34 results about "Morphological gradient" patented technology

The invention provides a multi-scale morphological gradient and NSST-PCNN image fusion method, and relates to the technical field of multi-source remote sensing image fusion. The method comprises the following steps: performing Gaussian curvature filtering decomposition and LEE filtering decomposition on an SAR image to obtain SAR decomposition features; decomposing the optical image by using non-subsampled shearlet transform (NSST) to obtain an NSST coefficient of the optical image; performing feature extraction on the SAR image after LEE filtering through various structural elements to obtain a multi-scale morphological gradient feature map; and inputting the SAR decomposition features and the NSST coefficient of the optical image into a pulse coupled neural network (PCNN) to obtain a preliminary fusion image, and reconstructing the preliminary fusion image through NSST inverse transformation to obtain a final fusion image. The fused image retains the spectral characteristics of the original optical image, enhances the spatial resolution and edge details, and is suitable for scenes needing to retain high-precision edges, such as road slope deformation monitoring.

The invention provides an industrial anomaly classification method and system based on a multi-modal large language model, and belongs to the technical field of visual detection. The visual pre-screening module adopts a BYOL unsupervised feature extractor and a local block matching technology to calculate the minimum Euclidean distance between an image to be detected and a normal sample so as to generate an abnormal thermodynamic diagram, and efficient preliminary screening is realized; the coordinate set is converted into a binary mask through an abnormal contour marking module, and an accurate single-pixel contour is generated through morphological gradient and SIFT displacement correction; fusing a CLIP visual encoder and a GPT text encoder in a multi-modal decision engine, realizing visual-semantic alignment through cross-modal fusion and a multi-head attention mechanism, and supporting alpha hybrid dynamic rule update; and a dynamic classification executor calculates an abnormal region risk index based on the risk grading model, and generates a grading response action and a structured report. According to the invention, the classification precision is improved, so that the method can adapt to complex and diversified industrial abnormal scenes.

The invention discloses a ceramic microstructure defect intelligent identification system based on morphological image analysis, and relates to the technical field of ceramic material quality detection, and the system comprises a multi-scale morphological feature extraction engine, a defect morphological feature analysis module, a deep semantic enhancement module and a defect intelligent identification decision module. The multi-scale morphological feature extraction engine adopts an adaptive rotation morphological operator group to carry out multi-direction morphological operation, a multi-scale morphological gradient pyramid is generated and morphological feature vectors are extracted, the defect morphological feature analysis module carries out feature coding and classification through a morphological feature encoder to generate defect morphological descriptors, and the defect morphological descriptors are subjected to feature extraction; and the deep semantic enhancement module extracts semantic features under the guidance of morphological feature coding, performs feature fusion, generates operator optimization parameters and feeds the operator optimization parameters back to the feature extraction engine to form closed-loop optimization, and the system realizes deep fusion of morphological features and deep semantic features and adaptive operator optimization.

The invention discloses a muzzle arc energy and mass center spatio-temporal evolution analysis method and system, and relates to the technical field of muzzle arc analysis, and the method comprises the steps: building an electromagnetic track launch muzzle arc simulation experiment platform, and obtaining a muzzle arc image through a high-speed photographing system; an arc contour image is obtained based on a local adaptive threshold segmentation and morphological gradient extraction algorithm, and the integrity and robustness of arc contour extraction are improved; based on a robust centroid tracking algorithm of multi-connected domain energy fusion and time sequence consistency constraint, a final arc centroid is obtained, and stable positioning and tracking of the arc energy centroid in the full evolution stage are achieved; and quantitatively analyzing the time-domain evolution characteristics of the highlighted area of the muzzle arc and the law of the spatial motion trail of the mass center of the highlighted area based on the muzzle arc images at different muzzle speed. An effective means is provided for quantitative characterization of a bore port arc, and an experimental basis and a theoretical reference are provided for optimization of a bore port structure, anti-ablation design, regulation and control of emission working conditions and the like of an electromagnetic emission system.

The application discloses a bolt surface defect detection method and system based on computer vision, comprising: 360-degree image acquisition and preprocessing of the bolt through a multi-camera array arranged in a ring; Fourier transform is used to analyze periodic texture features of the thread, and an adaptive filter is designed to enhance the image; multi-scale morphological operation is used to extract morphological gradient features using a structure element for crack, pit and scratch type defects; an adaptive threshold segmentation method based on local statistical features is used to dynamically adjust the segmentation threshold combined with thread direction information to segment out the defect candidate region; geometric and texture features are extracted for the candidate region, and a random forest classifier is used to identify the defect type, position and severity. The application realizes high-precision automatic detection and classification of various defects on the bolt surface.

