to perform consistency analysis on the segmentation results of preliminary image segmentation data across frames. In specific operations, preliminary image segmentation data of multiple consecutive frames are input into the temporal consistency constraint model, and the model detects edge changes across frames by comparing and matching the segmentation results between adjacent frames. For erroneous segmentation areas caused by temporal inconsistency, temporal correction is performed through interpolation or filtering operations. The edge stability in consecutive frames is analyzed, and short-term edge jitter or erroneous edges are corrected to stable edge contours to ensure that the edges of moving objects remain consistent in each frame, thereby obtaining temporally corrected segmentation data.

[0135] Step S353: performing refinement processing on the segmentation boundary according to the timing correction segmentation data and the screen response characteristic data, thereby obtaining optimized segmentation data;

[0136] An embodiment of the present invention refines the segmentation boundaries based on the time-corrected segmentation data and the screen response characteristic data. First, the segmentation boundaries are refined using an edge enhancement algorithm, using the edge areas marked in the time-corrected segmentation data and the contrast and blur characteristics in the screen response characteristic data. Specifically, the Sobel operator or the Canny algorithm performs secondary edge detection on the blurred boundaries to enhance edge clarity. Then, for complex edge details, sub-pixel interpolation processing technology is used to ensure the accuracy and consistency of the boundaries, eliminate possible blur or damage, and ultimately generate optimized segmentation data.

[0137] Step S354: constructing a segmentation region growing rule set based on the optimized segmentation data, wherein the segmentation region growing rule set includes region similarity evaluation and boundary extension strategy;

[0138] This embodiment of the present invention constructs a set of segmentation region growing rules based on optimized segmentation data. First, based on the segmentation results, the similarity between the boundary of each segmented region and its adjacent regions is evaluated. The differences in color, texture, and grayscale of each region are analyzed, and this similarity data is used to construct a set of region growing rules. This rule set defines a region expansion strategy: adjacent pixels are evaluated based on a similarity score, and boundary expansion is allowed if the similarity reaches a threshold. The smoothness of the boundary shape is also considered to ensure that the boundary does not expand excessively or abnormally during expansion, thereby forming a reasonable set of region growing rules.

[0139] Step S355: Optimizing the segmentation strategy of the optimized segmentation data according to the segmentation region growing rule set, thereby generating segmentation strategy data.

[0140] This embodiment of the present invention optimizes the segmentation strategy for optimized segmentation data based on a set of segmentation region growing rules. During this process, each region in the optimized segmentation data is first expanded according to the growth rules. For each pixel, the region is gradually expanded based on similarity assessment and boundary expansion strategies to ensure pixel consistency within the region while preventing excessive overlap between regions. During the boundary expansion process, the expansion step size and similarity threshold are dynamically adjusted to ensure accuracy during the boundary expansion process. Ultimately, optimized segmentation strategy data is generated, ensuring that the segmented regions more closely match the true structure of the image.

[0141] The dynamic contrast and motion blur of the present invention reflect the visual characteristics of the image when it is displayed. By establishing a temporal consistency constraint model based on them, it is possible to ensure that the image segmentation results in consecutive frames remain consistent, and there will be no discontinuous or jittery segmentation caused by rapid scene switching or motion blur. The temporal consistency constraint model provides a temporal continuity guarantee for the image segmentation results, which is particularly suitable for dynamic display and video application scenarios. Through cross-frame analysis, inconsistent segmentation phenomena caused by inter-frame differences can be identified and corrected. Temporal correction can ensure that the segmentation results maintain the same logic between different frames, avoiding segmentation errors caused by scene changes or noise interference. This is particularly critical for processing dynamically changing images, ensuring segmentation continuity across frames and improving the overall image segmentation quality. The temporally corrected segmentation data is further refined by combining the screen response characteristics to enhance the accuracy of the segmentation results, especially when the boundaries are blurred or unclear. Through this processing step, the segmentation boundaries are clearer and sharper, especially in scenes with more severe motion blur. This refinement process greatly improves the accuracy of the boundaries. By defining regional similarity and boundary expansion strategies, the growth process of the segmented regions can be precisely controlled. Region similarity assessment ensures that the visual characteristics of the newly segmented regions are consistent with those of the existing regions, while the boundary extension strategy further optimizes the boundary processing of the segmentation results, allowing the segmented regions to expand naturally based on the image content, thereby ensuring the integrity and consistency of the segmented regions. By optimizing the segmentation region growing rules, the final segmentation strategy is made more flexible and adaptable. The region growing process not only considers similarity assessment but also adjusts boundary extension based on the local characteristics of the image to ensure the precise delineation of the segmented regions. The final segmentation strategy can provide more accurate and complete segmentation regions in complex image scenes and can adapt to dynamically changing image content. These steps further improve the accuracy, stability, and continuity of image segmentation by establishing a temporal consistency constraint model, cross-frame consistency analysis, boundary refinement, and segmentation region growing optimization. Especially in dynamic scenes, these processing steps enable image segmentation to effectively address blurring and jitter caused by screen response characteristics, ensuring the integrity and consistency of the segmentation results.

