Method and system for adjusting color of baked porcelain false tooth based on image analysis

Abstract

The invention relates to the technical field of color correction, in particular to a baked porcelain false tooth color adjusting method and system based on image analysis, and the method comprises the following steps: identifying teeth, gingiva and surrounding tissues in an image, determining a region boundary by using an edge detection technology, and calibrating a region according to gray difference. According to the method, teeth and surrounding tissues are accurately segmented by using an edge detection technology, accurate identification of a tooth area in an image is realized, a clear starting point is provided for color adjustment, a wave band with high reflectivity is selected, the quality of image data is optimized, the precision of subsequent color analysis is ensured, and hue, brightness and saturation are adjusted in a CIELAB color space; the matching degree of denture colors and natural teeth is improved, the visual coherence is enhanced, neural network analysis and time sequence data are integrated to predict long-term color changes, a real-time feedback mechanism automatically adjusts color parameters according to illumination conditions, the visual effect is continuously optimized, and the naturalness and consistency of colors in different environments are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of color correction, and in particular to a method and system for adjusting the color of porcelain dentures based on image analysis. Background Art

[0002] Color correction technology mainly involves methods and systems for adjusting and optimizing colors in image processing to make the color representation of the image more realistic, more in line with specific visual effects or meet specific output requirements. This technical field is widely used in digital photography, film production, printing industry and any field that requires accurate color reproduction. Color correction can be dynamically adjusted according to light sources, environmental variables and device characteristics, including modification of color balance, adjustment of contrast and brightness, and enhancement or suppression of specific colors. These processes help improve the quality of visual presentation and ensure the consistency and accuracy of color output.

[0003] Among them, the porcelain denture color adjustment method refers to the use of image analysis technology to accurately adjust the color of porcelain dentures to ensure that the color of the denture matches the natural tone of the patient's other teeth. The main use of this technology is in the process of dental restoration, through advanced image capture and analysis software, to accurately control the color effect of porcelain materials during the production process, and ultimately achieve seamless connection with the patient's own tooth color. This not only improves the aesthetics, but also enhances the natural feel of the denture and overall oral health.

[0004] Existing color correction technologies fail to effectively handle color deviations caused by changes in lighting, especially in the color matching of dental dentures. This limitation leads to inconsistencies between the color of dentures and natural teeth under different lighting conditions, affecting aesthetics and patient satisfaction. In addition, these technologies lack the ability to predict and dynamically adjust the color changes of dentures during use, and cannot adapt to the color changes that may occur in natural teeth over time. This reduces the durability and naturalness of their effects in long-term applications, limiting their actual application effects in dental beauty. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a method and system for adjusting the color of porcelain dentures based on image analysis.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a method for adjusting the color of porcelain dentures based on image analysis, comprising the following steps: S1: Identify the teeth, gums and surrounding tissues in the image, use edge detection technology to determine the region boundary, calibrate the region according to the grayscale difference, divide the image into two parts, teeth and non-teeth, and obtain the segmented image result; S2: performing band screening on the segmented image result, extracting visible light, near infrared and ultraviolet band data of the tooth area image, analyzing the multi-band reflectance according to the spectral resolution, and selecting the band with high reflectance as the preferred band result; S3: In the CIELAB color space, adjusting the hue, brightness and saturation of the preferred band result, extracting the color features within the target wavelength range by analyzing the multi-color channel data one by one, adjusting the ratio between the color channels, and obtaining the color adjustment parameters; S4: Through the color adjustment parameters, the hybrid neural network analyzes the long-term color change trend, combines the time series data, compares the color difference optimization model before and after the adjustment, and generates a dynamic adjustment result; S5: Through a real-time feedback mechanism, the color parameters of the dynamic adjustment result are automatically adjusted according to changes in lighting and monitoring conditions. By comparing the target color and the actual output color difference, the color depth and saturation are adjusted, the actual performance is continuously monitored, and a color matching result is generated.

[0007] The segmented image results specifically include dental images and non-dental images, the preferred band results include visible light band, near-infrared band, and ultraviolet band, the color adjustment parameters specifically refer to hue adjustment parameters, brightness adjustment parameters, and saturation adjustment parameters, the dynamic adjustment results include long-term color change trends and time series data, and the color matching results include color depth and saturation.