The application relates to the technical field of image processing, in particular to a chemical intermediate purity detection method and system based on chromatographic features. The method comprises the following steps: performing baseline correction on a chromatographic detection signal of a chemical intermediate sample to obtain a one-dimensional discrete time sequence, converting the one-dimensional discrete time sequence into a gray-scale chromatographic image, determining a plurality of regions of interest from a morphological gradient image of the gray-scale chromatographic image, respectively determining scale energy attenuation rates and horizontal skewness of the regions of interest, determining confidence index of the regions of interest based on the scale energy attenuation rates and the horizontal skewness, removing the regions of interest with the confidence index lower than a preset threshold, and determining the purity of the chemical intermediate sample based on integral areas of the one-dimensional discrete time sequences corresponding to the retained regions of interest. Through the technical scheme, a more accurate detection result of the purity of the chemical intermediate sample can be obtained.

The invention discloses a prostate cancer image classification method based on axial consistency enhancement and threshold guide selection, and the method comprises the steps: designing an axial consistency context enhancement module ACE, combining strip pooling with axial self-attention, and enhancing the correlation expression between different spatial positions in a feature map through explicit modeling cross-row / cross-column long-distance structural dependence; a threshold guide semantic selection module TGSS is proposed to fuse horizontal, vertical and channel three-view weight information, differential processing of foreground focus enhancement and background tissue reconstruction is realized through hard threshold segmentation, and morphological gradient modeling is performed on a boundary transition region. Compared with the prior art, the method can further improve the precision of the algorithm model on related tasks of prostate cancer image classification.

The invention discloses an unmanned aerial vehicle light show three-dimensional image generation method based on dynamic path planning, and relates to the related technical field of image generation, and the method comprises the steps: constructing a dynamic accessibility tensor space which uniformly describes the space accessibility, the time propulsion and the light trace stability; according to the three-dimensional target morphological gradient, dividing an unmanned aerial vehicle group into three subgroups of configuration generation, light trace enhancement and morphological stability, and setting different path priorities, morphological sensitivities and energy consumption thresholds; and applying a three-dimensional image growth operator to the path field in the dynamic reachability tensor space to form a light trace density field, and performing double-coupling iterative analysis to generate a dynamic path planning result. The technical problem that unmanned aerial vehicle light show in the prior art is difficult to give consideration to space-time continuity, visual stability and cluster cooperation efficiency of three-dimensional light tracks is solved, and the technical effects of efficient, stable and high-quality three-dimensional light show image generation and improvement of planning efficiency, adaptability and three-dimensional image fidelity are achieved.

The invention relates to the technical field of image processing, in particular to a chemical intermediate purity detection method and system based on chromatographic characteristics. The method comprises the following steps: performing baseline correction on a chromatographic detection signal of a chemical intermediate sample to obtain a one-dimensional discrete time sequence, converting the one-dimensional discrete time sequence into a gray-scale chromatographic image, and determining a plurality of regions of interest from a morphological gradient map of the gray-scale chromatographic image; respectively determining the scale energy attenuation rate and the horizontal skewness of the region of interest; determining a confidence index of the region of interest based on the scale energy attenuation rate and the horizontal skewness; and removing the region of interest with the confidence index lower than a preset threshold value, and determining the purity of the chemical intermediate sample based on the integral area of the one-dimensional discrete time sequence corresponding to the reserved region of interest. According to the technical scheme, a more accurate detection result of the purity of the chemical intermediate sample can be obtained.

The application provides a visual detection method, system and edge computing box. The surface image of a target product is subjected to multi-scale edge detection to generate edge feature maps of different scales. A defect area candidate frame of the surface of the target product is determined according to all the edge feature maps, and the gradient variance of pixels in each edge feature map is determined. The positioning loss of the surface defects of the target product is determined according to the defect area candidate frame and all the gradient variances, and the contour shape gradient of each type of defect on the surface of the target product is determined based on the positioning loss and the fluctuation characteristics of the light intensity around the production line. The influence level of the surface defects of the target product on the target product is determined through the contour shape gradient of each type of defect, and a graded alarm signal of the surface defects of the target product is output based on the influence level. The application can realize dynamic grading alarm of products based on the geometric topological characteristics of product defects.

