15 results about "Edge density" patented technology

The invention relates to the field of defect detection, in particular to a wallpaper defect detection method and system based on machine vision. The method comprises the following steps: analyzing acquired to-be-detected wallpaper image data to obtain regional texture variance, edge density and brightness gradient indexes, and generating a scale image layer set; extracting salient edge points in each scale image and generating an edge anchor point set; analyzing the scale image layer set and the edge anchor point set to obtain a texture difference index, and generating a texture feature map set according to the edge anchor point set; processing the texture feature map set to generate a texture significance distribution map; based on the texture significance distribution diagram, main direction distribution is extracted, a direction residual error model is constructed, and a residual error diagram and a direction difference distribution diagram are generated; and establishing a region scoring matrix, and generating a wallpaper defect credibility distribution map and a defect classification image output set on the basis of the region scoring matrix. The wallpaper defect detection precision can be improved.

The application discloses a multi-source data feature extraction and fusion method and system for power grid infrastructure construction, and relates to the technical field of electronic data processing.The method comprises the following steps: obtaining monitoring images and noise data of a construction site and performing pretreatment; extracting color, texture and edge density features from the images, generating an initial segmentation mask of an engineering area through weighted fusion; performing morphological operation, area filtering and solid degree filtering optimization on the mask, extracting an engineering area area proportion progress index and shape features based on the final mask; extracting frequency band energy, power spectrum density peak value and statistical features from the noise data; and finally, fusing the image features and noise features through an adaptive weighted fusion algorithm based on variance and correlation coefficient to generate a final feature vector.The application improves the accuracy and reliability of state perception through multi-source data processing and feature-level fusion, and provides a high-quality data basis for subsequent intelligent analysis.

The invention relates to the technical field of image processing, can be applied to business system platforms of financial science and technology, medical health and the like, and discloses a target sample identification method, device and equipment and a computer readable storage medium, the method comprises the following steps: generating a global feature vector according to scene category information and space structure information in each sample image; calculating the similarity between the global feature vectors of the target image and the other images, selecting the images with the similarity greater than a first threshold value as similar images, and converging all the similar images to generate a first candidate image group; calculating a background score according to the edge density feature and the texture feature in the background region of each sample image, and deleting the images with the background scores smaller than a second threshold value to form a second candidate image group; and selecting a sample image pair of which the number of matched key points is less than a third threshold and the similarity is greater than a fourth threshold from the second candidate image group as a target sample. According to the invention, the accuracy of identifying the target sample is improved.

An image content analysis method is applied to an image analysis apparatus and includes acquiring an image, utilizing an edge detection technology to compute an edge density of the image, utilizing a texture detection technology to compute a texture richness of the image, and analyzing the edge density and the texture richness to generate a richness score of the image.

The invention discloses an image storage method, image storage equipment and a computer storage medium, and relates to the technical field of image processing and storage. The method comprises the following steps: acquiring original data through an image acquisition sensor, calculating local contrast and edge density, performing fusion to generate a visual saliency map, and performing region segmentation to obtain an average visual saliency value of each region; calculating a statistical variance and a global peak value according to the values of all the areas, and generating an adaptive rate distortion reference value; comparing each region value with a reference value to determine a quantization step size and a visual saliency level, and performing adaptive coding on the region to form a composite data stream; calculating the proportion of the geometric center to the area of each region, packaging the proportion into metadata, and packaging and storing the metadata and the coding stream; and constructing a mapping index from the region identifier to the physical address and a reverse index based on the visual saliency level. The method can improve the utilization rate of the storage space and optimize the image quality of the important region.

The application discloses a bridge crack detection method based on image recognition and concretely relates to the field of bridge detection, which is used to solve the problems of misjudgment, missed detection and crack breakage caused by the similar shape of the code or steel stamp characters on the bridge surface and the crack fine lines; the edge graph is generated from the original image, the character candidate area is limited based on the edge density and the contrast on both sides of the edge, the character mask and the character skeleton are constructed in the character candidate area, the cost graph is constructed by combining the multi-directional line top-hat transformation inside the character mask, the minimum cost path is quantified to pass through the uniform cost and attach the uniform distance, the classification model is used to output the crossing coefficient to separate the crossing candidate set and the homologous candidate set, the sample block matching is used to generate the background graph without characters and the residual fine line graph in the character repair area to complete the missing section of the crossing candidate set and remove the corresponding pixels of the homologous candidate set, and the crack result pixel graph is output.