[0008] As a further solution of the present invention, the step of obtaining the segmented image result is specifically as follows: S111: Identify teeth, gums and surrounding tissues, identify regional boundaries by applying edge detection technology, determine differentiated regions, and obtain boundary detection results; S112: using the boundary detection result, comparing the grayscale values ​​inside and outside the boundary, using a threshold segmentation method to determine the tooth and non-tooth areas, and comprehensively referring to the grayscale distribution characteristics to obtain a grayscale segmentation result; S113: Classify the tooth and non-tooth areas in the image according to the grayscale segmentation result, using the formula: Optimize the regional segmentation effect and obtain the segmented image result ; in, Represents the average grayscale value of the tooth area, focusing on the brightness and darkness of the tooth area. Represents the average gray value of the non-tooth area, used to balance the contrast between teeth and surrounding tissues. It is the display weight of the tooth area and adjusts the contrast between the tooth area and the non-tooth area in the image.

[0009] As a further solution of the present invention, the step of obtaining the optimal band result is specifically as follows: S211: Identify and extract visible light, near infrared and ultraviolet band data of the tooth area from the segmented image result, determine the spectral range of each band, and measure the spectral signal intensity of multiple bands to obtain band data results; S212: Based on the band data results, average reflectance of multiple bands is calculated, statistical data of reflectance of multiple bands are compared, bands with the highest average reflectance are selected, and reflectance comparison results are generated; S213: Using the reflectivity comparison result, adopt the formula: Select the band with the smallest standard deviation as the preferred band result ; in, Representative The reflectivity of each band directly affects the quality of the selected band. For the The weight of each band is inversely proportional to the stability of the band reflectivity.

[0010] As a further solution of the present invention, the step of obtaining the color adjustment parameters is specifically as follows: S311: Analyzing multiple color channels from the preferred band results through the CIELAB color space, calculating the hue, brightness and saturation data of the multiple channels, and identifying the basic color features of each channel by integrating the data to obtain color feature results; S312: Based on the color feature results, analyze the color data of each color channel within the target wavelength range, extract key color characteristics, evaluate the adjustment requirements of multiple channels, and generate color characteristic evaluation results; S313: Using the color characteristic evaluation result, the formula is: Calculate the new ratio between color channels, perform color balancing, and obtain color adjustment parameters ; in, Indicates The original parameters of the color channel refer to the initial color value of the channel. To determine the intensity and direction of adjustment based on the adjustment parameters obtained from color analysis, It is the sum of all tuning parameters and is used to normalize multi-channel parameters.

[0011] As a further solution of the present invention, the step of obtaining the dynamic adjustment result is specifically: S411: Starting from the color adjustment parameters, configuring a neural network model, inputting long-term color data, and setting a network to analyze color change trends, extracting preliminary color trend features from the data, and obtaining color trend feature results; S412: using the color trend feature results in combination with time series analysis technology, evaluating color changes, adjusting and optimizing parameters of the neural network model, and generating model adjustment results; S413: Based on the optimized model in the model adjustment result, the formula is used: Calculate and compare the color difference before and after adjustment to generate dynamic adjustment results; in, is the adjusted color difference, which indicates the amount of color change after model optimization. is the color difference calculated by the previous model, , The adjustment coefficient obtained through data analysis is used to regulate the sensitivity and adjustment range of color differences to match the differentiated change rate and range.

[0012] As a further solution of the present invention, the step of obtaining the color matching result is specifically as follows: S511: configuring a real-time feedback mechanism to monitor the state of the ambient lighting and the color display device, collecting data of the ambient lighting and monitoring conditions in real time, automatically adjusting the color parameters of the dynamic adjustment result according to the data, and generating an environmental monitoring data result; S512: using the environmental monitoring data results, comparing the difference between the target color and the actual output color, calculating the deviation of color depth and saturation, adjusting the color parameters to match the current monitoring conditions, and generating a color adjustment result; S513: Using the color adjustment result, using the formula: Adjust color matching to minimize color differences between target and actual output, generating color matching results ; in, Represents the target color value, which is the preset or expected color parameter. Indicates the actual displayed color value, which is affected by environmental factors and device performance. It is an adjustment coefficient, which is calculated based on the color differences monitored in real time and is used to dynamically adjust the color parameters.