The present application belongs to the technical field of image processing, and particularly relates to a multi-source data driven detection method and system for ecological resilience of a flood storage and detention area, which comprises the following steps: step 1: performing geometric correction and radiation correction on an optical satellite remote sensing image to form a time series basic data set; step 2: a first axis and a second axis of an ecological state matrix correspond to spatial grids of the flood storage and detention area respectively, and a third axis corresponds to time; step 3: mapping the ecological state matrix to a fractal space to form a time coupling matrix; step 4: for each time slice of the time coupling matrix, a fixed size neighborhood is set around each pixel, and a multi-scale morphological gradient graph is subjected to time window segmentation and fractal complexity estimation; and step 5: according to the area and the resilience values of all positions therein, the resilience value of the connected region is calculated. The present application significantly enhances the accuracy and practicability of ecological resilience detection.

The invention belongs to the technical field of asphalt pavement paving defect detection, and particularly discloses a video image-based asphalt pavement paving defect identification method, which comprises the following steps of: continuously acquiring video images of a paving working surface, respectively extracting an initial material area and a motion area, fusing the initial material area and the motion area, and combining morphological optimization and edge contour positioning to identify an asphalt pavement paving defect. According to the method, the paving operation area in the current frame is locked, the boundary positioning precision of the operation area is remarkably improved, a reliable analysis domain is provided for subsequent defect detection, and meanwhile, the paving operation area is identified by constructing a gridding local feature field based on image apparent features in the identified paving operation area and analyzing the consistency of grid unit features in the advancing direction of the paver. Therefore, a suspected defect area is identified, the suspected defect area is subjected to spatial position verification and morphological gradient feature verification, a real defect area is screened out, non-defect visual interference can be inhibited to the maximum extent, and the credibility of defect detection is improved.

The invention relates to the field of machine vision, in particular to a building roof defect detection method and system based on an unmanned aerial vehicle, and the method comprises the steps: obtaining a building roof gray image collected by the unmanned aerial vehicle; calculating morphological gradient maps in multiple directions of the roof grayscale image; calculating the variance of each pixel point in the gradient values in all directions, generating a gradient direction consistency graph, and carrying out the weighted fusion of the morphological gradient maps in all directions based on the gradient direction consistency graph, and generating an edge saliency map; in the neighborhood of each pixel point of the roof grayscale image, according to the Euclidean distance between a neighborhood pixel and a central pixel, performing attenuation weighting on the occurrence frequency of a grayscale pair, and based on the weighted symbiotic probability, calculating a spatial position sensitive entropy, and generating a texture complexity map; further obtaining a defect response diagram; calculating a local discrimination threshold value corresponding to the pixel point; and when the value of the pixel point in the defect response diagram is greater than the corresponding local discrimination threshold, determining that the pixel point is a defect point.

The application discloses a kind of oral prosthetic body abutment relationship image quantitative evaluation systems, belong to oral prosthetic body image processing technical field, including image acquisition and pretreatment module, feature atlas construction module, topological analysis calculation module and quantitative evaluation mapping module.Image acquisition and pretreatment module obtains the three-dimensional scanning image of prosthetic body in situ state, obtains two-dimensional development image by gray mapping and two-dimensional development, separates out highlight contact zone by multi-scale morphological opening and closing reconstruction operation;Feature atlas construction module constructs morphological feature atlas according to the morphological gradient distribution characteristics of highlight contact zone under different scales;Topological analysis calculation module tracks the continuous boundary of abutment contact zone in morphological feature atlas, and calculates the length, width and area quantitative value of contact zone;Quantitative evaluation mapping module maps quantitative value to prosthetic three-dimensional space frequency domain, generates quantitative index for evaluating the closeness of abutment relationship.

The invention discloses a sea ice contour adaptive threshold segmentation and identification method based on a radar image. The method comprises the following steps: step 1, acquiring an image after accumulation of a shipborne X-band radar in continuous N periods; step 2, performing statistics on near sea clutters and far background noise based on the radar image of the first period, and adaptively determining a threshold lower limit of ice-water separation; 3, filtering the background in the radar image of the first period, and adaptively determining the upper threshold of ice-water separation based on morphological gradient statistics; step 4, performing global threshold segmentation and sea ice region extraction on the accumulated image according to the lower limit and the upper limit of the ice-water separation threshold to obtain sea ice contour information; and continuously repeating the steps to obtain sea ice contour information in real time. The method does not need training data, is high in calculation efficiency, can adapt to adaptive threshold segmentation and contour extraction of complex sea conditions, can operate in real time on a common industrial personal computer, and facilitates real-time sea ice recognition of ships in polar regions or seasonal icing sea areas.

