136 results about "Corner point detection" patented technology

The invention relates to the technical field of image analysis, in particular to an automatic marking robot calibration method based on visual guidance, which comprises the following steps of: establishing an image set comprising different resolution levels based on operation site image data acquired by a marking robot, and performing corner detection and straight line segment detection on each level image in parallel. According to the method, through a parallel detection mechanism of a multi-resolution hierarchical image set, angular point and straight line segment features under different scales are synchronously extracted, and a multi-scale feature point set with high robustness is constructed in combination with cross-hierarchical coordinate stability measurement and response intensity quantification. And performing dynamic screening and grouping association on the feature points based on a preset geometric constraint condition, eliminating noise interference and false detection features, and generating a candidate calibration structure set with spatial consistency. Weighted contribution value fitting is adopted, cross-scale stability and detection confidence of feature points are integrated, and geometric accuracy and anti-interference capability of calibration reference point coordinates are improved.

The invention discloses a projection single attitude calibration method, system and device and a medium, and relates to the technical field of computer vision and projection interaction.The method comprises the steps that a plurality of calibration patterns are generated and projected to a calibration plane, gray level images are collected, corner point detection and sub-pixel refinement processing are carried out, and a projection single attitude calibration result is obtained. The method comprises the following steps: acquiring an angular point corresponding relation between a projector image and a camera image, carrying out iterative solution on a projection matrix of a projector and a projection matrix of a camera, separating an internal parameter matrix and an external parameter matrix, calculating a basic matrix and an essential matrix, and decomposing to obtain a rotation matrix and a translation vector of the camera relative to the projector; camera imaging distortion is estimated, a distortion correction coefficient is obtained, and distortion correction is carried out; and counting a re-projection error generated in the correction process, and adjusting parameters of the projector and the camera until the error meets a preset precision requirement. Projector parameter calculation is completed through projection single posture calibration, posture adjustment is not needed in the calibration process, the automation degree is improved, and meanwhile manual intervention is reduced.

The invention relates to the technical field of image stitching, and discloses a video transmission high-definition image intelligent stitching method and system. The system comprises an image preprocessing module, a feature point matching and screening module, an image optimal splicing seam generation module and a video output module. The method comprises the following steps: firstly, acquiring a video, carrying out dynamic range equalization, carrying out filtering processing by using an adaptive noise reduction method, and carrying out image correction; secondly, extracting feature points by using a self-adaptive corner detection algorithm, matching the feature points based on a multi-dimensional spatial data index to obtain feature point matching pairs, and screening the feature points; searching an overlapping region, and introducing a dynamic search algorithm to generate an optimal image splicing path to obtain an optimal image splicing seam; and finally, dividing the image according to the dynamic grid, and realizing video output by using priority ranking. According to the method, the video images are processed and spliced, the purpose of intelligent image splicing is achieved, and the method is accurate and objective.

The invention discloses a deformation monitoring method and system based on a machine vision technology, and relates to the technical field of crossing of structural health monitoring and computer vision, and the method comprises the steps: improving the angular point positioning precision in a calibration stage through a sub-pixel-level Harris angular point detection algorithm; robust recognition and initial positioning of an artificial marker in a first frame of image are realized by using an X-Feat convolutional neural network model, an anti-interference template is generated in combination with histogram equalization and Gaussian filtering, the stability of the system is enhanced, a local search area is delimited by taking an initial positioning point as a center, and a deep learning and interpolation algorithm is combined, so that the anti-interference performance of the system is improved. High-precision tracking and displacement calculation of the marker center are realized, the monitoring resolution is effectively improved, pixel-level displacement is accurately converted into physical space displacement through inverse transformation of a homography matrix, and dual threshold criteria of mode length and change rate are introduced for dynamic early warning, so that the engineering practicability and safety response capability of a monitoring result are improved.