[0013] A porcelain denture color adjustment system based on image analysis, the porcelain denture color adjustment system based on image analysis is used to execute the porcelain denture color adjustment method based on image analysis, the system comprises: The image segmentation module identifies teeth, gums and surrounding tissues based on the input porcelain denture image, divides the region boundaries using edge detection, completes region calibration based on grayscale differences, divides the image into tooth and non-tooth areas, and outputs the segmented image results; The band screening module screens the visible light, near infrared and ultraviolet band data of the tooth area based on the segmented image results, analyzes the multi-band reflectance, selects the band with high reflectance as the processing basis, and outputs the optimal band result; The color adjustment module adjusts the hue, brightness and saturation in the CIELAB color space based on the preferred band results, analyzes the multi-color channel data, extracts and adjusts the color channel ratio, and outputs the color adjustment parameters; The color matching monitoring module is based on the color adjustment parameters and analyzes the long-term color change trend through a hybrid neural network. It combines time series data to optimize the color difference model before and after adjustment, dynamically generates adjustment results, and automatically adjusts the color depth and saturation according to changes in lighting and monitoring conditions through a real-time feedback mechanism. It continuously monitors the actual performance and outputs the color matching results.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by using edge detection technology to accurately segment teeth and surrounding tissues, accurate identification of tooth areas in the image is achieved, providing a clear starting point for color adjustment. Selecting a band with high reflectivity optimizes the quality of image data and ensures the accuracy of subsequent color analysis. Adjusting hue, brightness, and saturation in the CIELAB color space improves the match between denture color and natural teeth and enhances visual continuity. Integrating neural network analysis with time series data to predict long-term color changes, the real-time feedback mechanism automatically adjusts color parameters according to lighting conditions, continuously optimizes visual effects, and ensures the naturalness and consistency of colors in different environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the workflow of the present invention; Figure 2 A flowchart of the steps for obtaining the image segmentation result of the present invention; Figure 3 The flowchart of the steps for obtaining the optimal band result of the present invention; Figure 4 A flow chart of the steps for obtaining color adjustment parameters of the present invention; Figure 5 A flowchart of the steps for obtaining the dynamic adjustment result of the present invention; Figure 6 The figure is a flow chart of the steps for obtaining the color matching results of the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0018] Embodiment 1 See also Figure 1 The present invention provides a technical solution: a method for adjusting the color of porcelain dentures based on image analysis, comprising the following steps: S1: Identify the teeth, gums and surrounding tissues in the image, use edge detection technology to determine the region boundary, calibrate the region according to the grayscale difference, divide the image into two parts, teeth and non-teeth, and obtain the segmented image result; S2: Perform band screening on the segmented image results, extract visible light, near infrared and ultraviolet band data of the tooth area image, analyze the multi-band reflectance according to the spectral resolution, and select the band with high reflectance as the preferred band result; S3: In the CIELAB color space, adjust the hue, brightness and saturation of the selected band results, extract the color features within the target wavelength range by analyzing the multi-color channel data one by one, adjust the ratio between the color channels, and obtain the color adjustment parameters; S4: Through color adjustment parameters, hybrid neural network analyzes long-term color change trends, combines time series data, compares the color difference optimization model before and after adjustment, and generates dynamic adjustment results; S5: Through the real-time feedback mechanism, the color parameters of the dynamic adjustment results are automatically adjusted according to the changes in lighting and monitoring conditions. By comparing the difference between the target color and the actual output color, the color depth and saturation are adjusted, and the actual performance is continuously monitored to generate color matching results.

[0019] The segmented image results specifically include dental images and non-dental images. The preferred band results include visible light band, near-infrared band, and ultraviolet band. The color adjustment parameters specifically refer to hue adjustment parameters, brightness adjustment parameters, and saturation adjustment parameters. The dynamic adjustment results include long-term color change trends and time series data. The color matching results include color depth and saturation.

