34 results about "Local histogram" patented technology

The invention relates to the technical field of image processing, in particular to an anti-collision beam weld defect detection method and system based on image processing, and the method comprises the steps: carrying out the image collection of a weld region of a produced anti-collision beam, and obtaining a gray image; the method comprises the following steps: performing initial partitioning on a grayscale image, respectively obtaining a local complexity index of each initial sub-block, obtaining at least two adaptive sub-blocks based on the local complexity index of each initial sub-block, and performing adaptive local histogram equalization on each adaptive sub-block to obtain a target grayscale image; the method comprises the steps of performing edge detection on a target grayscale image to obtain at least two edge pixel points, performing frequency domain conversion on the target grayscale image according to a gradient direction of each edge pixel point to obtain a frequency domain image, performing filtering processing and time domain conversion on the frequency domain image to obtain a denoised image, and identifying defects in the denoised image by using a neural network. And the defect detection efficiency is improved by inhibiting the periodic texture in the weld seam image.

The invention relates to the technical field of vision assistance, and discloses an anesthesia puncture positioning method and system based on vision assistance, and the method comprises the steps: accurately positioning an anesthesia puncture point through multi-view image fusion, hyperspectral image processing, image preprocessing, illumination equalization, edge enhancement, depth feature extraction and the like. The method comprises the following steps: firstly, constructing an image coordinate system, collecting a plurality of camera images, and obtaining a fused clear image through a designed multi-view fusion algorithm and distance calculation; and in combination with a hyperspectral image fusion algorithm, the image quality is further improved. Then, residual mapping filtering denoising and local histogram enhancement are used for illumination equalization, and the image contrast and edge details are enhanced; a convolutional neural network is adopted, interest point detection is carried out, a Hessian matrix is utilized to describe image second-order changes, and local depth features are extracted. And accurate positioning of an anesthesia puncture point is realized through a weighted soft voting classifier and a dynamic threshold method.

The invention relates to the technical field of machine vision and wind power detection, and discloses a surface defect identification method and system for a rotor core of a wind driven generator, and the method comprises the steps: collecting an original image of the surface of the rotor core, and carrying out the gray mapping, and obtaining a single-channel gray image; carrying out frequency domain periodic texture suppression and local histogram equalization processing with contrast limitation on the single-channel grayscale image to obtain an enhanced feature image; constructing a background fitting model for the enhanced feature image and performing differential operation to obtain a background differential image; performing adaptive segmentation and morphological refining based on a texture direction according to the background difference image to obtain a refined defect connected domain; local gray level distribution is extracted, sub-pixel-level geometric feature calculation is carried out, and defect geometric attribute data are obtained; and spatial clustering and grading evaluation are carried out according to the data, and a final defect distribution map is determined. According to the method, periodic texture interference can be effectively suppressed, and sub-pixel-level precise positioning and intelligent grading of the tiny defects are realized.

The invention relates to the technical field of image data processing, in particular to an impurity identification method and system for fruit and vegetable powder, and the method comprises the steps: collecting a fruit and vegetable powder image on a conveying belt, preprocessing the image into a gray-scale map, and calculating a dark channel index based on the gray-scale distribution characteristics in a neighborhood window to preliminarily screen a dark region; counting dispersion of gradient amplitude and information entropy of gradient direction in a pixel point neighborhood window, distinguishing smooth shadow and rough impurities by using the dispersion and the information entropy, and correcting a dark channel index to obtain impurity confidence; constructing a local histogram by using the impurity confidence as a weight, and carrying out adaptive weighted histogram equalization processing on the grey-scale map to enhance the impurity contrast and suppress the shadow; and completing impurity identification through Otsu threshold segmentation and connected domain analysis. According to the method, the problem that shadow is easily misjudged as impurities when accumulated powder is processed by a traditional algorithm is effectively solved, and the detection accuracy is improved.

The invention discloses a visualization method for marine survey shallow stratum profile data, which is applied to the field of marine survey, and comprises the following steps: extracting acoustic echo samples, navigation data and attitude data from JSF format data collected by a shallow stratum profiler, analyzing the JSF format data into a JSON format containing the original echo intensity, geographic position and attitude information of each Ping, and analyzing the JSON format data to obtain the marine survey shallow stratum profile data. The method comprises the following steps of: firstly, dividing a local sub-block, mapping the local sub-block into an initial gray level image matrix, then enhancing the initial gray level image matrix by adopting CLAHE, and generating a high-contrast profile image capable of clearly identifying the characteristics of the seabed shallow layer structure by dividing local sub-blocks, calculating a local histogram cumulative distribution function and redistributing pixel values. According to the method, the data accuracy is ensured, the accuracy and the efficiency of interpreting the submarine pipeline burial depth and the stratum structure by interior interpretation personnel are improved, the data processing cost is reduced, the readability, the transportability and the compatibility of the data are improved, and a standardized interface can be provided for long-term management of ocean survey data and application of different platforms.