The invention provides a pallet position identification system and method, and relates to the technical field of image identification, and the method comprises the steps: carrying out the Gaussian filtering and Canny edge detection of image data, extracting the edge features of pallets, judging whether there is an overlapped pallet in combination with depth information, and carrying out the layered identification of an overlapped region to mark a target pallet. Calculating a contour vector of the target pallet, and mapping the contour vector to a three-dimensional space by using depth information to obtain a spatial position coordinate; when the position meets a safe distance threshold value, angular point detection is carried out, coordinates of four angular points are extracted, orientation angles are calculated, and complete position features are output. According to the method, the problems of incomplete pallet edge detection, large space positioning error and inaccurate posture recognition are effectively solved, the pallet detection precision and reliability are improved, and meanwhile, the carrying safety in a storage environment is ensured.

The invention discloses a port container loading and unloading positioning system and method based on visual identification, and relates to the technical field of port logistics automation. The method comprises the following steps: acquiring a container surface image through an image acquisition device on a sling, and detecting an initial angular point through a deep learning model; when the angular points are missing or the confidence coefficient is insufficient, edge features are extracted, and sub-pixel coordinates are predicted through a reconstruction network so as to complement the angular points; sorting the complete angular point set to form closed-loop vectors, and calculating cross products of adjacent vectors to verify the topological effectiveness; and when the topology is effective, through coordinate conversion and pose optimization, outputting three-dimensional coordinates of the angular points in a world coordinate system, and further determining the position of the container and feeding back the position to a lifting appliance control system to guide loading and unloading. The system correspondingly comprises an image acquisition and corner detection module, a corner completion module, a corner sorting and cross product calculation module, a topology verification and coordinate conversion module and a positioning feedback module. The problem of inaccurate positioning caused by lifting tool shaking, stacking deviation and the like is solved, and the precision and efficiency of container loading and unloading are improved.

The invention discloses a target vehicle attitude recognition method based on 3D point cloud feature extraction, and the method comprises the steps: carrying out the preprocessing of the point cloud data of a target vehicle obtained through a laser radar, and the preprocessing comprises Gaussian statistical filtering, voxelization sampling, and vehicle tire point cloud extraction based on a region growing algorithm; based on the point cloud data, key points of tire steering features are extracted, and a Harris corner detection method is used for detecting the key points; calculating a fast point feature histogram (FPFH) descriptor based on the extracted key points, and performing coarse registration by adopting an SAC-IA algorithm to obtain a coarse registration matrix; and using a point-to-surface nearest point iteration ICP algorithm to perform fine registration on the point cloud after coarse registration to obtain attitude information of the target vehicle. According to the method, the three-dimensional attitude angle of the target vehicle is accurately estimated by preprocessing the laser radar point cloud, extracting tire key points and carrying out SAC-IA coarse registration and ICP fine registration. Therefore, vehicle attitude recognition with high shielding robustness and high precision is realized, and the safety of automatic driving is enhanced.

The invention discloses a text detection method and device, and relates to the technical field of data processing, and the method comprises the steps: carrying out the corner detection of text data of an original image based on a Harris corner detection algorithm, and obtaining a corner detection image; performing weighted fusion on the corner detection image and the original image based on a preset transparent parameter to obtain a fused image; performing feature extraction on the fused image based on a preset convolutional neural network model and a Vision Transform network model, and mapping the extracted features to a character probability based on a linear classifier and a softmax function; and generating extracted text data based on the character probability of each extracted feature. Compared with the prior art, corner detection is carried out on the text data of the original image based on the Harris corner detection algorithm to obtain the corner detection graph, even if the text data is rich and diversified in shape, the Harris corner detection algorithm can still carry out accurate positioning and extraction on key corners in the original image, and the accuracy of the corner detection graph is improved. Therefore, the method has better adaptive capacity for detecting the text data with abundant and diversified shapes.

The invention provides a 4D millimeter-wave radar and camera calibration method based on projection errors. The method comprises the following steps: acquiring radar point cloud and an original image of a calibration board in a static scene through a 4D millimeter-wave radar and a camera; performing dynamic object filtering and noise suppression on the synchronous data to obtain a static enhanced point cloud and a corrected image; performing coordinate extraction and checkerboard corner detection on the filtered data to obtain point cloud 3D coordinates and pixel 2D coordinates; performing projection conversion on the point cloud 3D coordinate through the external parameter matrix to obtain a conversion coordinate, and calculating a projection error between the conversion coordinate and the pixel 2D coordinate; and performing iterative optimization on the extrinsic parameter matrix through a nonlinear optimization algorithm based on the projection error to obtain an optimized matrix. According to the method, through dynamic interference suppression, noise collaborative filtering and a nonlinear optimization mechanism, the calibration precision and the environmental adaptability are remarkably improved.

