104 results about "Distance transform" patented technology

The invention provides a stone gradation automatic detection method based on image processing, belongs to the technical field of image processing, and designs a two-dimensional convolution kernel capable of enhancing features according to the features that small-particle stones are bright in center, dark in edge and approximate to a circle by acquiring a gray image and a background image of a stone field through a video stream. Convolution operation is carried out on the image to improve the contrast ratio of the stone and the background, then an accurate binarized image is obtained through double-threshold segmentation and background difference processing, in order to further separate the adhered particles, the particle center is positioned by adopting distance transformation, and effective segmentation is carried out by combining a watershed algorithm. And according to the extracted particle contour, geometric parameters are calculated and the mass is estimated, so that a stone grading curve for evaluating the filling quality is automatically generated. According to the invention, rapid and non-contact automatic analysis of rockfill material grading is realized.

The invention belongs to the technical field of image processing, and particularly relates to an inonotus obliquus raw material granularity and uniformity detection method based on image analysis, and the method comprises the steps: obtaining an inonotus obliquus raw material gray scale image, and carrying out the preprocessing and binaryzation to obtain an adhesion body candidate object; screening particle core seed points through distance transformation in combination with an adaptive threshold strategy based on image statistical characteristics; calculating the attribution probability of a pixel to a seed point through a geodesic distance, achieving the soft segmentation of an adhesion region through a negative index model, and extracting a non-destructive segmentation line to obtain independent particles; and calculating morphological parameters of the particles, and obtaining a global uniformity index by combining the size dispersion and the shape regularity. According to the method, the accuracy of adhesion segmentation of irregular inonotus obliquus raw materials with complex textures is improved, and the robustness and accuracy of granularity detection are improved.

The invention provides a pathological image processing method and system, and the method comprises the steps: carrying out the color space decoupling of an original pathological image, separating a plurality of staining components corresponding to the optical absorption characteristics of a staining agent, carrying out the space alignment and channel superposition of the staining components, and generating a multi-channel structure diagram containing the structural characteristics of cells; inputting the multi-channel structure diagram into an adversarial network for color distribution correction to obtain a standardized red-green-blue three-primary color image; performing morphological optimization on the standardized red-green-blue three-primary color image by using an optimization process to obtain a mask, and performing Euclidean distance transformation on the mask to generate a distance map; performing channel splicing on the mask, the distance map and the standardized red-green-blue three-primary-color image, inputting a multi-task deep learning network, and outputting a probability thermodynamic diagram and a boundary probability diagram of a cell mass center; and cell segmentation is realized based on the probability thermodynamic diagram and the boundary probability diagram by utilizing a segmentation process, so that the problem of structural deformation of the image is effectively avoided.

The invention discloses a part machining surface defect visual detection method and system based on image enhancement and a binarization algorithm, and belongs to the field of machine manufacturing. The problem of how to realize high-precision and low-false-detection defect detection by optimizing an image processing flow without relying on big data training is solved, and meanwhile, the requirements of an industrial scene on real-time performance and deployment cost are met. The method comprises the following steps: inputting a part surface image and a mask image, and carrying out corrosion operation to generate a safety detection area; carrying out bottom cap transformation by adopting longitudinal and transverse morphological nucleuses, and separating scratches and noise; extracting transverse and longitudinal gradients by using a Sobel operator, and generating a multidirectional gradient feature map; performing dynamic threshold segmentation to generate a binary image, and connecting fracture scratches through closed operation; and screening longitudinal and transverse scratch contours according to the length-width ratio, carrying out distance transformation and minimum inscribed circle radius calculation, and judging the region with the radius smaller than a threshold value as a real defect. The method is mainly applied to the defect detection field.