This invention belongs to the field of image data processing technology, specifically relating to a multi-feature adaptive cloud detection method based on satellite cloud images. The method includes: acquiring and preprocessing satellite cloud images to obtain grayscale images; extracting image feature vectors from the grayscale images, wherein the image feature vectors include contrast, edge density, texture complexity, mean brightness, and mean gradient magnitude; matching detection methods based on contrast features, edge density features, and texture complexity features in the image feature vectors; generating cloud masks based on the detection methods, wherein when K-means clustering is used as the detection method, the brightest cluster is selected to generate the cloud mask; when an adaptive threshold segmentation method is used, candidate masks are generated based on a first threshold and a second threshold formed by the standard deviation and mean of the pixel values ​​of the grayscale image, and the optimal candidate mask is selected as the optimal mask; and using morphological opening and closing operations to remove isolated noise points and fill holes in the optimal mask to obtain the final mask.

The invention belongs to the technical field of image data processing, and particularly relates to a satellite cloud picture-based multi-feature adaptive cloud detection method, which comprises the following steps of: acquiring and preprocessing a satellite cloud picture to obtain a grayscale image; extracting an image feature vector of the grayscale image, wherein the image feature vector comprises a contrast ratio, an edge density, a texture complexity, a brightness mean value and a gradient magnitude mean value; matching a detection method based on a contrast feature, an edge density feature and a texture complexity feature in the image feature vector; a cloud mask is generated based on a detection method, and when a K-means clustering method is adopted as the detection method, the brightest cluster is selected to generate the cloud mask; when the adaptive threshold segmentation method is adopted, candidate masks are generated based on a first threshold and a second threshold formed by a standard deviation and a mean value of pixel values of the grayscale image, and an optimal mask is selected from the candidate masks as an optimal mask; and removing isolated noisy points and filling holes for the optimal mask by adopting morphological opening and closing operation to obtain a final mask.

The application discloses an image storage method, an image storage device and a computer storage medium, and relates to the technical field of image processing and storage. The method comprises the following steps: acquiring original data through an image acquisition sensor, calculating local contrast and edge density, and fusing to generate a visual saliency map; obtaining an average visual saliency value of each region through region segmentation; calculating statistical variance and a global peak value according to the value of all regions to generate an adaptive rate distortion reference value; comparing the value of each region with the reference value to determine a quantization step and a visual saliency level, adaptively encoding the region to form a composite data stream; calculating the geometric center and area proportion of each region, packaging the data into metadata, and packing and storing the metadata with the encoded stream; and constructing a mapping index of region identification to a physical address and an inverted index based on the visual saliency level. The application can improve the storage space utilization rate and optimize the image quality of important regions.

The invention relates to the field of high-resolution remote sensing image analysis based on artificial intelligence, and discloses a high-resolution satellite data semantic analysis and intelligent identification system and method, and the method comprises the steps: carrying out the texture complexity, edge density and significance response analysis of a high-resolution satellite image; generating a multi-scale adaptive grid unit and establishing a spatial index; loading a corresponding multi-temporal image, and generating a multi-scale spatial-temporal feature sequence; determining a neighborhood grid set according to the multi-scale spatio-temporal feature sequence and the spatial index, generating a spatio-temporal incidence matrix and predicting a semantic change trend of a main grid, performing scale alignment and local disturbance suppression on multi-temporal features, and constructing a cross-spatio-temporal stable reference framework; fusing the cross-spatio-temporal stable reference framework, the semantic change trend, the spatio-temporal incidence matrix and the current frame preliminary semantic judgment to form a comprehensive confidence coefficient matrix; and adjusting the key feature weight and the matching parameter, and outputting a corrected grid semantic recognition result. The method has the advantage of improving the recognition precision.