[0142] Preferably, step S4 includes the following steps:

[0143] Step S41: using the image acquisition device of the LCD screen to perform high-frequency sampling on the current display content, thereby obtaining real-time image data;

[0144] The embodiments of the present invention utilize an image acquisition device on a liquid crystal display to perform high-frequency sampling of the currently displayed content. In a specific implementation, the display content on the liquid crystal display can be continuously sampled multiple times per second using a built-in image acquisition sensor or an external camera. During the sampling process, it is necessary to ensure that the sampling frequency of the device is high enough to capture subtle changes in the screen content, especially details of fast-moving images. Each sampled image data undergoes preprocessing, such as noise reduction and color space conversion, to ensure that the captured image has high quality. Ultimately, continuous real-time image data is generated, providing a foundation for subsequent processing.

[0145] Step S42: using a foreground extraction algorithm to perform preliminary separation of the static background and the dynamic foreground on the real-time image data, and performing motion feature extraction based on optical flow analysis and inter-frame difference on the separated dynamic foreground to obtain moving target data;

[0146] The embodiment of the present invention uses a foreground extraction algorithm to perform a preliminary separation of the static background and dynamic foreground of real-time image data. First, a static background modeling method (such as a Gaussian mixture model) is used to perform background modeling on the static part of the displayed content. Then, the image of the current frame is compared with the background model to preliminarily separate the dynamic foreground. Next, the separated dynamic foreground is subjected to motion analysis using the optical flow method, and the motion trajectory of the object is extracted by the changes in the optical flow field between frames. At the same time, the inter-frame difference algorithm is used to compare consecutive frames to further confirm the motion characteristics of the dynamic foreground, and finally the motion target data is obtained to provide motion information for subsequent clustering and boundary processing.

[0147] Step S43: clustering the moving targets according to the moving target data and the regional similarity evaluation rules in the segmentation strategy data, thereby obtaining preliminary moving target candidate regions;

[0148] This embodiment of the present invention clusters moving targets based on the target data and the regional similarity assessment rules in the segmentation strategy data. First, the extracted target data is analyzed and the targets are classified based on similarity features such as color, texture, and shape using a K-means clustering or hierarchical clustering algorithm. This process ensures high internal consistency of the clustered regions based on the similarity assessment rules defined in the segmentation strategy data. By scoring the similarity of moving targets, similar targets are grouped together to form preliminary candidate moving target regions, which are then used to identify possible target objects.

[0149] Step S44: performing a motion target boundary refinement process on the preliminary motion target candidate region based on shape analysis and contour extraction algorithm to generate accurate motion target contour data;

[0150] The embodiment of the present invention performs a refinement process on the boundaries of the preliminary moving target candidate area based on shape analysis and contour extraction algorithms. The preliminary boundaries of the moving target are extracted using a contour detection algorithm (such as Canny edge detection), and then a shape analysis method, such as Hough transform or shape context algorithm, is used to analyze the detected boundaries and remove possible noise and pseudo contours. To further enhance accuracy, the target contour is adjusted using a shape constraint model to ensure that the final contour is consistent with the shape of the actual moving object, ultimately generating accurate moving target contour data.