The invention relates to the technical field of image processing, in particular to a flower disease and insect pest monitoring method based on machine vision, and the method comprises the steps: obtaining a candidate pseudo boundary set in a gray level image of a to-be-monitored flower leaf, and obtaining a gray level distribution feature of a pixel point in a local analysis region of each candidate pseudo boundary; obtaining a vein feature index of each candidate pseudo-boundary according to the linear structure feature of each candidate pseudo-boundary and the linear structure feature of each candidate pseudo-boundary; according to the vein feature index and the direction feature of each candidate pseudo boundary, screening in the candidate pseudo boundary set to obtain at least one pseudo boundary; in the process of carrying out region segmentation on the gray level image by using the watershed algorithm, the initial distance of each pixel point after distance transformation is obtained, adaptive distance compensation is carried out on the initial distance of each pixel point according to the position relation between each pixel point and each pseudo boundary, the compensation distance is obtained, complete segmentation of a scab region is realized, and the scab image segmentation efficiency is improved. And a reliable quantitative basis is provided for flower disease and pest monitoring and prevention decision making.

A method of performing 3D vessel tree reconstruction includes providing segmented binary angiography images, applying a distance transform to the images, and generating distance transformed binary angiography images. The set of distance transformed binary angiography images are provided to a trained 3D vessel reconstruction machine learning model capable of reconstructing 3D vessels. The 3D vessel tree reconstruction machine learning model includes a multi-stage convolutional neural network comprising a multi-stage architecture with (i) a vessel centerline stage, and (ii) a radius reconstruction stage. Resultant 3D reconstructed vessel trees may be used in performing clinical assessment of coronary vessel health, and occlusion.

The invention discloses a fry counting method based on image processing, and relates to the technical field of image processing. Comprising the steps of image acquisition, fry image target segmentation, image region division, fry target identification, preliminary fry counting, overlapped fry identification and final fry counting. Median filtering denoising is adopted, and the image quality is guaranteed; a threshold value is determined in combination with global and local maximum between-cluster variance methods, expansion and corrosion operations are assisted, fry and a background are accurately distinguished, and the processing efficiency and segmentation precision are improved through a sub-region division strategy; through connected region marking, multi-feature fusion screening and adjacent region splicing de-weighting, single fry is effectively identified, repeated counting is avoided, distance transformation and watershed algorithm accurate segmentation are adopted for overlapped fry, then a correction coefficient optimization result is introduced, and finally high efficiency and high precision of fry counting are achieved. And powerful support is provided for intelligent counting management of fishery breeding.

The invention discloses a bridging defect visual detection method for a solder paste detection instrument, a storage medium and equipment, and belongs to the technical field of surface mounting defect detection. In order to solve the problems that an existing bridging defect visual detection algorithm in a solder paste detection instrument is high in complexity and the detection accuracy needs to be improved, a solder paste area binary image is obtained based on a binary image and a solder paste area binary image, and then a solder paste printing area in the solder paste detection area binary image is removed; obtaining a binary image of the solder paste overflowing the solder paste printing area; taking a pixel of a solder paste printing area in the solder paste detection area image as a target, taking other pixels as a background, and adopting a chessboard distance to measure the length of the solder paste overflowing from the solder paste printing area; obtaining the distance between each pixel in the overflowing solder paste and the solder paste printing area through the binary image and the distance transformation image of the solder paste overflowing from the solder paste printing area, searching the maximum value and the minimum value, taking the difference between the maximum value and the minimum value as the distance of the solder paste overflowing from the solder paste printing area, and judging that the bridging defect exists if the difference exceeds a length threshold value.

The invention mainly relates to the field of digital image photogrammetry. In order to solve the problems that road crack detection lacks end-to-end quantitative analysis capability and crack extraction results are fragmented, the invention provides a road crack extraction and grading method based on topological structure constraints, and the method comprises the following steps: crack end point extraction and width calculation, skeleton of a crack center line, identification of a connected region and end points, and classification of the end-to-end quantitative analysis capability of the end-to-end quantitative analysis capability of the end-to-end quantitative analysis capability of the end-to-end quantitative analysis capability. The crack width corresponding to each center point is calculated through Euclidean distance transformation; potential breakpoint pairs are screened according to attribution, distance and direction constraints, then a topological connection rule is constructed, self-adaptive repair of the fracture is achieved, and the geometric integrity of the fracture is recovered; and performing quantitative analysis on the repaired cracks, accurately calculating the length, the average width, the maximum width, the area and the geographic coordinates of each crack, and performing danger grade division on each crack according to a preset threshold value to form structured disease data and a grading result which can be directly used for road risk assessment.