The invention relates to the technical field of deburring, in particular to deburring equipment of a tool jig and an automatic burr positioning method. The method comprises the following steps: acquiring a plurality of monitoring images of a to-be-deburred tool jig under each view angle; performing Gaussian pyramid scale transformation on each monitoring image, performing angular point detection, and dividing the angular points in each monitoring image into two categories according to the presentation condition of the angular points in a scale image; obtaining a texture expression value of each angular point in the scale image by combining the relative distance between each angular point and other angular points in the corresponding scale image of each monitoring image and the edge change condition; and obtaining a texture feature value of each angular point in the monitoring image by combining the texture representation value of each angular point in the corresponding scale image in all the monitoring images under the same visual angle, and further dividing and screening a burr region for each category. According to the invention, the positioning accuracy of the burr area and the burr removal effect are improved.

The present application provides a corner point detection method and apparatus. The corner point detection method comprises the following steps: receiving a picture to be processed, generating a corner point set, performing edge detection on said picture and obtaining an edge map, and extracting M curves from the edge map; for each curve, performing the following processing: filling gaps along the curve, determining a plurality of T-junction points on the curve, adding the plurality of T-junction points into a corner point set S, smoothing the curve, acquiring the curvature values of all points in the curve, identifying a plurality of corner points on the basis of the curvature values of all the points, and adding the plurality of corner points into the corner point set, wherein the corner points in the corner point set are corner points detected from said picture. In this way, a corner point detection method is implemented.

The present invention provides a high-speed image analysis method suitable for identifying the interaction structure between flames in multi-jet flames, and belongs to the technical field of flame structure analysis. The present invention processes the flame self-luminescence / induced fluorescence image taken by a high-speed camera through a digital image processing method to identify the overlapping area between multi-jet flames, and specifically includes the following steps: selecting the analysis area, dividing the analysis area, Gaussian filtering, feature corner point detection, feature corner point supplementation, optical flow field calculation, flame area discrimination, flame outer contour recognition, and adjacent flame interaction extraction. The present invention discloses a high-speed image analysis method suitable for identifying the interaction structure between flames in multi-jet flames, which can identify the interaction structure between multiple flames from the flame image, and has high recognition efficiency. It can provide a method for further analyzing the interaction between flames and is very suitable for use in the field of flame structure research in multi-nozzle combustion chambers.

The invention discloses a target frame automatic tracking drift judgment method and device, equipment and a storage medium, and relates to the field of image recognition. Extracting a target frame region of two adjacent frames of images, and converting the target frame region into a grey-scale map; angular point detection is carried out on the previous frame grey-scale map, and a first angular point group is extracted; performing optical flow calculation on the current frame grey-scale map based on the previous frame grey-scale map and the first angular point group, and matching a second angular point group; based on the distance and angle information of the successfully matched feature angular point pairs in the angular point group, judging the drift state of the tracking target corresponding to the current frame image; and when it is judged that the tracking target drifts, predicting the target position in the current frame image based on Kalman filtering and re-identifying the tracking target, and determining the real target position in the current frame image by combining a re-identification result and a response diagram obtained after correlation calculation of the tracking template. According to the scheme, feature level drift judgment is realized through corner detection and optical flow matching, effective re-identification is carried out, and the problem of target frame drift is solved.

The invention provides a corner detection method and device, computer equipment and a program product, and the method comprises the steps: detecting each corner in a checkerboard image through employing a pre-trained corner detection network, and obtaining a first detection frame corresponding to each corner; performing image optimization operation on the detection frame images corresponding to the first detection frames to obtain optimized images; according to the gradient of each pixel point in the optimized image, screening out candidate saddle points conforming to saddle point features from each pixel point; and according to the first coordinate of each candidate saddle point in the optimized image, screening out a target corner point in the first detection frame from each candidate saddle point.