[0020] See also Figure 2 , the specific steps for obtaining the segmentation image results are: S111: Identify teeth, gums and surrounding tissues, identify regional boundaries by applying edge detection technology, determine differentiated regions, and obtain boundary detection results; Edge detection technology identifies the boundaries of teeth, gums and surrounding tissues, and ensures the capture of boundaries by adjusting detection parameters to match differentiated image contrast. Image preprocessing steps, including noise removal and grayscale conversion, optimize image quality to improve edge detection accuracy. This process operates on images and processes the processed images through edge detection algorithms. The boundary detection results include the demarcation of teeth and non-tooth areas.

[0021] S112: using the boundary detection result, comparing the grayscale values ​​inside and outside the boundary, using the threshold segmentation method to determine the tooth and non-tooth areas, and comprehensively referring to the grayscale distribution characteristics to obtain the grayscale segmentation result; The acquisition of boundary detection results is based on the data generated from the grayscale segmentation results. The data is used for threshold segmentation method, and classification is carried out according to the grayscale value difference between the teeth and non-tooth areas. The segmentation threshold is determined by statistical analysis of the grayscale values, and the grayscale segmentation parameters are adjusted to match the differentiated types of tooth images. During the segmentation process, the image processing library is called to process the grayscale values ​​to effectively distinguish between teeth and surrounding tissues. The grayscale segmentation results determine the tooth area and the non-tooth area, laying the foundation for the next step of image processing.

[0022] S113: Classify the teeth and non-tooth areas in the image according to the grayscale segmentation results, using the formula: Optimize the regional segmentation effect and obtain the segmented image result ; in, Represents the average grayscale value of the tooth area, focusing on the brightness and darkness of the tooth area. Represents the average gray value of the non-tooth area, used to balance the contrast between teeth and surrounding tissues. It is the display weight of the tooth area and adjusts the contrast between the tooth area and the non-tooth area in the image.

[0023] formula: The benefits of the formula are as follows: by weighting the grayscale values ​​of the tooth area and the non-tooth area, the contrast between the areas is enhanced, the accuracy and visual effect of image segmentation are improved, and the details can be better displayed at the boundary between the tooth area and the non-tooth area, which is beneficial for the subsequent analysis and diagnosis of dental lesions.

[0024] Detailed explanation of the formula and the process of formula calculation and derivation: Set the average gray value of the tooth area The average gray value of the non-tooth area is 180. is 80, the weight parameter Set to 0.5, the calculation process is as follows: 1. Calculate the weighted square of the tooth area: 2. Calculate the weighted square root of the non-tooth area: 3. Apply the formula to calculate the gray value: The results show that through weight processing, the boundary between the tooth area and the non-tooth area is more obvious, and the image segmentation results are The value is 5403, which means that the visualization of the dental area in the image is enhanced, which helps in diagnosing and evaluating the health of the teeth.

[0025] See also Figure 3 , the specific steps for obtaining the optimal band results are: S211: identifying and extracting visible light, near infrared and ultraviolet band data of the tooth area from the segmented image result, determining the spectral range of each band, and measuring the spectral signal intensity of multiple bands to obtain band data results; Visible light, near-infrared and ultraviolet bands are screened in the segmented image results, and the range of each band is determined by wavelength recognition technology to measure the spectral signal intensity of multiple bands. This step is based on the technical parameters of the spectral analysis instrument. By adjusting the equipment, optimizing the scanning speed and resolution, the accuracy and repeatability of the data are ensured. Data collection strictly depends on the band recognition algorithm and spectral analysis to obtain the band data results.

[0026] S212: Based on the band data results, the average reflectivity of the multiple bands is calculated, the statistical data of the multi-band reflectivity is compared, the band with the highest average reflectivity is selected, and the reflectivity comparison result is generated; Using the obtained band data, the average reflectivity of multiple bands is calculated for comparison, and the band with the highest reflectivity is selected as the target band. In this process, multiple scan data are statistically processed to ensure the accuracy of the results, and the reflectivity statistics of multiple bands are compared. The band with high reflectivity is selected as the research object to provide data support for spectral analysis and material research.

[0027] S213: Using the reflectivity comparison results, the formula is: Select the band with the smallest standard deviation as the preferred band result ; in, Representative The reflectivity of each band directly affects the quality of the selected band. For the The weight of each band is inversely proportional to the stability of the band reflectivity.

[0028] formula: The benefit of the formula is that by taking the weighted average of the reflectance of each band, the most representative band can be selected. This method is particularly suitable for spectral selection in materials science and helps to enhance the accuracy and efficiency of material analysis.