This application provides a multi-scale hybrid DR image enhancement method, apparatus, electronic device, and storage medium. The method includes: acquiring a target image to be identified; dividing the target image into N blocks according to N different block scales to obtain a first block image corresponding to each block scale; for each block scale, performing histogram equalization processing on each first block image corresponding to that block scale according to its corresponding contrast constraint to obtain a corresponding second block image; for each block scale, performing edge smoothing processing on each second block image corresponding to that block scale to obtain a corresponding third block image; and fusing all third block images corresponding to the N different block scales to obtain an enhanced image. This application solves the problem in related technologies where single local histogram equalization is difficult to simultaneously capture image details at different scales.

The invention discloses a detail-preserving multi-scale local histogram equalization method, which comprises the following steps of: partitioning an input image into a plurality of mutually overlapped or non-overlapped sub-blocks; selecting a multi-scale window, and defining a plurality of local neighborhood windows with different scales for each pixel point; secondly, according to a local histogram equalization (LHE) algorithm, histogram statistics is carried out on pixels in each local window of each scale, and gray mapping is carried out based on a cumulative distribution function (CDF) of the window; multi-scale result fusion is carried out, and weighted fusion is carried out on equalized results of the same pixel in different-scale windows; and finally, post-processing optimization is carried out, and further processing is carried out on the fused image. According to the method, the detail pertinence is high, the noise suppression effect can be improved, the blocking effect can be effectively eliminated, and sharp discontinuous linearity is prevented from being generated at the boundary in the aspect of mechanism.

The invention discloses a vein feature extraction method and system. The method comprises the following steps: acquiring a vein image; preprocessing the image to obtain a gamma enhanced image; setting a scale factor and a sliding window; extracting principal curvature information of the gamma enhanced image; taking the minimum value of the principal curvature information of the gamma enhanced image, and obtaining a Hessian matrix enhanced image according to the multi-scale response; carrying out local histogram equalization to obtain a local histogram equalization enhanced image; and carrying out binarization and morphological processing on the local histogram equalization enhanced image to obtain a final result. According to the method, background noise interference can be effectively suppressed, and the influence of environmental factors on vein recognition is reduced, so that the distinguishability and recognition accuracy of the vein image are improved, and the robustness and generalization ability of the model are enhanced.

The invention discloses a transmitting module, a laser radar and related electronic equipment. The invention provides a novel laser radar detection scheme, through histogram scaling design of rough measurement and fine measurement, light flight time counting is accumulated by a histogram with low time resolution, and the position of a signal peak corresponding to an object is generally determined; and then the local histogram with high time resolution is used to accumulate the light flight time counting to improve the distance measurement precision, and the design can greatly reduce the storage area of a laser radar receiving chip on the basis of not reducing the distance measurement performance, thereby reducing the chip cost.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for detecting and tracking edges in raster images using an advanced edge detection algorithm. For example, the disclosed systems generate a local histogram ranking of pixels within a sliding pixel window in a raster image, based on pixel values ​​located within the sliding pixel window. In some embodiments, the disclosed systems determine an edge thickness for an edge depicted in a region of the raster image enclosed by the sliding pixel window by comparing pixel ranks specified by the local histogram ranking of the pixels.In certain embodiments, the disclosed systems also provide the edge for display based on determining the edge thickness for the area of ​​the raster image enclosed by the sliding pixel window.

The application discloses a large-scale remote sensing image color correction method based on block local histogram matching, belongs to the technical field of remote sensing image processing, and aims at the problems of low calculation efficiency, neglect of local features and difficulty in maintaining overall color tone when existing methods process large-scale orthographic images. The correction is realized through five steps of adaptive image blocking, multi-scale statistical feature extraction, hierarchical nonlinear mapping model, high-precision seamless splicing and parallel computing framework optimization. The method can efficiently process massive data, takes into account local correction accuracy and overall color tone consistency, improves robustness through intelligent parameter adaptive adjustment, significantly eliminates image color differences, is suitable for multiple fields such as land resource investigation and city planning, and provides a high-quality solution for large-scale image splicing.