The invention discloses a narrow road passing path planning method suitable for an automatic driving sweeping vehicle. The narrow road passing path planning method is particularly suitable for a complex unstructured environment in an open space. The method is based on dynamic programming mapping expansion, and comprises the following steps: firstly, constructing a grid map containing obstacles, generating a global cost graph by using breadth-first search, and constructing a Voronoi graph through obstacle distance transformation; on this basis, a heuristic path search algorithm is used to generate a preliminary path, and a potential narrow lane area in the path is extracted. And then, kinematics compensation is performed on the path by adopting an expansion mechanism based on a bicycle model, the expansion resolution is improved in a narrow lane area, and path search is guided in combination with a composite cost function. And finally, an optimal path meeting the requirements of safety, smoothness and traffic efficiency is obtained. The method improves the path planning robustness and real-time performance in a narrow channel environment, and is suitable for an automatic driving system in a complex urban environment and a semi-closed space.

The invention relates to the technical field of intelligent detection, in particular to a method and system for detecting the cleanliness of automobile parts to be painted. The method comprises the following steps: acquiring a grayscale image of a to-be-painted automobile part; carrying out distance transformation on the preprocessed grayscale image, and calculating the saliency height of a local maximum point to screen and obtain a mark point; generating a gradient potential field map based on the gradient magnitude and the gradient confidence; generating a shape potential field map based on the distance transformation image; taking the mark point as a starting point, performing regional flooding on a fusion potential field graph obtained by weighting the gradient potential field graph and the shape potential field graph, and forming a final segmentation boundary; and finally, identifying and calculating a pollutant region based on the segmentation boundary to determine the cleanliness. According to the method, under-segmentation caused by pollutant adhesion is effectively avoided, and the accuracy of pollutant segmentation and the robustness of a detection result are remarkably improved.

The application provides a skeleton extraction method and system and a storage medium, wherein the method steps comprise: step S100, training a data set, obtaining a polygon contour and a skeleton key point of an object in an original image; taking the polygon contour as a domain constraint, triggering wave propagation connection between the skeleton key points, obtaining a preliminary skeleton, and performing an inflation operation to obtain a coarse-grained skeleton; and step S200, establishing a W-shaped network, taking a distance conversion grayscale image of the original image as an input of a first encoder, taking the coarse-grained skeleton as an input of a second encoder, taking an original real skeleton as a label, and adopting a hybrid loss function with a weight value decreasing layer by layer to complete training, so as to perform skeleton extraction. In this way, the problem of skeleton fracture caused by large shape and scale changes of different parts of the object in the process of extracting the skeleton is solved.

A method of performing 3D vessel tree reconstruction includes providing segmented binary angiography images, applying a distance transform to the images, and generating distance transformed binary angiography images. The set of distance transformed binary angiography images are provided to a trained 3D vessel reconstruction machine learning model capable of reconstructing 3D vessels. The 3D vessel tree reconstruction machine learning model includes a multi-stage convolutional neural network comprising a multi-stage architecture with (i) a vessel centerline stage, and (ii) a radius reconstruction stage. Resultant 3D reconstructed vessel trees may be used in performing clinical assessment of coronary vessel health, and occlusion.

This invention discloses a method for calculating the tortuosity of coal-uranium reservoirs based on the maximum inscribed circle and symmetry constraints, comprising the following steps: acquiring a three-dimensional binary image of the internal structure of the coal-uranium reservoir; extracting inlet and outlet end face slices respectively, and marking the pore connectivity domains in each slice; for each pore connectivity domain, determining the center and radius of the maximum inscribed circle through Euclidean distance transformation, and filling the maximum inscribed circle in descending order of radius; setting a radius threshold, and determining the center of the filled circle that satisfies the threshold as the channel endpoint; applying the same connectivity domain marking rules, filling rules, radius threshold, and non-overlapping constraints to the inlet and outlet end faces to obtain the set of starting points and the set of ending points; searching for the optimal connectivity path from each starting point to each ending point in the three-dimensional pore space, and recording the actual length of each path; calculating the tortuosity based on the actual length of each path and the seepage length, and thus obtaining the overall tortuosity.

