77 results about "Object contour" patented technology

The invention discloses a grape fruit grain segmentation method based on multi-source feature fusion, relates to the technical field of image processing, and establishes an instance segmentation network MFFNet based on multi-source feature fusion, and the MFFNet cooperatively utilizes strong semantic features extracted by Swin Transform and explicit boundary information generated by parallel edge branches. According to the method, fruit grain segmentation is carried out on multi-scale information of ASFF adaptive fusion, targeted feature fusion is carried out in different stages by adopting a staged feature fusion strategy, and the precision and robustness of grape fruit grain segmentation in a complex scene are improved. Through the MFFNet, the segmentation problems of dense grape fruits, shielding, fuzzy boundary and the like in a complex field environment are effectively solved. The staged feature fusion strategy improves the perception and description ability of the model to the object contour, and well solves the problems of instance adhesion and unclear boundary.

The invention discloses a visual prosthesis control method and system and a storage medium, and relates to the field of medical instruments, and the method comprises the steps: extracting at least one initial target image based on a target object selection result fed back by a user, carrying out the region-of-interest extraction of the initial target image, and obtaining a target object contour image, grouping each contour pixel point based on the brightness values of the contour pixel points in the target object contour map to obtain a plurality of data groups and a stimulation sequence thereof, mapping a stimulation current matrix of each data group based on the stimulation current value and the space coordinate of each contour pixel point, and obtaining a stimulation current matrix of each data group based on the stimulation sequence; and controlling the release of the stimulation current in the electrode array based on each stimulation current matrix in sequence. Redundant information is reduced based on user feedback and region-of-interest extraction, the stimulation sequence based on the average brightness value of each data set is configured, the stimulation current is controlled to be released in the electrode array based on each stimulation current matrix in sequence, and the perception quality of the brain to the target object is improved.

The invention discloses a method and equipment for extracting object contour vector data based on an orthoimage, and the method comprises the steps: S1, inputting the orthoimage into a Mask2Former model based on a VisionTransform architecture, and carrying out the segmentation of ground feature elements in the image; s2, based on a Douglas-Peucker algorithm, adaptively extracting geometric constraints including key endpoints, later angles and side lengths in an original boundary after surface feature element segmentation, rejecting abnormal sides and unreasonable angles, finally extracting a main direction from relatively long sides, and sequentially executing collinear line segment fusion and line segment intersection operation to ensure polygon closure and direction consistency; and S3, performing contour reconstruction operation of the ground feature elements based on the main direction and the key points. The method can reduce the dependence on manpower, achieves the efficient and automatic extraction of the object contour vector data based on the orthoimage of the unmanned aerial vehicle, and improves the working efficiency.

The invention discloses a neighbor soft package two-dimensional segmentation and three-dimensional positioning combined method, which comprises the following steps of: segmenting a two-dimensional picture by using a Segment Anything algorithm and carrying out preliminary screening according to the contour area of a segmented object; performing coordinate range screening, determining and calculating an external rectangle and an area thereof through processing of a two-dimensional minimum compact bounding box, and outputting a minimum area by rotating a line to be greater than 90 degrees; obtaining an included angle between an object center coordinate and x and y through a bounding box, calculating a result through a formula, and performing range screening to segment a recognition object; processing the soft package by using a two-dimensional minimum bounding box algorithm to obtain a center coordinate and an included angle; acquiring internal and external parameter matrixes, two-dimensional coordinates and depth of the camera, and converting two-dimensional image coordinates into three-dimensional coordinates in a camera coordinate system and a world coordinate system; and calculating a neighbor point cloud normal vector to obtain a three-dimensional attitude of the soft package center point. According to the positioning method disclosed by the invention, the problem of low positioning precision of the soft package is solved by a minimum compact bounding box algorithm, and a more accurate grabbing pose is conveniently provided for the robot.

The invention relates to the field of computer vision and image processing, in particular to a three-dimensional matching method based on wavelet transform and superpixel segmentation, which can improve the problems of poor depth estimation precision, blurred depth map contour and failure in multi-texture area depth estimation of the existing three-dimensional matching method. According to the method, different frequency domain sub-band information of the image is obtained by introducing a wavelet transform image processing method, the guiding effect of a low-frequency structure is enhanced, and the cost body aggregation process is participated; and meanwhile, fine modeling is carried out on the boundary of an image structure by combining a superpixel segmentation technology so as to enhance the integrity of an object contour in a depth image and the accuracy of parallax estimation. The method shows higher matching precision and boundary definition in a complex scene, has good calculation efficiency and deployment feasibility, and can provide reliable support for an embedded stereoscopic vision system with high precision and low resource consumption.

