55 results about "Pixel value differencing" patented technology

The embodiment of the invention provides an offset scanning offset determination method and device, equipment and a storage medium, and belongs to the technical field of offset scanning. Electronic equipment can obtain projection data obtained by performing offset scanning on a target object; based on the pixel value difference of the projection data pair in each column in the projection data, determining the predicted position of the rotation center shaft mapped in the detector; determining the offset range of the detector according to the pixel value difference of the projection data pair in the preset range; according to the pixel value fluctuation condition of the reconstructed image, the offset of offset scanning is determined, the preset range is a preset number of column data including the predicted position in the projection data, and the reconstructed image is obtained by reconstructing the projection data based on the offset. Due to the fact that the projection data are directly analyzed and processed, the accurate offset amount of offset scanning is determined, a correction die body does not need to be used, the process of determining the offset amount of offset scanning can be simplified, and the efficiency of determining the offset amount of offset scanning can be improved.

The invention relates to the technical field of image matching retrieval, and discloses a multi-scale similar image matching retrieval method, which comprises the following steps of: preprocessing an image to be retrieved; performing multi-scale feature extraction on the preprocessed to-be-retrieved image to obtain a multi-scale feature vector of the to-be-retrieved image; performing similarity measurement on the multi-scale feature vector of the to-be-retrieved image and the multi-scale feature vector of the matched image; and taking the matched image with the highest similarity as a retrieval result of the to-be-retrieved image. According to the method, the pixel value which is larger in information amount and smaller in difference with other pixel values is selected as the center pixel value, spatial filtering and pixel filtering processing is carried out on the to-be-retrieved image, edge information of the image is reserved, random noise is removed, the accuracy of image matching retrieval is improved, and the retrieval efficiency is improved. And extracting feature information of the preprocessed image under a light dark scale and a plurality of direction scales, forming a multi-scale feature vector, and carrying out matching retrieval to realize image search matching processing under multi-scale features.

The invention relates to the technical field of image data processing, in particular to a digital printing quality visual detection method and system for high-weather-resistance printed cloth, and the method comprises the steps: obtaining a surface image and a template image of the high-weather-resistance printed cloth, processing the surface image through an image algorithm, obtaining the color features of different channels, and carrying out the visual detection of the digital printing quality of the high-weather-resistance printed cloth. According to the color features of different channels, the surface image and the template image, determining the total difference value of each pixel value of pixel points at the same position of the surface image and the template image, and according to a preset local window and the total difference value of the pixel values, determining the possibility that each pixel point has color bleeding in a color layer in the preset local window, according to the method, the defect that the surface image is distorted and cannot highlight due to the influence of conditions such as light can be avoided, the potential bleeding area is determined according to the possibility so as to judge the digital printing quality of the high-weather-resistance printed cloth, and the accuracy of the potential bleeding area and the accuracy of quality detection are improved.

The invention provides a splash screen testing method based on machine vision. The splash screen testing method comprises the following steps: reading an image and cutting out a region of interest; carrying out preprocessing on the clipped image; calculating the difference between two adjacent frames of images to generate a difference chart; performing binarization processing on the difference image according to a threshold value, and extracting a change region; counting the number of pixels of which the pixel values are greater than a set threshold value in the change area, calculating the ratio of the number of the pixels to the total number of the pixels of the image, if the ratio is greater than the set threshold value, judging that a splash screen phenomenon occurs on the screen, and recording bounding box information of a splash screen area. According to the invention, the image information of the continuous frames of the screen is obtained through the camera, the pixel value difference of the two frames of images is automatically calculated, and whether the splash screen phenomenon occurs on the screen is judged according to whether significant brightness difference exists between the two adjacent frames of images; compared with a traditional manual identification method, the method can remarkably improve the efficiency and accuracy of the splash screen test, and provides a reliable basis for screen quality evaluation.

Generating a vector representation of a hand-drawn sketch is described. To do so, the sketch is segmented into different superpixel regions. Superpixels are grown by distributing superpixel seeds throughout an image of the sketch and assigning unassigned pixels to a neighboring superpixel based on pixel value differences. The border between each pair of adjacent superpixels is then classified as either an active or an inactive boundary, with active boundaries indicating that the border corresponds to a salient sketch stroke. Vector paths are generated by traversing edges between pixel vertices along the active boundaries. To minimize vector paths included in the vector representation, vector paths are greedily generated first for longer curves along active boundaries until each edge is assigned to a vector path. Regions encompassed by vector paths corresponding to a foreground superpixel are filled to produce a high-fidelity vector representation of the sketch.