[0029] Detailed explanation of the formula and the process of formula calculation and derivation: Set reflectivity with three bands , and their standard deviations are , then the weight The calculation formula is: The results show that the reflectivity of the selected optimal band is 0.302, which means that the band has the best average reflection characteristics and is suitable for scientific research and practical applications. This value can be directly used to guide the calibration of the spectrometer and the collection of spectral data.

[0030] See also Figure 4 , the steps for obtaining the color adjustment parameters are as follows: S311: Analyze multiple color channels through CIELAB color space from the optimal band results, calculate the hue, brightness and saturation data of the multiple channels, identify the basic color characteristics of each channel through comprehensive data, and obtain color characteristic results; When working in the CIELAB color space, the preferred band results are imported through color analysis, the hue, brightness and saturation at differentiated wavelengths are measured and calculated, and the initial characteristics of multiple color channels are identified based on the measured data. This involves batch numerical operations, including wavelength identification, color spectral analysis and data normalization. The data is processed through an algorithm that first calculates the average hue and saturation of the color channel based on the spectral data, and then adjusts the color balance based on color theory to ensure that the output color results can truly reflect the true color characteristics of the measured object. The obtained color characteristic results are the basis for color adjustment and simulation.

[0031] S312: Based on the color feature results, analyze the color data of each color channel within the target wavelength range, extract key color characteristics, evaluate the adjustment requirements of multiple channels, and generate color characteristic evaluation results; Based on the color feature results, the color characteristics within the target wavelength range are iteratively analyzed, and the adjustment requirements of each channel are determined through comparative analysis between color channels. This analysis process includes the deconstruction and feature extraction of the color data of each channel, and the use of statistical and image processing techniques to analyze the data, including the use of principal component analysis and clustering algorithms to identify and separate differentiated color features. These algorithms can effectively extract data from color information for color adjustment, and the obtained color characteristic evaluation results will directly affect the adjustment strategy.

[0032] S313: Use the color characteristic evaluation results, using the formula: Calculate the new ratio between color channels, perform color balancing, and obtain color adjustment parameters ; in, Indicates The original parameters of the color channel refer to the initial color value of the channel. To determine the intensity and direction of adjustment based on the adjustment parameters obtained from color analysis, It is the sum of all tuning parameters and is used to normalize multi-channel parameters.

[0033] formula: The formula is beneficial in that it increases flexibility and sensitivity to the impact of multiple color channels on the output during the adjustment process through an exponential weighting approach, allowing the color balance to be adjusted to more closely match the true perception of color by the human eye.

[0034] Detailed explanation of the formula and the process of formula calculation and derivation: set up is the initial parameter of the i-th color channel, taken from the color characteristic evaluation result, is the adjustment coefficient of the channel obtained based on color data analysis, is the sum of all adjustment coefficients. There are three channels, and the initial parameters , adjustment coefficient ,calculate: Compute new weights for each channel: The adjusted color parameters are: The results show that the adjusted color configuration is more balanced, referring to the human eye's perception sensitivity to differentiated color channels, effectively improving the visual effect of the image.

[0035] See also Figure 5 , the specific steps for obtaining dynamic adjustment results are: S411: Starting from the color adjustment parameters, configuring the neural network model, inputting the long-term color data, and setting the network to analyze the color change trend, extracting preliminary color trend features from the data, and obtaining the color trend feature results; In the process of configuring the neural network model, the focus is on the setting of the input layer, which requires the data obtained from the color adjustment parameters as input. The data is obtained through long-term color monitoring, including RGB values ​​and CIELAB values. The collection of data involves color sensors and time series analysis methods. By continuously monitoring the color changes in differentiated time periods, the collected data will be pre-processed, such as denoising and data normalization, to match the data input requirements of the neural network, ensure the quality and accuracy of the data, and provide a reliable foundation for subsequent trend analysis. The training of the neural network model involves multi-layer perceptrons. The layers adjust the weights through forward and back propagation algorithms to predict the color change trend in the future time period.