The invention provides a rock mass risk grading method based on feature data fusion, and belongs to the technical field of rock mass construction.The rock mass risk grading method comprises the steps that morphological gradient fracture edge enhancement is conducted on rock mass video images, fracture density width trend features are extracted, short-time Fourier transform is conducted on sound wave data, and wave velocity attenuation frequency features are extracted; the multi-source features are subjected to principal component analysis dimensionality reduction and feature decorrelation processing to eliminate redundant information, multi-scale local features are extracted through spatial pyramid pooling, sub-region spatial association is established through a graph convolutional network to construct a global feature vector, and finally accurate classification is achieved based on Euclidean distance matching standard risk levels. The technical problem of insufficient risk grading accuracy caused by incomplete rock mass feature expression of a single data source is solved.

The present application belongs to the technical field of image recognition, and particularly relates to a plant water deficit state recognition method and system based on computer vision, which comprises the following steps: collecting high-definition RGB video stream of plant canopy and extracting key frames; generating a binary mask based on a saturation channel, and extracting a structure image after enhancing a lightness channel; constructing a texture index representing leaf surface micro-roughness through multi-scale morphological gradient operation and frequency domain response reverse weighting logic; constructing a deformation index representing edge macro-deformation degree by using the derivative of contour curvature with respect to arc length; introducing a cusp catastrophe model to map the texture index and the deformation index into a splitting factor and a normal factor, and calculating a water deficit catastrophe potential value; and recognizing the water deficit state according to the positive and negative potential values. The present application can effectively distinguish environmental interference and capture the critical characteristics of the transition of plants from a physiological steady state to a wilting unstable state, thereby improving the accuracy of plant water deficit state recognition.

The invention belongs to the technical field of image recognition, and particularly relates to a plant water shortage state recognition method and system based on computer vision, and the method comprises the steps: collecting a high-definition RGB video stream of a plant canopy, and extracting a key frame; generating a binary mask based on the saturation channel, and extracting a structure image after enhancing the brightness channel; constructing a texture index for representing the microcosmic roughness of the blade surface through multi-scale morphological gradient operation and frequency domain response reverse weighting logic; using the derivative of the contour curvature relative to the arc length to construct a deformation index representing the edge microscopic deformation degree; a sharp point mutation model is introduced to map the texture index and the deformation index into a splitting factor and a regular factor, and a water shortage mutation potential energy value is calculated; and identifying the water shortage state according to the potential energy value. According to the method, environmental interference can be effectively distinguished, critical characteristics of transition from a physiological steady state to a withering and destabilizing state of the plant can be captured, and the accuracy of plant water shortage state identification is improved.

This invention relates to the field of machine vision, and in particular to a method and system for detecting defects on building roofs based on unmanned aerial vehicles (UAVs). The method involves acquiring grayscale images of building roofs captured by the UAV; calculating morphological gradient maps in multiple directions of the grayscale image; calculating the variance of each pixel across all gradient values ​​to generate a gradient direction consistency map; and weighted fusion of all morphological gradient maps based on the gradient direction consistency map to generate an edge saliency map. Within the neighborhood of each pixel in the grayscale image, the frequency of grayscale pairs is attenuated and weighted according to the Euclidean distance between neighboring pixels and the center pixel, and spatial location-sensitive entropy is calculated based on the weighted co-occurrence probability to generate a texture complexity map. Further, a defect response map is obtained; a local discrimination threshold corresponding to each pixel is calculated; and when the value of a pixel in the defect response map is greater than the corresponding local discrimination threshold, the pixel is determined to be a defect point.

The application discloses a time-space evolution analysis method and system for muzzle electric arc energy and centroid, relates to the technical field of muzzle electric arc analysis, and comprises the following steps: building an electromagnetic track launching muzzle electric arc simulation experiment platform, acquiring a muzzle electric arc image by using a high-speed photography system, obtaining an electric arc contour image based on a local adaptive threshold segmentation and a morphological gradient extraction algorithm, and improving the integrity and robustness of electric arc contour extraction; based on a robust centroid tracking algorithm of multi-connected energy fusion and time sequence consistency constraint, obtaining a final electric arc centroid, realizing stable positioning and tracking of the electric arc energy centroid in the whole evolution stage; and based on the muzzle electric arc images under different muzzle velocities, quantitatively analyzing the time-domain evolution characteristics of the high-light area of the muzzle electric arc and the law of the spatial motion trajectory of the centroid. The application provides an effective means for quantitative characterization of the muzzle electric arc, and provides experimental basis and theoretical reference for muzzle structure optimization, anti-ablation design and launch working condition regulation of an electromagnetic launching system.

