31 results about "Pixel value differencing" patented technology

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 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 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.

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 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 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.

The present application relates to the technical field of laser cutting, and particularly relates to a sheet metal laser cutting device and a laser cutting method thereof. The present application selects a piercing point of an outline and starts cutting at the piercing point; according to the pixel value difference of the corresponding pixel points in the sheet metal infrared image of the start and end time of the starting cutting point of the cutting crack in the local time period, the position and pixel value of the pixel points on the cutting crack, and the cutting position and the cutting crack width, the heat response position optimization degree is obtained; according to the relative size of the piercing optimization degree of the pixel points on the cutting crack of each local time period and the previous local time period of each outline, and the heat response position optimization degree of the previous local time period, the pulse demand degree is obtained, and then the actual pulse frequency of the previous local time period is adjusted, the preferred pulse frequency of each local time period is obtained, and the cutting of each outline is completed. The present application adaptively determines the preferred pulse frequency of the local time period of each outline, and improves the sheet metal laser cutting precision.

This invention discloses an underwater image collaborative enhancement and super-resolution method, belonging to the field of computer vision technology. It includes constructing a training dataset for the model training process using an underwater image dataset with visual enhancement reference images. The training dataset includes low-resolution original underwater degraded images, low-resolution reference ground truth images, high-resolution original degraded underwater images, and high-resolution reference ground truth images. An underwater image collaborative enhancement and super-resolution deep learning network is constructed, using pairs of original underwater degraded images as input to the deep network, and using their corresponding low-resolution reference ground truth images to constrain and optimize the predicted images output by the model. The model training process is constrained and optimized by minimizing pixel value difference loss and structural similarity loss, thereby training the underwater image collaborative enhancement and super-resolution deep learning network. Any pair of original underwater degraded images is used as input to the model, and the output is obtained through model calculation.

Disclosed is a method for enabling exposure-guided three-dimensional (3D) reconstruction, including (i) receiving Low Dynamic Range (LDR) input image captured by camera; (ii) rendering High Dynamic Range (HDR) image corresponding to input image, using HDR 3D reconstruction model pre-trained for HDR image reconstruction of 3D environment; (iii) applying HDR to LDR tone-mapping operator on HDR image, for producing tone-mapped LDR image; (iv) determining first loss function (FLF) between input image and tone-mapped LDR image, FLF comprising weighted pixel value differences between pixels of input image and corresponding pixels of tone-mapped LDR image; (v) determining whether input image has saturated pixel(s), saturated pixel(s) comprises: highlight saturated pixel, shadow saturated pixel; (vi) de-weighting pixel value difference corresponding to saturated pixel in FLF; (vii) back-propagating gradient of FLF with respect to model parameters, through differentiable render function of HDR 3D reconstruction model, for adjusting model parameters in way that reduces FLF.

The application discloses a pupil positioning method and device, which establishes M vectors for the to-be-processed pixels of an image, the M vectors correspond to different angles, and the vectors represent the pixel value difference between L pixels at the angle corresponding to the vector of the current pixel and the current pixel; for the pixels in a preset image region, an accumulated vector of the M vectors of the current pixel is obtained, a direction interval to which the accumulated vector of the current pixel belongs is determined, the average position of the pixels in the preset image region whose accumulated vectors belong to the same direction interval is obtained, and a mapping vector of any direction interval is established with the preset position and the average position as end points; and whether the preset image region corresponds to a pupil is determined according to the uniformity of the distribution of the mapping vectors. According to the application, the vectors reflecting the pixel value difference between the pixels and the surrounding pixels of the image are established according to the pixel values of the image, the direction interval distribution of the vectors in the image region is determined, and whether the image region corresponds to a pupil is determined, so that the pupil can be positioned more accurately under various complex conditions.

The application relates to the technical field of image processing, in particular to a circuit etching method and device for a thermal printing sheet, which comprises the following steps: obtaining each structure region in an exposure image of the thermal printing sheet; obtaining pixel disorder degrees of each structure region by equally dividing all pixel values in each structure region and analyzing pixel step changes and pixel value differences in the region; and obtaining an angle point distribution difference degree of each structure region by using angle point features in each structure region, taking corresponding structure regions with the same angle point distribution difference degree as same structure regions of each structure, and then obtaining a search step length of the same structure regions of each structure to etch the circuit of the thermal printing sheet. The application can improve the efficiency of circuit etching of the thermal printing sheet.

This invention relates to the field of image processing technology, specifically to a visual quality inspection method for connectors in new energy vehicles. The method acquires an image of the connector pins; determines a local similarity coefficient based on the pixel value difference between each pixel and its neighboring pixels within each first cluster region obtained by dividing the pin image; determines a sharpness evaluation parameter for each first cluster region based on the difference in spatial coordinate gradient direction at each first pixel within each first cluster region; classifies each first cluster region as a tip region or a root region based on the sharpness evaluation parameter; determines a pixel value correction coefficient for each second pixel based on the pixel value distribution characteristics between second pixels within each second cluster region in the same root region; and corrects the pixel value of each second pixel based on the pixel value correction coefficient. Thus, the image is corrected using the pixel value correction coefficient, improving the detection accuracy when comparing the corrected image with a standard part image.