[0036] S412: using the color trend feature results and combining time series analysis technology to evaluate color changes, adjust and optimize the parameters of the neural network model, and generate model adjustment results; Based on the color trend feature results, time series analysis technology is used to refine color changes, involving statistical methods, including autoregressive models and moving average models. The model can identify and predict seasonal and trend components in color changes. Through long-term collected color data, including daily RGB color values, the color changes at differentiated time points are calculated, and then the parameters of the model are optimized, including adjusting the number of hidden layer neurons and the learning rate. The adjustment of these parameters is optimized based on the results of cross-validation to reduce overfitting and improve prediction accuracy. The generated color change model will be used for color adjustment and application, and can dynamically adjust the color output according to actual changes.

[0037] S413: Based on the optimized model in the model adjustment results, the formula is used: Calculate and compare the color difference before and after adjustment to generate dynamic adjustment results; in, is the adjusted color difference, which indicates the amount of color change after model optimization. is the color difference calculated by the previous model, , The adjustment coefficient obtained through data analysis is used to regulate the sensitivity and adjustment range of color differences to match the differentiated change rate and range.

[0038] formula: The benefit of the formula is that, by logarithmically processing the color difference before and after, it smooths the excessive changes in color adjustment, reduces mutations in the adjustment process, and makes the color changes more delicate and natural. By dynamically adjusting to reflect more accurate color changes, the model can make more reasonable predictions based on actual monitoring data.

[0039] Detailed explanation of the formula and the process of formula calculation and derivation: Set up at a target monitoring point, (color change at the previous time point), (Adjustment factor), (smoothing parameters): The results show that after referring to the color change of the previous state, the color difference that should be adjusted in the current state is about 1.8792. This value will be used in the actual color adjustment to provide a smooth transition color change output that matches the user's visual perception.

[0040] See also Figure 6 , the steps to obtain the color matching results are as follows: S511: configure a real-time feedback mechanism to monitor the status of the ambient lighting and color display device, collect data of the ambient lighting and monitoring conditions in real time, automatically adjust the color parameters of the dynamic adjustment results according to the data, and generate environmental monitoring data results; Monitor the status of ambient lighting and color display devices, collect lighting data and device performance data, feed back real-time information to the central processing unit through environmental sensors and interfaces, adjust the output parameters of the display device to match environmental conditions according to the processing unit's algorithm, update the color display parameters in real time to match environmental changes, ensure the matching of display colors with ambient lighting, enhance the continuity and comfort of the user's visual experience, and generate environmental monitoring data results.

[0041] S512: using the environmental monitoring data results, comparing the difference between the target color and the actual output color, calculating the deviation of the color depth and saturation, adjusting the color parameters to match the current monitoring conditions, and generating a color adjustment result; Utilize environmental monitoring data to analyze the difference between the target color and the actual output color, calculate the deviation of color depth and saturation through algorithms, adjust color parameters according to color theory, dynamically adjust colors, optimize color display effects to match current monitoring conditions, provide real-time feedback on adjustment effects, refine color adjustment strategies in a data-driven manner, ensure a natural transition of display effects, and generate color adjustment results.

[0042] S513: Use the color adjustment result and the formula: Adjust color matching to minimize color differences between target and actual output, generating color matching results ; in, Represents the target color value, which is the preset or expected color parameter. Indicates the actual displayed color value, which is affected by environmental factors and device performance. It is an adjustment coefficient, which is calculated based on the color differences monitored in real time and is used to dynamically adjust the color parameters.

[0043] formula: The benefit of the formula is that, through real-time monitoring and feedback mechanisms, the color output of the display device can be continuously adjusted to achieve the color display effect that is closest to the user's expectations.

[0044] Detailed explanation of the formula and the process of formula calculation and derivation: Set target color 255, 255, 255 (white), the actual output color 250, 250, 245. When the actual output is yellowish due to changes in lighting, set the adjustment coefficient is 0.8, and the calculation process is as follows: Since the color value cannot exceed 255, it is adjusted to 255. Therefore, the white output remains unchanged, verifying the effectiveness and practicality of the adjustment algorithm. The results show that the method of dynamically adjusting color depth and saturation can match the expected and actual colors, optimize the visual output quality, and ensure the accuracy and matching of color display.