This invention discloses a method and system for candidate optimization, continuous repair, and quantization of crack images in complex backgrounds, belonging to the field of computer vision and digital image processing technology. To address the problems of crack recognition in complex backgrounds being susceptible to false target interference, discontinuous cracks, and difficulty in stable quantification of engineering parameters, this invention obtains the crack response probability map and determines a linked dual threshold. It retains low-threshold regions connected to the high-threshold region to generate initial candidate regions. The ratio of skeleton length to area is calculated as a thinness index for screening. Skeleton endpoints are extracted, and bridging pixel paths are generated to achieve local repair when the endpoints of different connected domains meet preset multi-condition constraints. The repaired region is then skeletonized to construct a graph structure. The number of original skeleton segments, the total number of cracks, and the total length are statistically analyzed. Width statistics and direction parameters are calculated by sampling along the skeleton using distance transformation. This effectively suppresses false targets in complex backgrounds, accurately connects discontinuous cracks, and outputs stable, accurate, and interpretable engineering parameters.

The invention discloses a moving abrasive particle detection and tracking method based on fusion of a Gaussian mixture model (GMM) and a watershed algorithm, which is characterized in that on the basis of acquiring abrasive particle moving targets based on the Gaussian mixture model (GMM), each extracted target is segmented by using the watershed algorithm based on distance transformation, so that the adhered abrasive particle targets can be accurately detected, and the detection accuracy is improved. Algorithm verification is carried out on abrasive particle videos in different stages, the detection accuracy of the abrasive particle adhesion target can reach 98.86%, the number, size and other characteristics of abrasive particles can be accurately extracted, and abrasion faults can be analyzed easily. In addition, on the basis of accurate detection of the adhered abrasive particle target, the SORT algorithm is further applied to track the multi-target abrasive particles in real time, and the result shows that the tracking effect of the adhered abrasive particle target is good.

The invention relates to the technical field of computer vision and digital image processing, and discloses an adhesion object image segmentation method and system based on an improved watershed algorithm, a computer readable storage medium and a computer program product. Reconstructing and normalizing the distance map through morphology; extracting a foreground mark from the normalized distance map; based on the foreground mark and the background mark of the binary image, performing mark-controlled watershed transformation on the modified gradient image to obtain an initial segmentation result; and carrying out contour extraction and shape fitting on each region in the initial segmentation result, and outputting a final segmentation contour and corresponding morphological parameters. According to the method, robust and accurate segmentation of an adhesion object is realized through optimized preprocessing and mark extraction strategies in combination with shape fitting of post-processing, over-segmentation is effectively inhibited, and a smooth contour better conforming to a real form is obtained.

The application provides a cloud image efficient synthesis method, belonging to the field of image processing, comprising the following steps: calculating the edge distance transform of the mask of the foreground image to generate a multi-level boundary information graph; converting the background image and the foreground image to the Lab color space, and inputting the Lab channel, the mask and the multi-level boundary information graph into a generator network to output a preliminary fusion image in the Lab color space; using a brightness discriminator and a chroma discriminator to respectively judge the local texture consistency of the preliminary fusion image and a real fusion image in the L channel, and the global color harmony of the preliminary fusion image and the real fusion image in the a and b channels; performing the adversarial training of the generator network and the discriminator based on a total loss function until the network model converges, and using the converged generator network to generate a final fusion image. The application can guarantee the high realism and consistency of the fusion image in the details and the overall color tone.