The invention relates to the technical field of image processing, in particular to a visual quality inspection method for a new energy automobile connector. The method comprises the steps of obtaining a pin image of a connector; determining a local similarity coefficient according to a pixel value difference between each pixel point and an adjacent pixel point in each first clustering region obtained by dividing the pin image; determining clear evaluation parameters of the first clustering areas according to the space coordinate gradient direction differences at the first pixel points in the first clustering areas; judging each first cluster region as a tip region or a root region according to the clear evaluation parameters; determining a pixel value correction coefficient of each second pixel point according to a pixel value distribution characteristic between the second pixel points in each second clustering region in the same root region; and correcting the pixel value of each second pixel point according to the pixel value correction coefficient, thereby performing correction processing on the image by using the pixel value correction coefficient, and improving the detection precision when the corrected image is compared with the standard component image.

To achieve both detection accuracy of a print defect and calculation cost in inspection of a printed matter.SOLUTION: An image processing apparatus that inspects a printed matter formed by a printing apparatus includes an image acquisition unit that acquires a reference image serving as an inspection reference of the printed matter and a read image obtained by reading the printed matter by a reading device, an alignment unit that performs alignment between the reference image and the read image, an extraction unit that extracts a defect candidate area having a possibility of a printing defect in a correspondence relationship between the reference image and the aligned read image, and a detection unit that detects a defect based on a difference value of pixel values in a search range larger than a search range of the defect candidate area for pixels included in the defect candidate area extracted by the extraction unit.SELECTED DRAWING: Figure 2

An image processing apparatus includes a processing unit and a training unit, and performs noise reduction processing on a target image. The processing unit inputs an input image to a CNN, and outputs an output image from the CNN. The training unit uses an evaluation function, and trains the CNN based on a value of the evaluation function. The evaluation function includes an error evaluation term representing an evaluation value related to an error between the output image and the target image, and a regularization term representing an evaluation value related to a difference of pixel values between adjacent pixels in the output image. The image processing apparatus repeatedly performs respective processes of the processing unit and the training unit, and sets the output image after the respective processes are repeatedly performed a certain number of times as an image after the noise reduction processing.

The present application relates to the technical field of image region detection, and particularly relates to a tea leaf broken wall rate detection method, device and system. The method obtains different angle leaf gray scale images after each batch of rolling, quantizes the broken wall condition through the initial area of pixel value difference, and obtains an initial broken wall index; determines a suspected crack edge through the consistent degree of edge direction and the width smoothing condition, obtains a broken wall sufficient evaluation index according to the length position distribution uniformity of the suspected crack, and adjusts the initial index by analyzing the boundary complexity to obtain an adjusted broken wall index; obtains a confidence degree through the consistency of the adjusted index at different angles and the similarity with historical batches, and determines the final broken wall rate based on the confidence degree. The present application considers the influence of the leaf vein and tearing characteristics to adjust the broken wall evaluation, and analyzes the correlation historical process reference in real time after rolling to find abnormal conditions caused by process drift in time, improves the broken wall evaluation accuracy in the factory production, and guarantees the quality stability of the tea production.

The invention discloses an underwear design parameter adjustment method and system based on image analysis, and relates to the technical field of image analysis, and the method comprises the following steps: constructing optical data of a reference object under a fitting site under a standard illumination condition; acquiring an underwear image of a fitting person under the fitting site, and acquiring a local reference image of the reference object at the same time; identifying a pixel value difference between the local reference image and the optical data; calculating a pixel gain and a pixel offset required by the underwear image based on the pixel value difference; correcting each pixel in the underwear image based on the pixel gain and the pixel offset; carrying out picture analysis processing on the corrected underwear image; and adjusting underwear design parameters based on the underwear image features. According to the method, image deviation caused by illumination condition changes can be effectively corrected, and the accuracy of image data is ensured, so that the accuracy and reliability of underwear design parameter adjustment are improved.

To suppress unnecessary information included in a difference image.SOLUTION: An image processing system according to an embodiment comprises: image acquisition means which acquires a first image and a second image of a subject captured at different times; identification information acquisition means which acquires identification information of portions at a plurality of coordinates of the first image; and difference image generation means which when a target pixel is identified as a first portion with the identification information, generates a difference image between the first image and the second image so as to regard as a pixel value of the target pixel a corrected difference value obtained by subjecting an initial difference value expressing a difference of pixel values between a first coordinate and a second coordinate to predetermined correction processing according to a result of determining whether or not the pixel values of the first coordinate of the first image corresponding to the target pixel and the second coordinate of the second image corresponding to the first coordinate satisfy a predetermined condition.SELECTED DRAWING: Figure 2

The application discloses a kind of underwear design parameter adjustment method and system based on image analysis, it is related to image analysis technical field, including the following steps: constructing optical data under standard illumination condition under reference object in fitting site;Acquire the underwear image of fitting person in the fitting site, and simultaneously acquire the local reference image of the reference object;Identify the pixel value difference between the local reference image and the optical data;Based on the pixel value difference, the pixel gain and pixel offset required by underwear image are calculated;Based on the pixel gain and pixel offset, each pixel in the underwear image is corrected;After the correction, the underwear image is processed by picture analysis;Based on the underwear image feature, adjust underwear design parameter.The application can effectively correct the image deviation caused by illumination condition change, ensure the accuracy of image data, so as to improve the accuracy and reliability of underwear design parameter adjustment.

To generate more accurate depth estimation models from images. [Solution] This is a program that causes a computer to perform a depth calculation step, a motion calculation step, a correspondence calculation step, and a learning step. The learning step updates the parameters of the first and second estimated models to optimize a first loss function that includes a term showing the difference between correspondence information and correspondence training data, which is training data for the correspondence between the first and second pixels; a second loss function that includes a term for depth; and a third loss function that includes a term showing the difference in pixel values ​​between the first and second pixels whose correspondence is indicated by the correspondence information. The program then generates the first and second estimated models represented by the updated parameters.