The present application belongs to the technical field of image processing, and particularly relates to a composite board welding quality detection method and system based on image processing, which comprises the following steps: performing morphological gradient and threshold segmentation preprocessing on the collected welding image to obtain a welding area image; calculating a defect spread index for any pixel point in the welding area image, and determining an optimal frequency in combination with a frequency boundary and the maximum and minimum values of the defect spread index; correcting a basic bandwidth by using the fluctuation of the optimal frequency of the neighborhood pixel points to determine a dynamic bandwidth; constructing a pixel-by-pixel adaptive Log-Gabor filter bank by using the optimal frequency and the dynamic bandwidth, extracting a defect energy distribution map, and identifying defects. The present application realizes adaptive adjustment of filter parameters through physical width perception, solves the problem that fixed parameters cannot take into account different types of defects, and improves the accuracy of quality detection.

The invention relates to the technical field of medical image analysis, in particular to an interpretable staging prediction method for a small sample colorectal cancer image, which comprises the following steps of: performing double-domain collaborative variational bias field correction on an obtained original tissue pathology image to eliminate imaging artifacts; and carrying out multi-scale morphological gradient texture analysis on the corrected image to generate a structure consistency adjustment coefficient matrix, and carrying out differentiation enhancement on an image region according to the matrix to obtain a vision enhanced image. And inputting the enhanced image into a pre-trained feature decoupling network to obtain an image feature coding vector. And inputting the vector into a small sample learning framework, generating a feature expression adapted to a task in combination with a support set sample, and outputting a cancer staging prediction result and a corresponding visual interpretation graph by using an interpretable classifier. According to the method, the accuracy of colorectal cancer staging prediction and the interpretability of model decision under the small sample condition can be effectively improved.

The application provides a cervical cell nucleus multi-scale accurate segmentation method based on an HSV channel difference feature, the method first converts an image to an HSV space and extracts a saturation S and a brightness V channel; then, adaptive enhanced difference feature channels are constructed by fusing a weighted difference value and a ratio relationship, the discrimination of the cell nucleus and the background is effectively improved, and the method has strong robustness to staining and illumination changes; subsequently, cell density sensing adaptive CLAHE and gradient guided adaptive median filtering are used for multi-scale enhancement and denoising, interference is suppressed while details are retained; in the segmentation stage, a hierarchical strategy of a global threshold value, a local adaptive threshold value and a morphological gradient enhancement is adopted, and adaptive morphological post-processing and screening are combined, so that accurate and complete segmentation of the cell nucleus in a complex scene is finally realized; the whole process of the application is based on clear image processing principles, does not require large-scale training data, has high calculation efficiency and is easy to integrate into an existing pathological information system.

This invention relates to the field of image data processing technology, and more particularly to an intelligent correlation analysis method for the microstructure and permeability performance of geopolymers based on image analysis. First, based on the acquired original image, grayscale normalization is performed to obtain a normalized image. Based on the normalized image, a structural response gradient enhancement algorithm is introduced to obtain an enhanced image. Based on the enhanced image, a coupled morphology-gradient fusion analysis method is introduced to obtain the structural response energy value. Then, based on the structural response energy value, the enhanced image is initially segmented to obtain preliminary segmentation results. Based on the preliminary segmentation results, effective structural regions are obtained. Based on the effective structural regions, structural parameters are extracted. Finally, based on the structural parameters, an energy level mapping prediction model is designed to obtain the predicted permeability coefficient. This method solves the technical problems of unclear image structural boundary identification, incomplete structural feature extraction, and the lack of an effective coupling mechanism between parameters and performance.

The present application relates to the technical field of medical image analysis, in particular to an interpretable staging prediction method for small sample colorectal cancer images, comprising: performing double-domain collaborative variational bias field correction on the obtained original histopathological images to eliminate imaging artifacts; performing multi-scale morphological gradient texture analysis on the corrected images to generate a structure consistency adjustment coefficient matrix, and performing differential enhancement of the image regions according to the matrix to obtain a visually enhanced image; inputting the enhanced image into a pre-trained feature decoupling network to obtain an image feature encoding vector; inputting the vector into a small sample learning framework to generate a task-adaptive feature expression combined with support set samples, and outputting a cancer staging prediction result and a corresponding visual interpretation diagram by using an interpretable classifier; and the present application can effectively improve the accuracy of colorectal cancer staging prediction and the interpretability of model decision under small sample conditions.