The invention relates to the technical field of image processing, in particular to a computer image processing system based on deep learning, which determines a first contour boundary of a target object through a preprocessing module according to a deep learning model, and draws brightness expression curves of pixel points of each sub-region in different direction dimensions through a feature extraction module. Marking a non-confidence sub-region in the aggregation analysis region through a feature aggregation module, obtaining brightness expression curves of pixel points subjected to exposure adjustment in different direction dimensions through a local processing module so as to determine contour boundary fitting points, and fitting contour boundary sections of the non-confidence sub-region, and updating the first contour boundary through the output end and outputting a second contour boundary of the target object. According to the method, local region optimization is carried out aiming at insufficient local features in a target object contour boundary extraction process under a complex illumination condition, and high-precision extraction and dynamic updating of a target object contour in a complex environment are realized.

The invention relates to the technical field of sewing automation, and discloses an automatic sewing identification method and system based on image identification. The method comprises the steps of obtaining surface image data of a target sewing object, and performing feature extraction on the surface image data to generate a geometric contour data set; according to sewing parameter configuration information input by a user, executing path generation operation to obtain an initial sewing path coordinate sequence; based on spatial distribution characteristics of the geometric contour data set, performing path density adaptive adjustment on the initial path, and generating a corrected sewing path coordinate sequence; performing smoothness optimization processing on the corrected path to obtain a final sewing path coordinate sequence; and converting the final path coordinate sequence into visual graphic data, and rendering and outputting the visual graphic data in real time. According to the method, the contour of the sewing object can be accurately obtained, different sewing requirements can be flexibly met, the path density and smoothness are optimized, the accuracy and efficiency of sewing path generation are improved, the defects of a traditional method are overcome, and the method is suitable for various sewing processing scenes.

The invention belongs to the technical field of rain and fog image processing, and particularly relates to a rain and fog image restoration method and system based on space-frequency collaborative awareness and electronic equipment, and the method comprises the steps: obtaining a rain and fog image, and respectively extracting the spatial domain features and frequency domain features of the rain and fog image; fusing the spatial domain features and the frequency domain features based on an adaptive channel attention mechanism to obtain dual-domain fusion features; the dual-domain fusion features with recovered resolution and the spatial domain features with the same size are added through residual connection to obtain a feature map, convolution processing is performed on the feature map to obtain a clear image, jump connection of traditional U-net is replaced with a residual layer structure in residual connection design, the gradient disappearance problem is relieved through residual connection, and the model training efficiency is improved. According to the design, cross-level fusion of shallow-layer edge information and deep-layer semantic features is achieved, and compared with original jump connection, recovery of local details (such as object contours) and removal of rain and fog textures are better facilitated.

An image augmentation device is provided, which includes a memory and a processor. The processor is configured for performing following operations: extracting a first object contour from a first image mask, wherein the first object contour corresponds to a first label; superimposing the first object contour to a superimposed region in a second image mask according to an augmentation parameter to generate a third image mask, wherein the augmentation parameter includes a contour scaling parameter, a contour moving distance, a contour rotation angle and a range which can be superimposed, and the third image mask includes the first object contour and a second object contour in the second image mask; and generating a sample image which corresponds to according to the first object contour and the second object contour in the third image mask by a generative adversarial network model for performing machine learning.

The invention discloses a TSTFP-Net-based semantic segmentation method for a low-illumination superposition rain image, and the method aims at an image segmentation scene shot in a weak light and rainy environment, and solves a problem that the semantic segmentation precision is reduced because of insufficient illumination, raindrop interference and a significant distribution difference between a source domain and a target domain. The method comprises the following steps: 1) processing an existing data set; 2) designing a semantic segmentation network based on low illumination and rain superposition of TSTFP-Net; 3) training the RIPLM network; 4) training a trunk segmentation network; 5) training a space domain and frequency domain double-teacher network; the method comprises the following steps of (1) training an FSFM module, (2) training a comparison learning module, (3) training a domain discriminator network, and (4) testing a semantic segmentation network with low illumination and rain overlay. The method can still accurately identify the contour and category of an object in complex weather, improve the domain generalization performance, reduce the domain gap, and further improve the precision of semantic segmentation in the low illumination and rain overlay environment.