The invention discloses an image brightness adjustment method based on a specified histogram and electronic equipment, and the method comprises the steps: carrying out the filtering, brightness offset, peak homogenization and other operations on an image initial brightness histogram, and achieving different brightness adjustment targets, such as smooth distribution, peak value translation and discrimination enhancement; a specified histogram is constructed based on luminance spatial distribution, mapping distortion is suppressed, and a spatial neighborhood is associated to select an adaptive mapping value. And the method is compatible with a local histogram method, so that'value domain-space domain 'two-dimensional self-adaption is formed, and the method is adaptive to a complex scene. The method can flexibly customize the enhancement effect, meets multiple requirements, is low in algorithm complexity, is low in requirement for computing resources, can efficiently optimize the image quality, is suitable for processing various types of images such as medical images and natural images, and improves the image detail observability and visual effect.

The application discloses a method and system for extracting vein features. The method comprises the following steps: acquiring a vein image; pre-processing the image to obtain a gamma enhanced image; setting a scale factor and a sliding window; extracting principal curvature information of the gamma enhanced image; taking the minimum value of the principal curvature information of the gamma enhanced image, and obtaining a Hessian matrix enhanced image according to a multi-scale response; performing local histogram equalization to obtain a local histogram equalization enhanced image; and binarizing and performing morphological processing on the local histogram equalization enhanced image to obtain a final result. The application can effectively suppress background noise interference, reduce the influence of environmental factors on vein recognition, thereby improving the distinguishability and recognition accuracy of the vein image, and enhancing the robustness and generalization ability of the model.

An electronic device according to one embodiment among various embodiments may comprise one or more processors and a memory for storing instructions. The instructions are executed by the one or more processors and can instruct the electronic device to: generate one or more pyramid images obtained from an input image by changing the resolution thereof; obtain local histogram information and global histogram information on the basis of a first pyramid image from among the one or more pyramid images; use the one or more pyramid images so as to perform an image signal processing operation, thereby obtaining a first image; and obtain a second image by performing local tone-mapping and global tone-mapping on the first image on the basis of the local histogram information and the global histogram information. Other various embodiments are possible.

The invention relates to a histogram generation method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring a local histogram corresponding to each partial node; acquiring local significant change points in each local histogram, and determining global significant change points according to the local significant change points; dividing global data buckets according to the global significant change points; according to the global data bucket, determining a target data bucket from local data buckets corresponding to the local histogram; acquiring a local frequency corresponding to the target data bucket, and performing weighted calculation on the local frequency to generate a target frequency; and generating a global histogram according to the global data bucket and the target frequency. By adopting the method, the accuracy of a data processing result can be improved.

This electronic device may comprise: a memory for storing instructions; a camera; and at least one processor. The instructions may cause the electronic device to: obtain, through the camera, a first set of local gain tables corresponding to multiple regions of a source image, respectively, by using an identified source image, wherein each of the local gain tables includes gains to be applied to brightness values of pixels of a corresponding region of the source image; obtain a set of pieces of local histogram information corresponding to the multiple regions, respectively, wherein the local histogram information indicates a distribution of brightness values of pixels changed by the gains; obtain a global gain table including gains to be applied to pixels of the entire region of the source image, by using the set of pieces of local histogram information; obtain a second set of local gain tables corresponding to the multiple regions, respectively, by combining the global gain table and the local gain tables included in the first set; and generate a result image, which is a result of tone mapping, by applying the second set of local gain tables to the multiple regions of the source image.

The application provides a computer vision-based structural vibration displacement monitoring method and system and a storage medium, and the method comprises the following steps: step 1, structural vibration video preprocessing; basic information of video data is automatically acquired by using a video basic information automatic acquisition algorithm, and an image gray scale and local histogram equalization processing method is used to simplify the image calculation complexity and enhance the image features; step 2, structural displacement monitoring based on improved FAST corner detection; an automatic threshold calculation method based on the maximum inter-class variance is introduced to form an improved FAST corner detection algorithm, the image features obtained by the improved FAST corner detection algorithm are combined with the pyramid Lucas-Kanade optical flow method, and a whole-process automatic image feature detection and motion tracking scheme is established. The method has the advantages that the overall monitoring process is optimized, and the method is non-contact, simple to operate and high in automation.

The application discloses a kind of depth-intensity joint optimization light weight SPAD neural network reconstruction methods, belong to single-photon imaging and depth learning technical field, including: based on wavelet transform to the original histogram data of SPAD array is carried out multistage wavelet decomposition, obtain compressed time-frequency domain data;Utilize multiscale superpixel to carry out non-maximum suppression, and obtain estimated depth map by voting mechanism;Local histogram is extracted from compressed time-frequency domain data, and sum along time dimension obtains intensity map;The estimated depth map is expanded, and expanded depth map is obtained;The double-branch complementary depth reconstruction network including depth branch, edge branch and perception interaction module is constructed;Real depth map and real edge gradient map are used to guide network, and training is carried out using joint loss function, to realize light weight SPAD neural network reconstruction.The method significantly improves the depth reconstruction accuracy under low illumination while greatly reducing the amount of calculation.