The invention discloses a three-dimensional point cloud registration method based on statistical spatial distance distribution, and the method comprises the following steps: extracting edge points and angular points in a source point cloud and a target point cloud through employing an HARRIS angular point detection algorithm, and generating a source point cloud key point set and a target point cloud key point set; counting the relative distribution frequency of the spatial distance between each point in the two key point sets and other key points in the field, and generating a high-dimensional feature vector; constructing spatial distance distribution feature descriptors by the spatial distance feature elements; according to the cosine similarity of the feature descriptors, points with the similarity degree of the feature descriptors larger than a set threshold value in the key points of the target point cloud are searched, and an initial corresponding point pair set is obtained; an initial spatial transformation matrix between corresponding point sets is solved by using an SVD algorithm through RANSAC, and coarse registration of the source point cloud and the target point cloud is realized; and after a relatively good initial corresponding pose is obtained, performing fine calibration on a registration result through an iterative closest point ICP algorithm. The method has the advantages of low operation cost, high speed, high precision and the like.

The present invention provides a high-precision fast camera calibration method and system based on a large-field fisheye lens, belonging to the field of computer vision and image processing. In order to solve the problems of low calibration accuracy and single evaluation index in the existing large-field fisheye lens calibration method. The present invention integrates improved methods of image super-resolution, image segmentation, corner point detection, corner point sorting, calibration model, and objective evaluation index, which can greatly shorten the time required for shooting and taking pictures, reduce the requirements for image quality while improving the robustness of the overall method, reduce the hardware requirements for calibration reference objects, improve calibration accuracy, and reduce the time required for model solution.

The application discloses a camera calibration method, a camera calibration system and a computer readable storage medium, and relates to the field of image processing. The calibration method comprises the following steps: acquiring first image information, wherein the first image information is used for representing an image after coarse screening of connected domains of a preset calibration template image; performing connected domain screening according to an overlap ratio and an overlapping area of a minimum circumscribed rectangle and a positive circumscribed rectangle of each first reserved connected domain in the first image information, to obtain a plurality of second reserved connected domains; classifying and sorting the second reserved connected domains; determining corner point information according to a classification and sorting result; and obtaining a conversion relationship between a pixel coordinate system and a world coordinate system according to the corner point information. The camera calibration method improves the accuracy and efficiency of corner point detection through twice different connected domain screening, and optimizes the effect of camera calibration.

The present application relates to the technical field of visual detection, and discloses a kind of offshore floating photovoltaic power station cleaning robot edge distance calculation method, comprising the following steps: S1, image and radar azimuth acquisition;S2, image correction and pretreatment;S2-1, based on the aforementioned direction angle, image is sequentially subjected to histogram equalization, rotation and projection processing, and the image horizontal line is 0 ° image;S3, edge and corner point detection are carried out by image recognition system, and point set is obtained;S4, point set is used to remove impurities algorithm, and block straight line fitting is carried out, and intercept data is obtained;S5, in combination with camera inclination, height and straight line intercept data, the actual cleaning robot edge distance is calculated.By in the process of robot normal work, distance photovoltaic panel edge angle and distance are judged at any time, to avoid cleaning robot falling into the sea, to improve the cleaning rate of photovoltaic power station and the safety factor of itself.

The embodiment of the invention provides a parking space detection method and device and a vehicle, and the method comprises the steps: obtaining original images shot by a plurality of camera devices on the vehicle, the original images being used for representing an image obtained by shooting at least one parking space around the vehicle, and the different original images having different shooting angles of view; splicing the original images based on camera shooting parameters of camera shooting equipment to obtain a target image; the parking space is detected on the basis of the original image and the target image, parking space detection results are obtained, and the parking space detection results at least comprise a target corner point detection result corresponding to the corner points of the parking space and a target entrance detection result corresponding to the entrance of the parking space. According to the invention, the technical problem of low accuracy of parking space detection in the prior art is solved.