[0151] Step S45: Determine the seed point and growth direction of region growth according to the precise moving target contour data and the segmentation strategy data, and perform adaptive region growing processing according to the region growing rule in the segmentation strategy data, thereby obtaining preliminary segmentation region data.

[0152] In this embodiment of the present invention, the seed point and growth direction for region growth are determined based on precise moving target outline data and segmentation strategy data. First, by analyzing the precise moving target outline data, the center point or other representative pixels within the target outline are selected as the seed point for region growth. Then, based on the region growing rules in the segmentation strategy data and combined with a boundary extension algorithm, the growing region is gradually expanded outward. During the region growing process, the growth direction and rate are dynamically adjusted based on similarity and boundary conditions to ensure that the grown region maintains the integrity of the target. Ultimately, preliminary segmented region data is obtained for subsequent processing.

[0153] Through high-frequency sampling, the present invention can accurately capture changes in the displayed content on the LCD screen, especially the subtle movements of moving targets. This sampling method allows for timely updates of image data, ensuring that subsequent analysis is based on the latest displayed content, thereby ensuring real-time and accurate moving target detection. The foreground extraction algorithm effectively separates the static background from the dynamic foreground, allowing for independent extraction of moving target features. Optical flow analysis and inter-frame differencing identify the actual moving portion of the foreground and extract feature information such as motion trajectory and direction, thereby generating accurate moving target data. This separation and feature extraction method improves the accuracy of moving target recognition and reduces background interference. By combining moving target data with the regional similarity assessment rules in the segmentation strategy, target regions with similar motion characteristics can be clustered together. This clustering process helps to preliminarily identify possible moving target regions, reduces the risk of misidentification, and provides more directional candidate regions for subsequent boundary refinement. Shape analysis and contour extraction algorithms further refine the initial moving target boundary. These algorithms can further refine the moving target boundary, eliminating blurred or irregular boundaries. This refinement process can improve the accuracy of the moving target's boundaries, making the moving target's outline closer to reality and suitable for subsequent region segmentation and growing processing. By accurately selecting seed points and determining the growth direction, region growing can be expanded in a targeted manner. The adaptive region growing rule can adaptively adjust the growth process according to the shape and characteristics of the moving target, ensuring that the segmented region can be reasonably extended to the entire moving target area, avoiding omissions or over-segmentation. This adaptive region growing method helps to accurately identify the complete boundaries and internal areas of the moving target. Through high-frequency sampling, foreground extraction, moving target clustering, boundary refinement processing, and region growing processing, these steps can accurately capture dynamic moving targets on the LCD screen and perform precise segmentation. In particular, the adaptive region growing processing ensures the complete segmentation of the moving target, making the final segmentation result more accurate and consistent. This series of steps has significant advantages in target detection and segmentation applications in dynamic scenes.

[0154] Preferably, step S5 includes the following steps:

[0155] Step S51: extracting the contour information of the segmented area from the preliminary segmented area data, and performing sub-pixel refinement processing according to the screen response characteristic data to generate high-precision edge contour data;

[0156] The embodiment of the present invention extracts contour information from the preliminary segmented area data, using traditional edge detection algorithms such as the Sobel or Canny algorithms to first perform edge detection on the segmented area. The detected edges are then used to extract complete contour information using a contour tracking algorithm (e.g., a chain code or Freeman chain code). Subsequently, based on the LCD screen's screen response characteristic data (e.g., grayscale response time, dynamic contrast), the contours are refined at the sub-pixel level. Interpolation techniques or edge refinement algorithms (e.g., sub-pixel edge detection) are used to improve the spatial resolution of the edges, thereby generating high-precision edge contour data and ensuring more accurate and clear contour details.

[0157] Step S52: constructing an edge smoothing model based on curvature and direction according to the high-precision edge contour data to perform local optimization on the edge to obtain smoothed edge data;

[0158] Based on high-precision edge contour data, the present invention constructs an edge smoothing model based on curvature and direction, and uses Gaussian smoothing or bilateral filtering to locally smooth the edges. The curvature is calculated based on the geometric properties of the contour, for example, by calculating the radius of curvature on the contour to ensure that edge features are preserved in areas with large curvature, while stronger smoothing is performed in areas with small curvature. This model flexibly adjusts the smoothing intensity based on the local curvature and directionality of the edge to reduce edge noise and jaggedness, ultimately generating smoothed edge data that appears more natural and smooth.