The invention relates to a colorectum navigation correction method based on intra-operative image fusion, which comprises the following steps of: judging an image continuity state by analyzing tissue edge continuity and a gray level change trend between intra-operative image frames, if a breakpoint is detected, starting an image backtracking mechanism, and selecting a stable frame sequence as fusion input; dividing the intra-operative image into a plurality of space blocks, calculating the structural complexity and edge density index of each block, and only reserving a credible region as a fusion candidate region; performing spatial arrangement logic analysis on anatomical structure labels identified in a fusion result, comparing the anatomical structure labels with a preoperative standard anatomical model, and if structural sequence abnormity or logic conflict exists, judging that the fusion registration is invalid and stopping navigation updating; after the fusion error is identified, analyzing the change of the main axis direction of the structure in the operation, deducing the rotation vector of the navigation offset, carrying out local self-rotation adjustment on the image in the operation, re-aligning the pre-operation planning coordinate system, correcting the navigation position information, and using the corrected result for updating the navigation system.

The present application relates to the field of defect detection, in particular to a wallpaper defect detection method and system based on machine vision; the method comprises the following steps: analyzing the obtained wallpaper image data to be detected, obtaining regional texture variance, edge density and brightness gradient index, and generating a scale image layer set; extracting significant edge points in each scale image and generating an edge anchor point set; analyzing the scale image layer set and the edge anchor point set to obtain a texture difference index, generating a texture feature map set based on the edge anchor point set; processing the texture feature map set to generate a texture saliency distribution map; based on the texture saliency distribution map, extracting a main direction distribution, constructing a direction residual model, generating a residual map and a direction difference distribution map; establishing a regional score matrix, and generating a wallpaper defect credibility distribution map and a defect classification image output set based on the regional score matrix. The present application can improve the detection accuracy of wallpaper defects.

This invention discloses a method for detecting small targets in remote sensing images against complex backgrounds, comprising the following steps: Step 1: Acquiring the original remote sensing image data and corresponding metadata of the area to be detected; Step 2: Performing grayscale distribution stretching, band normalization, and local brightness balancing processing; Step 3: Calculating local texture repetition, edge density, regional entropy, high-frequency energy in the frequency domain, and directional gradient; Step 4: Constructing a background complexity constraint map; Step 5: Extracting the low-level feature maps of standardized remote sensing sub-patterns and performing matrix operations with the background complexity constraint map; Step 6: Inputting the initial feature tensor into an improved CrossFormer model to generate a cross-scale small target enhancement representation sequence; Step 7: Mapping the detection results to the original remote sensing image coordinate system to obtain the final detection result. This invention achieves accurate detection of small remote sensing targets through the improved CrossFormer model.

The invention discloses an insulating cylinder defect detection method and a storage medium. The method comprises the following steps: acquiring multi-angle image data of an insulating cylinder, and respectively preprocessing edge and center features through anisotropic diffusion filtering and a CLAHE algorithm to generate a high-contrast image; enhancing edge detection by adopting an improved Scharr operator, adaptively dividing rectangular sub-blocks based on an edge density function, and ensuring that each sub-block comprises at least one potential defect unit; extracting multi-scale LBP entropy value features by using wavelet transform, and constructing a support vector machine classification model to realize accurate defect classification; and finally marking the position and the size of the defect through a visualization algorithm. According to the method, a dynamic partitioning strategy and multi-scale texture analysis are fused, and the technical problems that a traditional method is high in false detection rate and small defects are missed are solved.

The invention discloses a power distribution network defect image definition screening method and system based on multi-feature fusion, and belongs to the interdisciplinary field of power system intelligent detection and computer vision technology, and the method comprises the following steps: 1, inputting an RGB image collected by a power distribution network unmanned plane in an inspection manner; step 2, basic definition feature extraction is carried out through three types of airspace features of Laplace variance, Tenenggrad gradient and Sobel gradient energy; 3, calculating the average brightness of the image, and eliminating the interference of illumination fluctuation on definition evaluation; 4, obtaining a binary edge image of the image through Canny edge detection, and calculating an edge density and an edge gradient mean value; 5, after normalization processing is carried out on the three types of basic features, a weighted model is constructed in combination with an illumination compensation factor and an edge confidence coefficient weight; and step 6, setting a definition threshold value, and if the comprehensive score reaches or exceeds the threshold value, determining that the image is a clear image and keeping the image for subsequent defect identification.