This invention relates to the field of image processing technology, and more particularly to a machine vision-based method for monitoring flower diseases and pests. The method acquires a set of candidate pseudo-boundaries in a grayscale image of the leaves of the flower to be monitored. Based on the grayscale distribution characteristics of pixels in the local analysis region of each candidate pseudo-boundary, and the linear structure characteristics of each candidate pseudo-boundary, it obtains the leaf vein feature index of each candidate pseudo-boundary. Based on the leaf vein feature index and direction characteristics of each candidate pseudo-boundary, at least one pseudo-boundary is selected from the candidate pseudo-boundary set. During the process of using the watershed algorithm to segment the grayscale image, the initial distance of each pixel after distance transformation is obtained. Based on the positional relationship between each pixel and each pseudo-boundary, adaptive distance compensation is performed on the initial distance of each pixel to obtain the compensated distance, thereby achieving complete segmentation of the lesion area and providing a reliable quantitative basis for flower disease and pest monitoring and control decisions.

Several examples describe how to perform localization using camera-based maps augmented with sensor reflection information for autonomous and / or semi-autonomous systems and applications. For example, for a machine, an image-based map can be used to determine one or more locations associated with one or more landmarks within the environment, and also to determine that these landmarks correspond to sensor reflections. Sensor data generated by the machine can then be analyzed with respect to these locations to determine the machine's location within the environment.As described here, various techniques can be used to locate the machine, such as using one or more distance transformation areas around the one or more landmarks and / or using cost values ​​based at least on the analysis of the one or more distance transformation areas in relation to points represented by the secondary data.

The invention provides an efficient cloud image synthesis method, which belongs to the field of image processing and comprises the following steps of: calculating edge distance transformation for a mask of a foreground image to generate a multi-level boundary information graph; converting the background image and the foreground image into a Lab color space, inputting the Lab channel, the mask and the multi-level boundary information graph into a generator network together, and outputting a preliminary fusion image in the Lab color space; a brightness discriminator and a chrominance discriminator are adopted to discriminate the local texture consistency of the preliminary fusion image and the real fusion image in the L channel and the global color harmony of the preliminary fusion image and the real fusion image in the a channel and the b channel respectively; and performing adversarial training on the generator network and the discriminator based on the total loss function until the network model converges, and generating a final fusion image by using the converged generator network. According to the method, the high reality sense and consistency of the fused image in detail texture and overall hue can be ensured.

The invention discloses a pest prevention method, system and device based on computer vision, and the method comprises the steps: obtaining a plant X-ray image, carrying out the texture extraction, obtaining a plant texture image, and obtaining a pest mask image through gradient analysis; identifying the specific position information of the pests of the specific type, and obtaining a conversion mask image of the pests of the specific type through distance conversion based on the specific position information and the pest mask image; obtaining an intersection point of the image boundary of the transformation mask image of the specific type of pests and the pest area, taking the intersection point as a current guide point, obtaining a next guide point through a neighborhood pixel point set of the current guide point, and carrying out iterative analysis to obtain a next guide point set; calculating the distance between all the guide points, obtaining the length of the pest body, analyzing the age of the pest, determining the age of the pest, and preventing the pest according to the age of the pest. By analyzing the X-ray image of the plant, the length of the pest body is obtained, and pest prevention is achieved through pest age analysis.

This invention discloses a method for extrinsic parameter calibration of a LiDAR and camera based on directional equalization, comprising the following steps: extracting edge feature maps from the camera image and performing Euclidean distance transformation on the edge feature maps to obtain the range field and its gradient; performing voxel downsampling on the LiDAR point cloud and using a region growing algorithm to segment the principal plane from the downsampled LiDAR point cloud; solving for the intersection lines between adjacent principal planes pairwise to obtain LiDAR edge line features; transforming the LiDAR edge line feature points to the camera coordinate system and projecting them onto the image plane, and performing bilinear sampling of the range field and its gradient at the projected pixel positions; constructing a global orientation histogram and calculating the directional equalization weights; constructing a joint objective function that fuses the range field residuals and the directional equalization weights; and iteratively optimizing the joint objective function to solve for the optimal extrinsic parameters between the LiDAR and the camera. The advantage of this invention is that it improves the stability of extrinsic parameter estimation.