The invention relates to the technical field of data anomaly detection, in particular to a monitoring video data anomaly behavior detection method based on deep learning. The method comprises the following steps: acquiring a figure video image of any figure in real time, detecting the figure video image, marking the figure video image as a target image when the front position information of the figure cannot be detected, and acquiring continuous multi-frame target images to obtain a target image set; according to the head position information and the hand position information of any person in each frame of target image in the target image set, acquiring a motion coefficient, and identifying whether any person has a suspected abnormal behavior; and if any person has the suspected abnormal behavior, determining an abnormal behavior detection result of any person according to the smoke concentration data in the target place and the pixel value difference of the pixel points in each frame of target image in the target image set. According to the invention, the abnormal behaviors of the employees in the target place can be accurately identified.

The invention relates to an image display method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring an image sequence comprising multiple frames of images; wherein the image frames in the image sequence comprise image frames to be displayed; determining a target pixel area based on pixel information of each image frame in the image sequence; wherein the pixel points in the target pixel area are points with pixel value differences within a preset difference range in different image frames of the image sequence; and adjusting the display in the target pixel area in the image frame to be displayed, and displaying the adjusted image. Through the method, on one hand, the burn-in prevention accuracy and effect of the display screen are improved; and on the other hand, the condition of poor user experience caused by low screen brightness is reduced.

The invention discloses a full-process visual monitoring and intelligent management method for a glass bracket production line, and relates to the technical field of industrial quality control, and the method comprises the steps: carrying out the continuous image collection of all stations of the glass bracket production line through an image collection unit, and constructing a full-process image data sequence; detecting the size of the glass bracket by using the qualified glass bracket size image database, and judging whether the size is abnormal or not; further performing defect detection on the glass bracket with the normal size, and determining whether an abnormal defect exists or not through pixel value difference analysis; and finally, summarizing the glass brackets with abnormal sizes or defects and related information thereof, and generating a visual quality report, so as to quickly position problems and optimize the production process. The comprehensive monitoring and intelligent management of the glass bracket production process are realized, and the production efficiency and the product quality are improved.

The present invention relates to a method for processing compressed image noise and a circuit system thereof. In the method, the circuit system receives an image compressed in a compression format, decompresses the image, obtains the pixel value of each pixel, calculates the difference in pixel values ​​between adjacent pixels, then divides a certain range of the image into multiple regions, obtains the maximum and minimum pixel values ​​in each region, and compares the maximum difference values ​​of each region to obtain the maximum difference value within the range. Based on this maximum difference value, it is determined whether the corresponding range, such as a flat area or a high-frequency area in the image, should be subjected to noise removal. The difference can also be adjusted using the weighted average brightness of pixels near the current pixel, and a noise removal process is performed only on this range, ultimately outputting an image with reduced noise generated during compression.

The present application discloses a video encoding method, apparatus, device and product, and relates to the field of video encoding and decoding technology. The method comprises: obtaining gradient information and brightness information of a unit to be encoded; when the gradient information meets a first condition, or the brightness information meets a second condition, canceling the intra-frame search operation for the unit to be encoded; when the intra-frame search operation is canceled, encoding the unit to be encoded to obtain the encoding information of the unit to be encoded. In the technical solution provided by the embodiment of the present application, by judging whether the gradient information of the unit to be encoded meets the first condition, and judging whether the corresponding pixel value difference information between at least two sub-coding units in the unit to be encoded meets the second condition, it is determined whether to skip the intra-frame search operation corresponding to the intra-frame block copy coding mode, thereby avoiding complex search operations on coding units with simple texture information, thereby improving video encoding speed and encoding efficiency.

The invention discloses a video frame rendering method, system and device, a storage medium and a program product, and belongs to the technical field of video processing. And a decoder in the CPU transmits the reference frame data to a reference frame cache region of the GPU. And a graph renderer in the GPU renders the reference frame according to the reference frame data. And a decoder in the CPU generates residual data according to the pixel value difference between the target frame and the reference frame, and transmits the residual data to a residual cache region of the GPU. And a graphic renderer in the GPU renders a local image area of the target frame according to the residual data, and superposes the local image area of the target frame with the reference frame to obtain a rendered image of the target frame. The technical effects of reducing GPU computing resources and memory bandwidth consumption, reducing data transmission delay and improving rendering efficiency and resource utilization rate in a high-resolution and high-frame-rate scene are achieved.