[0045] A porcelain denture color adjustment system based on image analysis, the porcelain denture color adjustment system based on image analysis is used to execute the porcelain denture color adjustment method based on image analysis, the system comprises: The image segmentation module identifies teeth, gums and surrounding tissues based on the input porcelain denture image, divides the region boundaries using edge detection, completes region calibration based on grayscale differences, divides the image into tooth and non-tooth areas, and outputs the segmented image results; The band screening module screens the visible light, near infrared and ultraviolet band data of the tooth area based on the segmented image results, analyzes the multi-band reflectance, selects the band with high reflectance as the processing basis, and outputs the optimal band results; The color adjustment module adjusts the hue, brightness and saturation in the CIELAB color space based on the results of the optimal bands, analyzes the multi-color channel data, extracts and adjusts the color channel ratio, and outputs the color adjustment parameters; The color matching monitoring module is based on color adjustment parameters. It analyzes long-term color change trends through a hybrid neural network, combines time series data, optimizes the color difference model before and after adjustment, dynamically generates adjustment results, and automatically adjusts color depth and saturation according to changes in lighting and monitoring conditions through a real-time feedback mechanism. It continuously monitors actual performance and outputs color matching results.

[0046] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for adjusting the color of porcelain dentures based on image analysis, characterized in that: The following steps are involved: Identify the teeth, gums and surrounding tissues in the image, use edge detection technology to determine the region boundary, calibrate the region according to the grayscale difference, divide the image into two parts, teeth and non-teeth, and obtain the segmented image result; Performing band screening on the segmented image results, extracting visible light, near infrared and ultraviolet band data of the tooth area image, analyzing the multi-band reflectance according to the spectral resolution, and selecting the band with high reflectance as the preferred band result; In the CIELAB color space, the hue, brightness and saturation of the preferred band result are adjusted, the color characteristics within the target wavelength range are extracted by analyzing the multi-color channel data one by one, and the ratio between the color channels is adjusted to obtain the color adjustment parameters; Through the color adjustment parameters, the hybrid neural network analyzes the long-term color change trend, combines the time series data, compares the color difference optimization model before and after the adjustment, and generates a dynamic adjustment result; Through the real-time feedback mechanism, the color parameters of the dynamic adjustment result are automatically adjusted according to the changes in lighting and monitoring conditions. By comparing the target color and the actual output color difference, the color depth and saturation are adjusted, the actual performance is continuously monitored, and the color matching result is generated.

2. The method for adjusting the color of porcelain dentures based on image analysis according to claim 1, characterized in that: The segmented image results specifically include dental images and non-dental images, the preferred band results include visible light band, near-infrared band, and ultraviolet band, the color adjustment parameters specifically refer to hue adjustment parameters, brightness adjustment parameters, and saturation adjustment parameters, the dynamic adjustment results include long-term color change trends and time series data, and the color matching results include color depth and saturation.

3. The method for adjusting the color of porcelain dentures based on image analysis according to claim 2, characterized in that: The steps for obtaining the segmented image result are specifically as follows: Identify teeth, gums and surrounding tissues, identify regional boundaries by applying edge detection technology, determine differentiated areas, and obtain boundary detection results; Using the boundary detection result, the grayscale values ​​inside and outside the boundary are compared, the tooth and non-tooth areas are determined by a threshold segmentation method, and the grayscale distribution characteristics are comprehensively referred to to obtain a grayscale segmentation result; According to the grayscale segmentation results, the teeth and non-tooth areas in the image are classified using the formula: Optimize the regional segmentation effect and obtain the segmented image result ; in, Represents the average grayscale value of the tooth area, focusing on the brightness and darkness of the tooth area. Represents the average gray value of the non-tooth area, used to balance the contrast between teeth and surrounding tissues. It is the display weight of the tooth area and adjusts the contrast between the tooth area and the non-tooth area in the image.

4. The method for adjusting the color of porcelain dentures based on image analysis according to claim 3, characterized in that: The steps for obtaining the optimal band result are specifically as follows: Identify and extract visible light, near infrared and ultraviolet band data of the tooth area from the segmented image results, determine the spectral range of each band, and measure the spectral signal intensity of multiple bands to obtain band data results; Based on the band data results, the average reflectivity of the multiple bands is calculated, the statistical data of the multiple band reflectivity are compared, the band with the highest average reflectivity is selected, and the reflectivity comparison result is generated; Using the reflectivity comparison results, the formula is adopted: Select the band with the smallest standard deviation as the preferred band result ; in, Representative The reflectivity of each band directly affects the quality of the selected band. For the The weight of each band is inversely proportional to the stability of the band reflectivity.