[0159] Step S53: performing a regional morphological operation to eliminate holes and protrusions based on the smoothed edge data and the preliminary segmented region data to generate morphologically optimized data;

[0160] Based on the smoothed edge data, this embodiment of the present invention uses morphological operations to further optimize the segmented regions. Specifically, morphological operations such as dilation and erosion are used to eliminate small holes and protrusions. Erosion is first used to remove small noise and burrs on the edges, and then dilation is used to restore the normal edge shape. Based on the results of the morphological operations, the morphological structure of the segmented regions is corrected to ensure the integrity and smoothness of the regional edges. Ultimately, morphologically optimized data is generated, eliminating irregular noise and unnecessary details in the regions.

[0161] Step S54: performing texture consistency analysis based on the morphologically optimized data and the real-time image data, and locally adjusting the segmentation boundary to obtain texture optimized segmentation data;

[0162] This embodiment of the present invention performs texture consistency analysis on morphologically optimized data. By analyzing the texture differences between the segmented region and its surrounding background, combined with texture features extracted from real-time image data, and using texture analysis algorithms such as Gabor filters or wavelet transforms, the boundaries of the segmented region are fine-tuned, particularly at boundaries with sudden texture changes, ensuring a smooth texture transition between the edge and the adjacent region. This process makes local adjustments based on texture consistency, optimizes the boundaries of texture transition areas, and ultimately generates texture-optimized segmentation data, resulting in more natural and accurate segmentation results.

[0163] Step S55: performing a segmentation quality assessment on the texture optimized segmentation data based on edge accuracy, regional consistency, and consistency with the moving target, and identifying areas where the segmentation quality is less than a preset value and marking them as areas to be optimized, thereby obtaining segmentation quality assessment data;

[0164] The embodiment of the present invention performs a segmentation quality assessment on the texture optimized segmentation data. First, the accuracy of the edge, the consistency of the region, and the degree of fit with the moving target data are calculated, and a scoring-based evaluation model (such as the Jaccard index, the Dice coefficient, etc.) is used for quantitative evaluation. For areas whose segmentation quality is lower than the preset standard identified in the evaluation, they are automatically marked as areas to be optimized. These areas may be inaccurately segmented due to complex textures, dynamic blur, or segmentation errors. Segmentation quality assessment data is generated through these evaluation data to provide a basis for subsequent optimization.

[0165] Step S56: Optimizing the segmentation results based on local threshold adjustment and region merging and splitting operations on the segmentation quality assessment data, thereby obtaining final segmentation result data.

[0166] Based on the segmentation quality assessment data, the present invention applies optimization operations based on local threshold adjustment to the regions to be optimized. First, the boundaries of these regions are recalculated and the segmentation boundaries are optimized by adjusting the local thresholds. Adjacent similar regions are merged into a unified segmented region using a region merging algorithm. Regions with significant internal differences are subdivided using a region splitting algorithm to ensure consistency within the region. These local optimization measures ultimately generate the final segmentation result data after segmentation optimization.