The invention relates to the field of image processing, in particular to an intelligent scanning method and system based on an oral digital impression instrument. The method comprises the following steps: acquiring a current oral cavity image at each angle and a historical oral cavity image at each angle; carrying out matching processing on the current oral cavity images at different angles, and obtaining suspicious pixels according to pixel value differences between matched pixels; acquiring a plurality of texture regions in the historical oral cavity image and the current oral cavity image, and fitting an area change rule and an interval change rule of the continuous texture regions; calculating the probability that any suspicious pixel is an interference pixel according to an area change rule and an interval change rule; interference pixels in the current oral cavity image are removed in a mode of comparing the probability with a preset probability threshold value, so that three-dimensional reconstruction of the oral cavity is achieved. The precision of oral cavity reconstruction is improved by eliminating interference pixels.

The present application relates to the technical field of multi-image analysis, in particular to a highway pavement quality detection method and system based on machine vision. According to the present application, the brightness crack probability is obtained according to the gray scale difference of the same suspected crack area in the pavement gray scale images before and after watering, and the temperature crack probability is obtained according to the pixel value difference of the same suspected crack area in the pavement infrared images before and after watering and the environmental data; according to the significant degree of the pixel value difference of the same suspected crack area in the pavement feature images before and after watering, the brightness crack probability and the temperature crack probability are adjusted to obtain the final crack probability of the suspected crack area, and the pavement quality of the to-be-tested highway is detected by using the same. The present application combines the brightness change characteristics and temperature change characteristics of the crack before and after watering, reduces the dependence on light, and improves the accuracy of pavement crack detection.

The invention relates to the technical field of image processing, in particular to a line etching method and device for a thermal printing sheet, and the method comprises the steps: obtaining each structural region in an exposure image of the thermal printing sheet, carrying out the equal division of all pixel values in each structural region, and analyzing the pixel order change degree and the pixel value difference in the region, acquiring the pixel disorder degree of each structural area; the method comprises the following steps of: obtaining a plurality of structural areas, obtaining the angular point distribution difference degree of each structural area by utilizing the angular point characteristics in each structural area, taking the corresponding structural areas with the same angular point distribution difference degree as the same structural area of each structure, and further obtaining the search step length of the same structural area of each structure so as to carry out line etching on the thermal printing sheet. The circuit etching efficiency of the thermal printing sheet can be improved.

The invention discloses an intelligent cloth printing defect monitoring method and system based on machine vision, and relates to the field of image data processing, and the method comprises the steps: collecting a real-time video stream on the surface of a fabric, intercepting single-frame image data, and carrying out the grid segmentation, so as to obtain candidate image blocks; performing feature clustering analysis on sample blocks in the candidate image blocks, and constructing a standard texture feature library; sparse coding operation is carried out on the candidate image blocks based on the standard texture feature library, linear combination weights are solved, and sparse coefficients are obtained; generating a reconstructed image block according to the standard texture feature library and the sparse coefficient; calculating a pixel value difference between the candidate image block and the reconstructed image block to obtain a residual matrix representing a difference degree; and when the energy norm value of the residual matrix is greater than an anomaly judgment threshold value, outputting a marking signal indicating that the corresponding candidate image block has an image defect. By implementing the method and the device, defect detection without a preset template can be realized, and the adaptability of printing defect monitoring is improved.

The application discloses a pattern defect detection method and device and a storage medium. The method comprises the following steps: obtaining an overall pattern region and a plurality of local pattern regions corresponding to a target pattern in a to-be-detected image, and obtaining an overall template image and a plurality of local template images from template information corresponding to the target pattern; aligning the overall pattern region with the overall template image, and respectively aligning each local pattern region with a corresponding local template image; determining a first defect position in the overall pattern region based on a first pixel value difference between the aligned overall pattern region and the overall template image, and respectively determining a second defect position in each local pattern region based on a second pixel value difference between the aligned each local pattern region and the corresponding local template image; and comprehensively determining a third defect position of the target pattern in the to-be-detected image based on the first defect position and the second defect positions. Through the above method, the accuracy of pattern defect detection can be improved.

The application discloses a lossless compression method and system for image sensor raw data, and relates to the technical field of image sensors.The application fully utilizes the statistical characteristics that the pixel value difference between adjacent frames under the same exposure is extremely small, combines the inherent color arrangement rule of the Bayer format image, separates the color channels first and then performs time domain difference calculation, greatly reduces the data entropy value, and obtains a compression ratio much higher than that of a general lossless compression algorithm on the premise of completely lossless original pixel values; meanwhile, independent compression and packaging are performed with the minimum service unit as the granularity, target data can be directly located and accessed without decompression and large-scale package, the data retrieval and calculation overheads are reduced, the traceability of the original data is retained, the statistical confidence of the test result is not affected, and the batch data storage requirement in the image sensor production test scene is adapted.