The invention relates to a parameter capturing model training method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring multiple groups of training parameters and training scenes, wherein the training scenes comprise multiple target objects; for each group of training parameters, determining an object grabbing score of the target object grabbed by the end effector based on the training parameters; based on the object capture scores corresponding to the multiple groups of training parameters, determining a total point capture score corresponding to the sampling points of the target object; based on the scene point cloud corresponding to the training scene, the object contour mask and the total point capture score corresponding to each sampling point, determining a training sample corresponding to the training scene; training the initial grabbing parameter model by using a training sample to obtain a target grabbing parameter model; the target grabbing parameter model is used for determining target grabbing parameters of the end effector. By adopting the method, the accuracy of capturing the parameter model can be improved.

The invention provides a scatter diagram generation method based on an object contour diagram, and belongs to the technical field of graphic processing, and the method comprises the steps: contour preprocessing: carrying out the smooth denoising processing of a contour diagram, carrying out the Gaussian blurring of the contour diagram, converting the contour diagram into a binary image, extracting a contour set of a connected region in the binary image, and carrying out the Gaussian blurring of the contour image; calculating the surrounding area of each contour and carrying out noise filtering; uniform sampling and initial scatter point generation: constructing a pixel point set, and calculating a maximum distance and a minimum distance of sampling; and scatter-contour matching optimization: traversing the contour line and the scatter set, and eliminating interference scatters to obtain a final scatter diagram of the object contour. Through the methods of uniform sampling, contour point matching, repeated point de-duplication and the like, a specified number of uniformly distributed scatter points can be quickly generated for a contour map. According to the method, the defect of scatter point generation in the current method is overcome, model training is not needed, generalization is high, and the method can be widely used in profile diagrams of various graphs.

The application relates to an object pose recognition method and device, computer equipment, a storage medium and a computer program product. The method is applied to an automatic guided vehicle and comprises the following steps: dividing point cloud data into multiple point group data; determining a point group difference value between a pose recognition parameter of each point group data and a historical point cloud cumulative value; selecting object point group data, in which a point group difference value meets an object contour condition, from each point group data; determining object pose reference lines in different directions according to the object point group data, and determining the pose of the object point group data relative to the automatic guided vehicle through the object pose reference lines. The object pose can be more accurately recognized by using the method.

The embodiment of the invention discloses a curve generation method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a target point set; wherein the target point set at least comprises coordinate information of a first coordinate point of a target object contour; based on a pre-trained detection model, determining an index sequence of second coordinate points belonging to the boundary of the target object in the target point set; and determining a target boundary curve of the target object based on the index sequence. According to the technical scheme, the problems that a traditional curve generation method depends on manual parameter setting and is difficult to adapt to complex data distribution and the precision is difficult to guarantee are solved, high-precision automatic generation of the object boundary curve is achieved, and the technical effects of improving the curve generation accuracy and stability and enhancing the multi-scene applicability are achieved.

The application provides a warning zone identification method and device, electronic equipment and storage medium. The identification method comprises: performing yellow and white warning zone image preprocessing on a to-be-identified region image to determine a processed first image, and performing red warning zone image preprocessing on the to-be-identified region image to determine a processed second image; performing object contour extraction on the first image and the second image to determine a first object contour of the first image and a second object contour of the second image; performing preliminary contour screening processing and contour shape calculation processing on the first object contour and the second object contour to determine a candidate contour; wherein the candidate contour is a contour that meets the warning zone characteristics in the first object contour and the second object contour; and based on the number of pixel points of a target color in the candidate contour, the warning zone in the to-be-identified region image and the type of the warning zone are determined, thereby improving the accuracy of warning zone identification.

The two-stage object six-degree-of-freedom pose estimation method based on a monocular camera solves the problem of how to achieve high-precision and high-efficiency pose estimation of weak-texture industrial parts, and belongs to the technical field of monocular camera pose estimation. The present invention is a two-stage pose estimation method from coarse to fine. The coarse pose estimation network roughly estimates the six-degree-of-freedom pose value based on the fusion of mask features and 2D position features. The coarse pose estimation stage provides the initial value for precise pose estimation, avoiding the optimization process of the precise pose estimation algorithm from falling into the local optimal value. The precise pose estimation network introduces a dynamic snake-shaped convolution layer to construct an object contour feature extraction network, improves the quality of contour feature generation, and filters out background contours. Through the differentiable Gauss-Newton optimization method, iterative optimization is performed at multiple scales to minimize the reprojection error between the projection contour and the object contour, thereby achieving accurate pose estimation.