The invention provides an expressway thrown object detection method and system based on open vocabulary detection. The method belongs to the field of thrown object detection, and comprises the following steps: carrying out local histogram equalization on each monitoring image block; extracting a potential target area of a throwing object in the image after local histogram equalization; calling a visual encoder and a text encoder in a visual language pre-training model CLIP to respectively extract regional features of the potential target region and text features of categories in the open vocabulary detection list, and then inputting the regional features and the text features into a plurality of visual language fusion modules to obtain regional features of fused text information; and finally, calculating the similarity between the regional features of the fusion text information and the text features to align the potential target region with the category in the open vocabulary detection list, so as to identify the category of the thrown object in the potential target region, and recording the throwing time. The method can accurately capture and identify the thrown objects on the expressway.

The application discloses a malicious code detection method and system based on an improved GhostNetV2 and a medium, relates to the technical field of network information security, and comprises the following steps: extracting an Android software code binary file to be detected, and converting the binary file into an RGB image; performing image enhancement processing on the RGB image by using a local histogram equalization algorithm; converting the RGB image into a single-channel image, and extracting corresponding image texture features; inputting the image texture features into a trained lightweight malicious software detection model of the improved GhostNetV2 for detection, and obtaining a detection result. The method effectively improves detection precision by performing image enhancement through local histogram equalization, adopts the improved lightweight deep learning model to detect malicious software, reduces the parameter quantity, improves detection efficiency, and enhances detection performance and robustness.

The invention discloses a malicious code detection method and system based on improved GhostNetV2 and a medium, and relates to the technical field of network information security, the method comprises the following steps: extracting a binary file of an Android software code to be detected, and converting the binary file into an RGB image; performing image enhancement processing on the RGB image by using a local histogram equalization algorithm; converting the RGB image into a single-channel image, and extracting corresponding image texture features; and inputting the image texture features into a trained lightweight malicious software detection model of the improved GhostNetV2 for detection to obtain a detection result. According to the method, image enhancement is carried out through local square equalization, the detection precision is effectively improved, an improved lightweight deep learning model is adopted to detect malicious software, the parameter quantity is reduced, the detection efficiency is improved, and the detection performance and robustness are enhanced.

The invention provides a method and system for enhancing a Mura defect image in an OLED display panel, and relates to the field of defect detection and quality control, and the method comprises the steps: obtaining an original image of the OLED display panel, the original image having a Mura defect; performing histogram image conversion and pseudo-infrared image conversion on the original image by adopting a dual-channel preprocessing strategy to obtain two complementary histogram enhanced images and pseudo-infrared images; wherein the histogram image conversion process comprises color image graying, local histogram statistics, equalization mapping and smooth fusion, and the pseudo infrared image conversion process comprises contrast and brightness adjustment and pseudo infrared color mapping; and performing three-mode feature fusion on the original image, the histogram enhanced image and the pseudo-infrared image to obtain a defect integrated image. According to the method, a complementary information fusion image preprocessing mode is adopted, histogram equalization and pseudo-thermal imaging transformation are combined, and the visibility of low-contrast defects can be enhanced.

This invention discloses an image brightness adjustment method and electronic device based on a defined histogram. First, the initial brightness histogram of the image is filtered, brightness shifted, and peak homogenized to achieve different brightness adjustment targets, such as smoothing the distribution, shifting peak values, and enhancing discriminability. A defined histogram is constructed based on the spatial distribution of brightness to suppress mapping distortion and select suitable mapping values ​​from associated spatial neighbors. It is compatible with local histogram methods, forming a two-dimensional adaptive "value domain-spatial domain" model, adapting to complex scenarios. The enhancement effect can be flexibly customized to meet diverse needs, and the algorithm has low complexity and low computational resource requirements. It can efficiently optimize image quality and is applicable to various image processing types, including medical images and natural images, improving image detail observation and visual effects.