The invention relates to the technical field of laser cutting, in particular to a metal plate laser cutting machining device and a laser cutting machining method thereof. The method comprises the following steps: selecting a puncture point of a contour, and starting cutting at the puncture point; according to the pixel value difference of corresponding pixel points in the sheet metal infrared image of the start cutting point of the cutting crack in the local time period at the start and end moments, the position and the pixel value of the pixel points on the cutting crack, and the cutting position and the width of the cutting crack, the thermal response goodness degree is obtained; according to the relative size of the puncture preferences of pixel points on cutting cracks of each local time period and the previous local time period of each contour and the thermal response optimization degree of the previous local time period, the pulse demand degree is obtained, then the actual pulse frequency of the previous local time period is adjusted, the optimal pulse frequency of each local time period is obtained, and the optimal pulse frequency of each local time period is obtained; and cutting of each contour is completed. According to the method, the optimal pulse frequency of the local time period of each contour is determined in a self-adaptive mode, and the metal plate laser cutting precision is improved.

The invention provides a board-level TGV packaging defect detection method and system, a medium and electronic equipment, and relates to the technical field of through hole detection, and the method comprises the steps: obtaining a to-be-detected image and a standard template image, and determining feature points on the standard template image; obtaining position information of the feature points, finding a target point in the to-be-detected image according to the position information, and searching all pixel points within a second preset range from the target point one by one until a feature positioning kernel having the same pixel value distribution as the feature points is found; obtaining the position deviation of the feature positioning kernel and the feature points, and correcting the position of each pixel point in the to-be-detected image in combination with a translation method and an affine transformation method to obtain a target image; and extracting a pixel value difference of pixel points corresponding to the target image and the standard template image to obtain a first defect detection result. The problem that an existing detection method is low in efficiency and accuracy is solved.

Efficiently acquire data for machine learning to identify the characteristics of an object. [Solution] The information processing device has a communication unit and a control unit that communicates using the communication unit. The control unit acquires a first image of an object captured with light from a first region and a second image of the object captured with light from a second region from an imaging device. Using a model that has learned the correspondence between the first and second image pairs, the control unit generates a fourth image using light from the second region from a third image of another object captured with light from the first region, such that the difference in pixel values ​​between the third image and the fourth image is small or the edge similarity is large.

This invention relates to the field of data anomaly detection technology, and more particularly to a method for detecting abnormal behavior in surveillance video data based on deep learning. The method acquires video images of any individual in real time and detects them. When no frontal position information of the individual is detected, the video image is marked as a target image. Multiple consecutive frames of target images are acquired to obtain a target image set. Based on the head and hand position information of any individual in each frame of the target image set, motion coefficients are obtained to identify whether any individual exhibits suspected abnormal behavior. If any individual exhibits suspected abnormal behavior, the abnormal behavior detection result is determined based on the smoke concentration data in the target area and the pixel value differences of pixels in each frame of the target image set. This invention can accurately identify abnormal behavior of employees in a target area.

The invention relates to the technical field of image area detection, in particular to a tea wall breaking rate detection method, equipment and system. The method comprises the following steps: acquiring gray images of leaves at different angles after each batch of rolling, and initially quantifying the wall breaking condition through a pixel value difference area to obtain an initial wall breaking index; determining a suspected crack edge according to the edge direction consistency degree and the width smoothness condition, obtaining a wall breaking sufficient evaluation index according to the length position distribution uniformity condition of the suspected crack, and analyzing the boundary complexity to adjust the initial index to obtain an adjusted wall breaking index; and obtaining a confidence coefficient by adjusting index consistency and a historical batch similarity degree through different angles, and determining a final wall breaking rate based on the confidence coefficient. According to the method, wall breaking evaluation is adjusted by considering the influence of leaf veins and tearing characteristics, abnormal conditions caused by process drift are found in time through real-time detection and correlation historical process reference analysis after rolling, and stable quality of tea production is guaranteed while the accuracy of wall breaking evaluation is improved in factory production.

A thermal image processing method according to the present invention comprises the steps of: receiving a captured image from a thermal image camera; dividing each frame included in the captured image into a plurality of blocks; identifying each block as a target block or an excluded block on the basis of the pixel value difference between corresponding blocks of two neighboring frames and / or the average pixel value of each block of the frame; determining whether to mark each block position on the basis of the cumulative count of the target block within a plurality of frames; calculating a non-uniformity correction value for an unmarked block position; and outputting a corrected image by correcting the captured image on the basis of the calculated non-uniformity correction value.