5. The method for adjusting the color of porcelain dentures based on image analysis according to claim 4, characterized in that: The steps of obtaining the color adjustment parameters are specifically as follows: From the preferred band results, multiple color channels are analyzed by CIELAB color space, hue, brightness and saturation data of the multiple channels are calculated, and the basic color characteristics of each channel are identified by comprehensive data to obtain color characteristic results; Based on the color characteristic results, analyzing the color data of each color channel within the target wavelength range, extracting key color characteristics, evaluating the adjustment requirements of multiple channels, and generating color characteristic evaluation results; Using the color characteristic evaluation results, the formula is used: Calculate the new ratio between color channels, perform color balancing, and obtain color adjustment parameters ; in, Indicates The original parameters of the color channel refer to the initial color value of the channel. To determine the intensity and direction of adjustment based on the adjustment parameters obtained from color analysis, It is the sum of all tuning parameters and is used to normalize multi-channel parameters.

6. The method for adjusting the color of porcelain dentures based on image analysis according to claim 5, characterized in that: The steps for obtaining the dynamic adjustment result are specifically as follows: Starting from the color adjustment parameters, a neural network model is configured, long-term color data is input, and a network is set to analyze color change trends, and preliminary color trend features are extracted from the data to obtain color trend feature results; Using the color trend feature results, combined with time series analysis technology, to evaluate color changes, adjust and optimize the parameters of the neural network model, and generate model adjustment results; Based on the optimized model in the model adjustment results, the formula is adopted: Calculate and compare the color difference before and after adjustment to generate dynamic adjustment results; in, is the adjusted color difference, which indicates the amount of color change after model optimization. is the color difference calculated by the previous model, , The adjustment coefficient obtained through data analysis is used to regulate the sensitivity and adjustment range of color differences to match the differentiated change rate and range.

7. The method for adjusting the color of porcelain dentures based on image analysis according to claim 6, characterized in that: The steps for obtaining the color matching result are specifically as follows: Configure a real-time feedback mechanism to monitor the state of the ambient lighting and color display device, collect data of the ambient lighting and monitoring conditions in real time, automatically adjust the color parameters of the dynamic adjustment result according to the data, and generate environmental monitoring data results; Using the environmental monitoring data results, comparing the difference between the target color and the actual output color, calculating the deviation of color depth and saturation, adjusting the color parameters to match the current monitoring conditions, and generating a color adjustment result; Using the color adjustment result, the formula is used: Adjust color matching to minimize color differences between target and actual output, generating color matching results ; in, Represents the target color value, which is the preset or expected color parameter. Indicates the actual displayed color value, which is affected by environmental factors and device performance. It is an adjustment coefficient, which is calculated based on the color difference monitored in real time and is used to dynamically adjust the color parameters.

8. The porcelain denture color adjustment system based on image analysis is characterized by: According to the method for adjusting the color of porcelain dentures based on image analysis according to any one of claims 1 to 7, the system comprises: The image segmentation module identifies teeth, gums and surrounding tissues based on the input porcelain denture image, divides the region boundaries using edge detection, completes region calibration based on grayscale differences, divides the image into tooth and non-tooth areas, and outputs the segmented image results; The band screening module screens the visible light, near infrared and ultraviolet band data of the tooth area based on the segmented image results, analyzes the multi-band reflectance, selects the band with high reflectance as the processing basis, and outputs the optimal band result; The color adjustment module adjusts the hue, brightness and saturation in the CIELAB color space based on the preferred band results, analyzes the multi-color channel data, extracts and adjusts the color channel ratio, and outputs the color adjustment parameters; The color matching monitoring module is based on the color adjustment parameters and analyzes the long-term color change trend through a hybrid neural network. It combines time series data to optimize the color difference model before and after adjustment, dynamically generates adjustment results, and automatically adjusts the color depth and saturation according to changes in lighting and monitoring conditions through a real-time feedback mechanism. It continuously monitors the actual performance and outputs the color matching results.

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