[0167] The present invention significantly improves the positioning accuracy of segmented edges through sub-pixel-level refinement. Subtle variations in the response of LCD screens, such as grayscale response and dynamic contrast, can affect the accuracy of edge contours. Sub-pixel processing can capture these subtle features and generate highly accurate edge contour data, thereby reducing edge blur and discontinuity. A smoothing model based on curvature and direction effectively addresses jagged edges or irregularities, resulting in smoother and more natural-looking segmented edges. This smoothing process, particularly when dealing with high dynamic contrast and motion blur, can reduce the impact of noise and irregular edges, enhancing the visual consistency of the segmentation results. Small holes and irregular protrusions in the segmented region are eliminated, enhancing the coherence and integrity of the region. These morphological operations help improve the geometric integrity of the segmented region, particularly in complex moving objects or multi-layer structures, effectively removing unnecessary interference and enhancing the stability of the target region. The segmentation boundary can be adjusted based on the texture characteristics of the target region, ensuring that the segmentation boundary is consistent with the actual image content. This step effectively optimizes the position of the segmentation boundary by analyzing the texture variations in the region, ensuring that the segmentation result more closely matches the natural boundary of the target region and avoiding unnecessary boundary errors. An automated segmentation quality assessment mechanism is provided. By comprehensively evaluating edge accuracy, regional consistency, and target fit, it identifies areas requiring further optimization. This not only improves overall segmentation accuracy but also mitigates the impact of local variations in segmentation quality, providing target regions for final optimization. Local threshold adjustment and region merging / splitting operations further optimize the shape and boundaries of the segmented regions, ensuring that the final segmentation results more closely match the actual target characteristics. Local threshold adjustment precisely controls boundary segmentation, while region merging and splitting operations help address irregularities within the segmented regions, ensuring a more reasonable structure and morphology. These steps, through refined edge extraction, optimization, and quality assessment, effectively improve the accuracy and consistency of segmented regions, ultimately generating high-quality segmentation results. In particular, the combination of sub-pixel processing, morphological operations, and texture analysis ensures precise and consistent segmentation boundaries for moving targets, making it suitable for demanding visual inspection tasks.

[0168] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0169] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. An image segmentation method for liquid crystal display, characterized in that: The following steps are involved: Step S1: acquiring basic data of the LCD screen; performing screen response characteristic analysis based on the basic data of the LCD screen to obtain screen response characteristic data; performing multi-scale image acquisition based on the basic data of the LCD screen to generate multi-scale image data; The characteristics of the LCD screen are classified according to the screen response characteristic data, and edge compensation analysis is performed on the multi-scale image data to obtain edge feature compensation data; Step S2: Acquire real-time data of the touch screen; perform touch interference recognition analysis on the real-time data of the touch screen according to the edge feature compensation data to generate touch interference mask data, wherein the touch interference includes pressure deformation interference, electric field interference, refresh rate interference, and timing interference; Step S3: performing edge feature correction processing on the edge feature compensation data according to the touch interference mask data, and performing adaptive threshold segmentation to obtain preliminary image segmentation data; Performing temporal consistency constraint processing on the preliminary image segmentation data according to the screen response characteristic data, thereby generating segmentation strategy data; Step S4: collecting real-time image data from the LCD screen, and performing moving target detection based on the real-time image data to obtain moving target data; Perform region growing processing based on segmentation strategy data and moving target data to obtain preliminary segmentation region data; Step S5: Perform edge refinement processing on the preliminary segmented area data, evaluate the segmentation quality, and optimize the segmentation results to obtain the final segmentation result data.

2. The image segmentation method for liquid crystal display according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: collecting information on the size, resolution, and pixel density of the LCD screen to obtain screen physical property data; Step S12: acquiring electrical parameters of the liquid crystal display based on the driving voltage and refresh rate, thereby obtaining screen electrical characteristic data; Step S13: merging the screen electrical characteristic data and the screen physical characteristic data into LCD basic data; Step S14: performing screen response characteristic analysis based on the basic data of the LCD screen to obtain screen response characteristic data; Step S15: designing a multi-scale sampling scheme based on the screen physical characteristic data, and using the sampling parameter data to perform multi-scale image acquisition on the LCD display content to generate multi-scale image data; Step S16: classifying the characteristics of the liquid crystal screen according to the screen response characteristic data, and performing dynamic edge compensation analysis on the multi-scale image data to obtain edge feature compensation data.

3. The image segmentation method for liquid crystal display according to claim 2, characterized in that: Step S14 includes the following steps: Step S141: performing a grayscale switching response time test according to basic data of the LCD screen, thereby obtaining grayscale response time data; Step S142: performing dynamic contrast analysis based on the grayscale response time data to generate dynamic contrast characteristic data; Step S143: testing and analyzing pixel overshoot and undershoot according to the screen electrical characteristic data to obtain pixel stability data; Step S144: constructing a motion blur characteristic model according to the grayscale response time data and the dynamic contrast characteristic data, thereby generating motion blur characteristic data; Step S145: performing inter-frame interpolation capability analysis based on the refresh rate information in the screen electrical characteristic data, thereby obtaining frame interpolation characteristic data; Step S146: performing data integration based on high-dimensional feature fusion on the pixel stability data, the frame interpolation characteristic data, the motion blur characteristic data, and the grayscale response time data, thereby obtaining screen response characteristic data.