The application discloses a kind of plankton microscopic image enhancement method and system of optical density reconstruction, belong to resource and environmental technical field.Firstly, the microscopic image is converted to Lab space;In L channel, optical density map is calculated according to Lambert-Beer law, the highest M point mean value of brightness is taken as incident light intensity I0, the texture is enhanced by reconstructing L channel initial map, Gamma transformation and local histogram equalization;Local variance is calculated in a, b channel, color retention weight is obtained by Tanh mapping, and color cast is corrected in combination with background color mean value.Finally, the enhanced Lab image is converted back to RGB format.Through testing, the method can effectively solve the problem of blurred limb features caused by lugol reagent fixation, and provide key technical support for plankton species identification.

Method: generating local histogram ranking of pixel values located within sliding window within raster image; determining edge strength for edge depicted by comparing pixel ranks indicated by local hi

The application discloses a kind of depth-intensity joint optimization light weight SPAD neural network reconstruction methods, belong to single-photon imaging and depth learning technical field, including: based on wavelet transform to the original histogram data of SPAD array is carried out multistage wavelet decomposition, obtain compressed time-frequency domain data;Utilize multiscale superpixel to carry out non-maximum suppression, and obtain estimated depth map by voting mechanism;Local histogram is extracted from compressed time-frequency domain data, and sum along time dimension obtains intensity map;The estimated depth map is expanded, and expanded depth map is obtained;The double-branch complementary depth reconstruction network including depth branch, edge branch and perception interaction module is constructed;Real depth map and real edge gradient map are used to guide network, and training is carried out using joint loss function, to realize light weight SPAD neural network reconstruction.The method significantly improves the depth reconstruction accuracy under low illumination while greatly reducing the amount of calculation.

The invention discloses a zooplankton microscopic image enhancement method and system based on optical density reconstruction, and belongs to the technical field of resources and environments. The method comprises the following steps: firstly, converting a microscopic image into a Lab space; an optical density map is calculated in an L channel according to the Lambert-Beer law, the average value of M points with the highest brightness is taken as incident light intensity I0, and texture is enhanced through Gamma transformation and local histogram equalization by reconstructing an initial map of the L channel; local variance is calculated in a channel a and a channel b, a color retention weight is obtained through Tanh mapping, and color cast is corrected in combination with a background color mean value. And finally, the enhanced Lab image is converted to an RGB format. Tests show that the method can effectively solve the problem of limb feature blurring caused by fixation of the Luge reagent, and provides key technical support for species identification of zooplankton.

This invention relates to the fields of machine vision and wind power inspection technology, and discloses a method and system for identifying surface defects in wind turbine rotor cores. The method includes acquiring an original image of the rotor core surface and performing grayscale mapping to obtain a single-channel grayscale image; performing frequency domain periodic texture suppression and contrast-limited local histogram equalization on the single-channel grayscale image to obtain an enhanced feature image; constructing a background fitting model for the enhanced feature image and performing difference operations to obtain a background difference image; performing adaptive segmentation and texture direction-based morphological refinement based on the background difference image to obtain refined defect connected components; extracting local grayscale distribution and performing sub-pixel-level geometric feature calculations to obtain defect geometric attribute data; and performing spatial clustering and hierarchical evaluation based on the data to determine the final defect distribution map. This method can effectively suppress periodic texture interference and achieve sub-pixel-level accurate positioning and intelligent hierarchical classification of minute defects.

The present application relates to the technical field of image data processing, and more particularly to a method and system for identifying impurities in fruit and vegetable powder, which comprises the following steps: collecting images of fruit and vegetable powder on a conveyor belt and preprocessing them into grayscale images, calculating a dark channel index based on the grayscale distribution characteristics within a neighborhood window to preliminarily screen dark areas, counting the dispersion of gradient amplitude and the information entropy of gradient direction within the neighborhood window of a pixel point, using the two to distinguish smooth shadows from coarse impurities, correcting the dark channel index to obtain an impurity confidence, using the impurity confidence as a weight to construct a local histogram, performing adaptive weighted histogram equalization processing on the grayscale image to enhance the contrast of impurities and suppress shadows, and completing impurity identification through Otsu threshold segmentation and connected component analysis. The present application effectively solves the problem that traditional algorithms are prone to misjudging shadows as impurities when processing piled powders, and improves the detection accuracy.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for detecting and tracing edges in raster images using an advanced edge detection algorithm. For example, the disclosed systems generate, for a sliding pixel window in a raster image, a local histogram ranking of pixels according to pixel values located within the sliding pixel window. In some embodiments, the disclosed systems determine, for a region of the raster image enclosed by the sliding pixel window, an edge strength for an edge depicted in the raster image by comparing pixel ranks indicated by the local histogram ranking of the pixels. In certain embodiments, the disclosed systems also provide the edge for display based on determining the edge strength for the region of the raster image enclosed by the sliding pixel window.