The application relates to the technical field of image recognition, and particularly provides an unmanned aerial vehicle part automatic recognition method based on corner point detection, which comprises the following steps: constructing a neural network model comprising a channel attention mechanism, acquiring multiple unmanned aerial vehicle images, and pre-processing and unmanned aerial vehicle part labeling the unmanned aerial vehicle images; acquiring corner point information in the unmanned aerial vehicle images after the unmanned aerial vehicle parts are labeled through a corner point detection method, and generating a corner point feature map; fusing the corner point feature map and the unmanned aerial vehicle images after the unmanned aerial vehicle parts are labeled to obtain a corner point image; and training the neural network model again by using the corner point image, inputting a to-be-detected target image into the trained neural network model, and recognizing unmanned aerial vehicle parts in the to-be-detected target image. The application enhances the robustness of the neural network model to complex scenes and noise interference, and realizes accurate and efficient unmanned aerial vehicle part automatic recognition.

The application provides an AVM calibration method, comprising the following steps: determining at least one original calibration pattern in which corner point detection fails in a plurality of original calibration patterns laid around a vehicle and an original calibration pattern arrangement in which the at least one original calibration pattern is located; acquiring a redundant calibration pattern arrangement arranged at intervals with the original calibration pattern arrangement; performing a corner point detection algorithm on the original calibration pattern arrangement and the redundant calibration pattern arrangement to obtain corner points of a calibration pattern arrangement in which corner point detection succeeds in the original calibration pattern arrangement and the redundant calibration pattern arrangement, so as to calibrate AVM by using the corner points. The application also discloses a vehicle-mounted device and a storage medium. The application realizes the following advantages: without repeatedly adjusting light, without a special material calibration pattern and without constructing a darkroom and arranging controllable light sources, the operation process of AVM calibration is simple.

The present invention discloses a posture estimation method and device for a commercial vehicle electronic exterior rearview mirror camera, which belongs to the field of vehicle networking technology, obtains a calibration image, uses the Harris corner point detection method to identify the pixel coordinate data of the corresponding corner point according to the calibration image; uses the Kannala-Brant model to obtain the coordinate data of the corresponding corner point after distortion correction according to the pixel coordinate data of the corresponding corner point; uses the direct linear transformation method to obtain the transformation matrix of the commercial vehicle electronic exterior rearview mirror camera according to the coordinate data after distortion correction of the corresponding corner point; uses the SVD singular value decomposition according to the transformation matrix of the commercial vehicle electronic exterior rearview mirror camera to obtain the corresponding data, and completes the calibration. The present invention can accurately calibrate the rotation matrix and translation vector of the commercial vehicle electronic exterior rearview mirror camera.

The present invention discloses an object separator based on a 2D camera, comprising: an object conveyor belt arranged at the lower part, a plurality of 2D cameras at different heights and / or angles arranged above the object conveyor belt, a data processor connected to the plurality of 2D cameras, and a conveyor belt control system. The present invention also discloses a method for separating objects based on a 2D camera, comprising: a preprocessing part and an on-line processing part, wherein the preprocessing part includes a camera erection step and a camera calibration step, and the on-line processing part includes a corner point detection step of an object, a same object recognition step across cameras, and an object size and position calculation step, realizing the single-piece separation of fully automated multi-side-by-side objects, improving the object sorting efficiency, reducing the erection cost of visual devices in the single-piece separation system, and solving the problems of high hardware cost of the visual system in the current single-piece separation system, inaccurate object positioning, and low practicability.

This application relates to the field of heating, ventilation, and air conditioning (HVAC) and discloses a method for detecting window openings based on image processing, including: fitting a motion trajectory curve of a window sash marker at different window openings; performing corner point detection on the window image and retaining corner points within a set range of the motion trajectory curve; performing line detection on the window image and searching for corner points within the retained corner points that are simultaneously within a set distance from both ends of the line as target marker points; and determining the window opening based on the position of the target marker point. The above-mentioned window opening detection method combines corner point detection with line detection in the image processing method and the motion trajectory curve to filter out corner points and lines detected on the background, thereby accurately detecting the specific window opening and is suitable for more complex scenarios.

The application provides a target positioning method based on panoramic segmentation, and belongs to the field of target positioning. Firstly, an image acquisition sensor is used to acquire a real scene image of a target area, and an initial pose of the sensor is acquired; a rendering image is generated according to the initial pose of the sensor and a three-dimensional geographical space model which has been established; then, panoramic segmentation is performed on the real scene image and the rendering image, corner point detection and matching are performed on the segmentation results, and matched point pairs of the two images are obtained; the real pose of the sensor is calculated by using the matched point pairs and the initial pose of the sensor; finally, the real pose of the sensor is used to realize target positioning in a real scene. Compared with the matching method in the prior art which uses a deep learning method, the application does not need a large number of samples, uses a corner point detection method to determine the matched point pairs of the two images, improves the work efficiency, improves the matching precision between the heterogeneous images, and uses the real pose of the sensor to realize target positioning, thereby improving the target positioning precision.