4. The image segmentation method for liquid crystal display according to claim 3, characterized in that: Step S16 includes the following steps: Step S161: establishing a screen response characteristic classification model according to the grayscale response time data, dynamic contrast characteristic data, and pixel stability data in the screen response characteristic data; Step S162: Identifying the type of the LCD screen according to the screen response characteristic classification model, thereby generating screen type identification data, wherein the screen type identification data includes a high grayscale response time type, a high dynamic contrast type, an unstable pixel type, and a standard response type; Step S163: selecting an edge detection algorithm based on the screen type identification data, and extracting edge features from the multi-scale image data using the selected edge detection algorithm, thereby obtaining preliminary edge feature data. Specifically, the algorithm selected is: for a screen with a high grayscale response time, the Canny edge detection algorithm is selected; for a screen with a high dynamic contrast ratio, the Sobel algorithm is selected; for a screen with an unstable pixel type, the Laplacian algorithm is selected; and for a screen with a standard response type, the Prewitt algorithm is selected. Step S164: constructing a dynamic edge blur compensation model according to the screen response characteristic data, and performing edge compensation analysis on the preliminary edge feature data using the dynamic edge blur compensation model, thereby obtaining edge feature compensation data.

5. The image segmentation method for liquid crystal display according to claim 4, characterized in that: Step S2 includes the following steps: Step S21: collecting touch coordinates, pressure values, and touch area information of the touch screen in real time, thereby obtaining real-time data of the touch screen; Step S22: Identifying and extracting the pressure deformation interference area based on the real-time data of the touch screen, thereby generating pressure deformation interference data; Step S23: analyzing the electric field changes around the touch point based on the touch coordinate information in the real-time data of the touch screen, thereby obtaining electric field interference data; Step S24: Analyzing the timing relationship between touch operation and screen refresh using the refresh rate information in the basic data of the LCD screen, thereby generating refresh rate impact interference data; Step S25: analyzing the response delay of the touch operation based on the real-time data of the touch screen and the edge feature compensation data, thereby obtaining response delay interference data; Step S26: analyzing the afterimage effect caused by the touch operation on the touch screen real-time data and the screen response characteristic data, thereby generating afterimage effect interference data; Step S27: performing spatial superposition and temporal correlation analysis on the pressure deformation interference data, the electric field interference data, the refresh rate interference data, the response delay interference data, and the afterimage effect interference data, thereby obtaining comprehensive touch interference data; Step S28: establishing a touch interference space-time model based on the comprehensive touch interference data, and generating preliminary touch interference mask data; Step S29: performing edge alignment and refinement processing on the preliminary touch interference mask data using the edge feature compensation data, thereby obtaining touch interference mask data.

6. The image segmentation method for liquid crystal display according to claim 5, characterized in that: Step S3 includes the following steps: Step S31: performing mask processing on the edge feature compensation data according to the touch interference mask data, thereby obtaining preliminary edge feature correction data; Step S32: performing time domain compensation on the preliminary edge feature correction data using the grayscale response time data in the screen response characteristic data, thereby obtaining time-compensated edge feature data; Step S33: performing contrast enhancement processing on the time-compensated edge feature data and performing noise suppression processing according to the pixel stability data, thereby obtaining final edge feature correction data; Step S34: constructing an adaptive threshold model based on local image features and global brightness distribution according to the final edge feature correction data, and performing segmentation processing on the final edge feature correction data according to the adaptive threshold model, thereby obtaining preliminary image segmentation data; Step S35: performing temporal consistency constraint processing on the preliminary image segmentation data according to the screen response characteristic data, thereby generating segmentation strategy data.