The invention relates to the technical field of industrial detection, in particular to a VCSEL epitaxial wafer warping degree detection method, which comprises the steps of acquiring a three-dimensional height field of a to-be-detected VCSEL epitaxial wafer surface; based on the original three-dimensional height field, extracting a macroscopic warping field by solving a variational optimization model; subtracting the original height field from the macroscopic warping field to obtain a high-frequency residual field; the morphological gradient of each pixel of the high-frequency residual field in multiple directions is calculated, and a texture index is solved according to the morphological gradient; the pixels with the texture indexes higher than a threshold value are judged as texture pixels, and the pixels with the texture indexes lower than or equal to the threshold value are judged as noise pixels; performing local mean filtering on the position corresponding to the noise pixel in the macroscopic warpage field to obtain a corrected warpage field; and calculating the warping degree based on the corrected warping field, and if the warping degree is within a set error range, determining that the to-be-detected VCSEL epitaxial wafer is qualified. The problem that an existing algorithm is not high in detection accuracy is solved.

The application provides a method and device for diagenetic strength analysis of oil and gas reservoir rocks, which comprises the following steps: determining the common boundary of each adjacent mineral particle in a mineral binary image obtained based on a preset common boundary extraction algorithm, to obtain a particle boundary extraction result image; determining the maximum circumscribed rectangle of each mineral particle, and calculating the total equivalent particle diameter of all mineral particles; performing image processing based on a preset image processing algorithm, to obtain a mineral morphological gradient image and a mineral watershed line image; performing logical AND operation, to obtain a logical operation result image; performing color space transformation, to obtain a mineral particle contact image; calculating the total point contact length and the total line contact length according to the mineral particle contact image; and obtaining a diagenetic strength coefficient according to the total equivalent particle diameter of all mineral particles, the total point contact length and the total line contact length. The result is accurate because the mineral automatic analysis image is used for automatic quantitative calculation.

The invention discloses a tumor immune environment image enhancement and intelligent recognition method based on computer vision, and relates to the technical field of artificial intelligence, and the method comprises the steps: S1, processing an obtained tumor tissue pathological section sample through a standard pathological processing flow; s2, generating a cell instance segmentation mask in combination with a watershed algorithm constrained by a multi-scale morphological gradient and a local adaptive threshold; s3, dynamically constructing a cell neighborhood graph according to the centroid distance and the area, and calculating corresponding topological features; s4, constructing a computer vision neural network model based on the cell neighborhood graph and the corresponding topological features; s5, obtaining the corresponding immune phenotype classification through the constructed computer vision neural network model, solving the key pain points in the prior art, forming an end-to-end intelligent recognition scheme, and greatly improving the accuracy and robustness of tumor immune environment image recognition.

The invention belongs to the technical field of image processing, and particularly relates to a composite board welding quality detection method and system based on image processing, and the method comprises the steps: carrying out the morphological gradient and threshold segmentation preprocessing of a collected welding image, and obtaining a welding region image; for any pixel point in the welding area image, a defect broadening index is calculated, and the optimal frequency is determined in combination with the frequency boundary and the maximum and minimum value of the defect broadening index; the basic bandwidth is corrected according to the fluctuation condition of the optimal frequency of the neighborhood pixel points, and the dynamic bandwidth is determined; and constructing a pixel-by-pixel self-adaptive Log-Gabor filter bank by utilizing the optimal frequency and the dynamic bandwidth, extracting a defect energy distribution diagram and identifying defects. According to the invention, adaptive adjustment of filter parameters is realized through physical width sensing, the problem that different types of defects cannot be considered by fixed parameters is solved, and the accuracy of quality detection is improved.

The application relates to the technical field of image analysis, in particular to a highway green passing vehicle image feature extraction and rapid screening system; the highway green passing vehicle image feature extraction and rapid screening system comprises an image acquisition unit, an image extraction unit and an image screening unit; the image acquisition, extraction and screening process are automated, the detection efficiency is greatly improved, the vehicle waiting time is greatly reduced, the smooth passing of the highway is ensured, a method based on morphological gradient and distance transformation is adopted, the complex scene that goods in a cargo box of a highway green passing vehicle are randomly stacked and mutually shielded can be coped with, adjacent and feature-similar sub-regions are combined based on feature similarity in an image fusion process, the redundant information in the image is reduced, in addition, goods not meeting the green passing standard can be screened out, and the subjectivity and uncertainty of manual judgment are avoided.