The application relates to a camera calibration method, device, equipment and computer readable storage medium. The method comprises the following steps: acquiring a first checkerboard image collected by a to-be-calibrated camera for a first checkerboard and a second checkerboard image collected by the to-be-calibrated camera for a second checkerboard, performing corner point detection on the first checkerboard image to obtain first corner points, and performing corner point detection on the second checkerboard image to obtain second corner points; determining an initial calibration model according to first image coordinates and first physical coordinates of the first corner points; determining candidate physical coordinates corresponding to the second corner points according to second image coordinates of the second corner points and the initial calibration model; aligning the second physical coordinates of the second corner points with the candidate physical coordinates to obtain target physical coordinates corresponding to the second physical coordinates; and determining a target calibration model according to the target physical coordinates and the second image coordinates. According to the application, high-precision calibration of a camera can be realized.

The application discloses a method for locating the wing point on the body surface based on NCCT image data and belongs to the technical field of medical image processing. The method uses the NCCT sequence data of a routine image inspection at any time to reconstruct the shape of a skull, automatically and individually identifies the eyebrow bone features through corner point detection and Kmeans mean clustering, accurately determines the outer edge points of the eyebrow bone, adopts the method that the outer edge of the eyebrow bone is pushed forward to the direction of the extension line of the eyebrow bone by a curved surface distance of 3.5 cm and the midpoint of the malar arch is pushed upward by a linear distance of 4 cm, and the wing point is automatically and quickly located, which has important clinical significance.

The application discloses an image segmentation method and device based on optical detection. The method comprises the following steps: acquiring a reference segmentation region of a target image; intercepting at least one sub-detection region in the reference segmentation region according to the boundary of the reference segmentation region, wherein the sub-detection region covers at least an intersection position of adjacent boundaries of the target detection region; performing corner point detection in the sub-detection region, and determining a target corner point according to the relative position of the intersection position of the adjacent boundaries in the reference detection region; and dividing a target segmentation region according to the target corner point. The technical scheme provided by the application improves the test efficiency and reduces the recognition error.

The invention is suitable for the field of biomedical signal processing, and provides a TcPO2 curve trend analysis method based on k-cosine corner detection, and the method comprises the following steps: collecting and recording TcPO2 data in real time, constructing a chain curve structure, and preprocessing the obtained data; based on a k-cosine method, all angular points in the curve are screened out; the difference slope and neighborhood characteristics of the curve are analyzed, and the curve is divided into a plurality of straight line sections and curve sections based on neighborhood corner point intensity saliency and a corner point intensity threshold value; identifying the trend type of each curve section based on a trend type identification algorithm of a curve section and a straight line section; according to the method, a more visual and reliable signal analysis tool can be provided for medical staff, and the accuracy of clinical diagnosis and intervention is improved.

The invention relates to a checkerboard image-based corner detection method and device, and a medium. The method comprises the steps of collecting a checkerboard image which is shot by a vehicle-mounted camera and is used for carrying out camera external parameter calibration; extracting all inner corner points in the checkerboard image and directions of all edge straight lines connected with the inner corner points; on the basis of the sizes of checkerboard units forming the checkerboard image, angular point searching is carried out on the basis of the directions of all edge straight lines connected with all the inner angular points; and for each searched angular point, extracting all edge straight lines connected with the angular point, judging whether the gray value difference value of two sides of each edge straight line is greater than a difference value threshold value or not, and if so, judging that the angular point belongs to the angular point of the checkerboard image. According to the invention, robustness extraction of angular point features in an illumination shadow interference environment can be realized, and the accuracy of angular point detection is ensured; detection of internal and external angular points of checkerboard images in various scenes can be realized, the number of angular point detection is increased, and the precision of external parameter calibration is further improved.