7. The image segmentation method for liquid crystal display according to claim 6, characterized in that: Step S35 includes the following steps: Step S351: establishing a temporal consistency constraint model according to the dynamic contrast characteristic data and the motion blur characteristic data in the screen response characteristic data; Step S352: using a temporal consistency constraint model to perform consistency analysis on the preliminary image segmentation data across frames, thereby obtaining temporal corrected segmentation data; Step S353: performing refinement processing on the segmentation boundary according to the timing correction segmentation data and the screen response characteristic data, thereby obtaining optimized segmentation data; Step S354: constructing a segmentation region growing rule set based on the optimized segmentation data, wherein the segmentation region growing rule set includes region similarity evaluation and boundary extension strategy; Step S355: Optimizing the segmentation strategy of the optimized segmentation data according to the segmentation region growing rule set, thereby generating segmentation strategy data.

8. The image segmentation method for liquid crystal display according to claim 7, characterized in that: Step S4 includes the following steps: Step S41: using the image acquisition device of the LCD screen to perform high-frequency sampling on the current display content, thereby obtaining real-time image data; Step S42: using a foreground extraction algorithm to perform preliminary separation of the static background and the dynamic foreground on the real-time image data, and performing motion feature extraction based on optical flow analysis and inter-frame difference on the separated dynamic foreground to obtain moving target data; Step S43: clustering the moving targets according to the moving target data and the regional similarity evaluation rules in the segmentation strategy data, thereby obtaining preliminary moving target candidate regions; Step S44: performing a motion target boundary refinement process on the preliminary motion target candidate region based on shape analysis and contour extraction algorithm to generate accurate motion target contour data; Step S45: Determine the seed point and growth direction of region growth according to the precise moving target contour data and the segmentation strategy data, and perform adaptive region growing processing according to the region growing rule in the segmentation strategy data, thereby obtaining preliminary segmentation region data.

9. The image segmentation method for liquid crystal display according to claim 8, characterized in that: Step S5 includes the following steps: Step S51: extracting the contour information of the segmented area from the preliminary segmented area data, and performing sub-pixel refinement processing according to the screen response characteristic data to generate high-precision edge contour data; Step S52: constructing an edge smoothing model based on curvature and direction according to the high-precision edge contour data to perform local optimization on the edge to obtain smoothed edge data; Step S53: performing a regional morphological operation to eliminate holes and protrusions based on the smoothed edge data and the preliminary segmented region data to generate morphologically optimized data; Step S54: performing texture consistency analysis based on the morphologically optimized data and the real-time image data, and locally adjusting the segmentation boundary to obtain texture optimized segmentation data; Step S55: performing a segmentation quality assessment on the texture optimized segmentation data based on edge accuracy, regional consistency, and consistency with the moving target, and identifying areas where the segmentation quality is less than a preset value and marking them as areas to be optimized, thereby obtaining segmentation quality assessment data; Step S56: Optimizing the segmentation results based on local threshold adjustment and region merging and splitting operations on the segmentation quality assessment data, thereby obtaining final segmentation result data.

10. An image segmentation system for liquid crystal display, characterized in that: For executing the image segmentation method for liquid crystal display according to claim 1, the image segmentation system for liquid crystal display comprises: The screen characteristic analysis module is used to obtain basic data of the LCD screen; perform screen response characteristic analysis based on the basic data of the LCD screen to obtain screen response characteristic data; perform multi-scale image acquisition based on the basic data of the LCD screen to generate multi-scale image data; classify the characteristics of the LCD screen based on the screen response characteristic data, and perform edge compensation analysis on the multi-scale image data to obtain edge feature compensation data; A touch interference recognition module is used to obtain real-time data from the touch screen; perform touch interference recognition analysis on the real-time data of the touch screen based on the edge feature compensation data, and generate touch interference mask data, where touch interference includes pressure deformation interference, electric field interference, refresh rate interference, and timing interference; A preliminary segmentation module is used to perform edge feature correction processing on the edge feature compensation data based on the touch interference mask data, and perform adaptive threshold segmentation to obtain preliminary image segmentation data; and perform temporal consistency constraint processing on the preliminary image segmentation data based on the screen response characteristic data to generate segmentation strategy data; The moving target detection module is used to collect real-time image data of the LCD screen and perform moving target detection based on the real-time image data to obtain moving target data; perform region growing processing based on the segmentation strategy data and the moving target data to obtain preliminary segmentation region data; The edge refinement module is used to perform edge refinement processing on the preliminary segmentation area data, evaluate the segmentation quality, and optimize the segmentation results to obtain the final segmentation result data.

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