The embodiment of the present application provides a method, device, equipment and storage medium for determining the offset of bias scanning, which belongs to the field of bias scanning technology. The electronic device can obtain projection data obtained by bias scanning the target object; determine the predicted position of the rotation center axis mapped in the detector based on the difference in pixel values ​​of the projection data pairs in each column in the projection data; determine the offset range of the detector according to the difference in pixel values ​​of the projection data pairs within a preset range; determine the offset of the bias scanning according to the fluctuation of the pixel values ​​of the reconstructed image, wherein the preset range is a preset number of columns of data including the predicted position in the projection data, and the reconstructed image is obtained by reconstructing the projection data based on the offset. Since a more accurate offset of the bias scanning is determined by directly analyzing and processing the projection data, there is no need to use a correction model, so the process of determining the offset of the bias scanning can be simplified, and the efficiency of determining the offset of the bias scanning can be improved.

An encoder (100) includes a processor and memory. Using the memory, the processor determines an asymmetrical filter characteristic across a block boundary, based on pixel values across the block boundary (S141), and performs deblocking filtering having the filter characteristic determined (S142). For example, when determining the filter characteristic, the processor may increase a difference in the filter characteristic across the block boundary with increase in difference in pixel value.

The application relates to a defect identification method, device and computer equipment, which comprises the following steps: acquiring an image containing a power small fitting collected by an image collection device; determining a depth threshold according to an actual shooting distance of the image; wherein the actual shooting distance is the actual distance between the image collection device and the power small fitting, and the depth threshold is positively correlated with the actual shooting distance; extracting a region of interest containing the power small fitting from the image according to the depth threshold; determining the definition of the region of interest according to the pixel value difference of different pixel points in the region of interest; and performing defect identification of the power small fitting according to the region of interest and the definition when the definition is greater than a preset definition threshold. The application can improve the accuracy and efficiency of image screening to ensure the accuracy and efficiency of defect identification.

An image processing device 1 includes a processing unit 11 and a training unit 12, and performs noise reduction processing on a target image 23. The processing unit 11 inputs an input image 21 to a CNN (convolutional neural network) and outputs an output image 22 from the CNN. The training unit 12 uses an evaluation function based on the output image 22 and the target image 23 and trains the CNN based on a value of the evaluation function. The evaluation function includes an error evaluation term, which represents an evaluation value related to an error between the output image and the target image, and a regularization term, which represents an evaluation value related to a difference in pixel values between adjacent pixels in the output image.The image processing device 1 repeatedly performs the respective processes of the processing unit 11 and the training unit 12 a plurality of times, and after the noise reduction processing, sets the output image 22 as the image after the respective processes have been repeatedly performed a certain number of times. Thus, an image processing device and an image processing method capable of suppressing image quality deterioration due to overtraining of a CNN in noise reduction processing using a DIP technique are realized.

An imaging system includes a controllable light source; an image sensor; metalens for focusing incoming light onto the image sensor; and processor(s). The processor(s) is configured to control a light source using a first illumination intensity and / or first illumination wavelength, while controlling the image sensor to capture first image; control the light source using a second illumination intensity and / or second illumination wavelength, while controlling the image sensor to capture a second image; calculate measured differences between pixel values of pixels in the first image and pixel values of corresponding pixels in the second image; estimate expected pixel value differences based on difference between the first and second illumination intensities and / or differences between the first and second illumination wavelengths; and correct pixel values of pixels in the first image and / or second image based on deviation in measured differences from expected differences.

In the present invention, an information processing device is capable of generating sub-images to be sequentially displayed, and comprises: a first processing unit that, on the basis of a frame image, is capable of generating two or more sub-images among a plurality of sub-images having a plurality of pixel values and arranged in a predetermined arrangement pattern in the frame image; a detection unit that detects detection pixels to be processed, on the basis of the display order of corresponding pixels of the two or more sub-images and the pixel value difference between the pixels in the display order and on the basis of the coordinates of the frame image; and a second processing unit that executes processing for reducing the pixel value difference between corresponding pixels in the detection pixels.

The application provides an image frame encoding method and device, a chip, electronic equipment, a storage medium and a computer program product, and relates to the field of image processing. The method comprises the following steps: obtaining a current image frame, the current image frame comprising a first image frame and a second image frame, the first image frame being a previous image frame of the current image frame, the second image frame being a previous image frame of the first image frame, and the current image frame being an image frame in a video stream that needs to be transmitted from a first node to a second node; determining a current predicted image frame according to the first image frame and the second image frame; determining a residual image between the current image frame and the current predicted image frame; determining a plurality of first image blocks with pixel value differences between the current image frame and the first image frame; in a case where the number of the first image blocks is less than or equal to a preset number, losslessly compressing image blocks in the residual image that are located at the same positions as the plurality of first image blocks; and transmitting the losslessly compressed image blocks to the second node.