The application discloses a segmental straightening method and terminal equipment based on computer vision. The method comprises the following steps: installing a camera sensor at a position between a rack and a straightening roll in a horizontal direction; performing joint calibration of all cameras to facilitate determination of relative positions of two-dimensional information; recording two-dimensional image information of a workpiece to be straightened and the straightening roll by the camera, and simultaneously performing model training preparation; performing semantic segmentation processing on the workpiece by an image recognition hub, and obtaining a bending curve by a distance transformation method or a skeleton thinning algorithm; according to bending information of the workpiece, performing discrete curvature calculation on the workpiece by a point taking method or a spline curve fitting method, and then performing segmental processing on the workpiece according to a point detection method and a curvature threshold method; and according to material thickness, yield strength and other parameters, determining optimal roll spacing and a press-down amount of a current segment, and performing real-time full-automatic closed-loop control on a steel straightening machine by real-time control on a press-down system and a variable roll spacing system of the straightening machine.

The invention provides a high-precision map constraint positioning method and device based on non-explicit data association, and the method is characterized in that the method comprises the following steps: S1, carrying out the semantic segmentation and clustering of a camera picture, and extracting a semantic object; s2, according to the first semantic object, calculating to obtain a camera ground clearance; s3, according to the prior height of the camera, the three-dimensional world coordinates of the camera and the ground clearance of the camera, an initial pose is obtained through calculation; s4, performing distance transformation on the segmentation masks of the second semantic object and the third semantic object to obtain a distance image; s5, constructing an image pyramid according to the distance image, and taking the initial pose as an iterative pose; s6, updating the iterative pose according to the image pyramid and the high-precision map; and S7, repeatedly executing the step S6 until a preset iteration termination condition is reached, and taking the updated iteration pose as a pose result. In a word, the method can obtain an accurate pose result.

In various examples, performing localization using camera-based maps augmented with sensor reflection information for autonomous and / or semi-autonomous systems and applications is described herein. For instance, and for a machine, an image-based map may be used to determine one or more locations associated with one or more landmarks located within the environment and also determine that the landmark(s) is associated with sensor reflections. Sensor data generated using the machine may then be analyzed with respect to the location(s) associated with the landmark(s) within the environment in order to determine a location of the machine within the environment. As described herein, various techniques may be used to localize the machine, such as using one or more distance transform areas around the landmark(s) and / or using costs determined based at least on analyzing the distance transform area(s) with respect to points represented by the secondary data.

The invention discloses an early pregnancy ultrasonic image gestational sac edge feature segmentation method and system, and the method comprises the steps: obtaining original ultrasonic image data, carrying out the image data preprocessing, and obtaining the preprocessed ultrasonic image data; constructing an improved segmentation network to perform automatic segmentation processing on the preprocessed ultrasonic image data to obtain an ultrasonic image gestational sac segmentation result; and performing target edge expansion processing on the ultrasonic image gestational sac segmentation result based on distance transformation and a morphology method to obtain an ultrasonic image gestational sac edge feature segmentation result. The method can improve the accommodation capability of the segmented region for the target structure, and achieves the improvement of the segmentation precision of the ultrasonic image gestational sac. The method and system for segmenting the gestational sac edge features of the early pregnancy ultrasonic image can be widely applied to the technical field of automatic segmentation of edge effective areas.

The invention provides a metallographic image grain size determination method based on artificial intelligence. The method comprises the following steps: step 1, image acquisition; 2, preprocessing the image to obtain a binary image; 3, morphological operation: performing morphological operation on the binarized image; 4, distance calculation is carried out, and a distance map is generated; based on a distance transformation result, a foreground area is determined by adopting an adaptive threshold value method, and an adaptive threshold value is a preset proportion of a distance transformation maximum value; 5, segmentation: performing connected domain marking on the foreground region, and applying a watershed algorithm to realize grain accurate segmentation; step 6, boundary reconstruction: based on a segmentation result, reconstructing the grain boundary by adopting a mode of combining skeleton extraction and Voronoi fitting; 7, calculating the grain size; and 8, outputting a result. According to the method, self-adaptive threshold optimization of the metallographic image is realized, and three-level architecture design is realized, so that the accuracy can be improved, and the automation and working efficiency can be improved.