The invention discloses a 3DGS data acquisition method and related equipment. The method comprises the following steps: after responding to a data acquisition instruction, firstly initializing a camera of the electronic equipment, then calibrating an object contour range and a middle axis of a to-be-acquired object, determining the type of a two-dimensional object or a three-dimensional object to which the to-be-acquired object belongs, and then formulating a corresponding shooting path and shooting parameters based on the determined object type and object contour range; and finally, displaying the shooting path and the shooting parameters on a display screen of the electronic equipment so as to prompt a user to complete data acquisition of the to-be-acquired object according to the path and the parameters. By means of the object contour range, calibration of the center axis and judgment of the object type, an accurate object feature basis is provided for subsequent shooting, then an adaptive shooting path and shooting parameters are determined based on the features, related information is visually displayed through a display screen, and a user is effectively guided to standardize acquisition operation.

This invention discloses a phase-height mapping calibration method and apparatus in fringe projection, relating to the field of three-dimensional object contour measurement technology. The method includes: controlling a projector to project a set of phase-shifted sinusoidal fringe patterns onto a micromirror array with known height values; simultaneously triggering a camera to acquire fringe image sequences; performing phase shift calculations on the fringe image sequences to obtain a wrap-around phase map; constructing a global optimization model of absolute phase using the known height value of the micromirror array as an absolute constraint; solving for the absolute phase field corresponding to the height of the micromirror array; extracting the center coordinates, absolute phase values, and known height values ​​of each micromirror element; constructing a three-dimensional control point set; fitting the three-dimensional control point set; calculating the continuous model coefficient field; and forming a phase-height lookup table. This invention can significantly improve the real-time calculation efficiency of three-dimensional measurement, meeting the practical needs of high-precision, high-stability industrial three-dimensional shape measurement.

This invention relates to the field of image processing technology and provides a motion control method and apparatus. The motion control method includes: acquiring a real-time image of an object to be clamped through an industrial vision module; extracting the initial object contour edge poles corresponding to the object to be clamped from the real-time image; identifying feature points located around the object to be clamped; determining the actual object contour edge poles based on the spatial distribution relationship between the feature points and the initial object contour edge poles; calculating the clamping position and motion path based on the position information of the actual object contour edge poles; and controlling the servo motion module to perform object clamping motion based on the clamping position and the motion path. The motion control method provided by this invention effectively improves the accuracy, flexibility, and efficiency of the clamping process through precise object contour recognition, path planning, and servo control.

This invention discloses a light rail vehicle safety monitoring system based on multi-sensor fusion, belonging to the field of light rail vehicle safety monitoring technology. The system consists of several functional modules, including: a multimodal perception module, which captures real-time images of the light rail's surrounding environment and delineates visually marked areas; performs 3D scanning of the visually marked areas to generate environmental point cloud data in real time, and uses a Doppler compensation algorithm to acquire the distance to dynamic objects; a data processing module, which uses optical flow and a CNN deformation detection network to calculate object deformation between adjacent frames; performs relative motion calculations based on object shape analysis; outputs object contours based on MaskR-CNN instance segmentation; simultaneously uses a dynamic target clustering algorithm to perform multi-target tracking of the object contours, and outputs the object's 3D coordinates and motion vectors in conjunction with environmental point cloud data; and a fusion and analysis module, which performs time synchronization based on the PTP protocol.

The invention discloses a 2.5 D convolution-based feature extraction method and system, and the method comprises the steps: carrying out the useless region cutting of an original 3D image, and obtaining a preprocessed 3D image; determining a plurality of slice directions according to the object contour of the original 3D image; according to the plurality of slice directions, segmenting the preprocessed 3D image into a plurality of 2D image sets; performing fusion processing on all the 2D images in the plurality of 2D image sets to obtain a 2.5 D image output set; multi-channel processing of a convolutional neural network is carried out on the 2.5 D image output set, and internal and external structural features of the object are extracted; and determining an abnormal region of the object according to the structural features. The 2.5 D image set is obtained by carrying out cutting preprocessing and multi-direction slice segmentation on the 3D image, non-time dimension correlation processing on the 3D image is achieved, the calculation complexity and the requirement for computing power and memory resources are reduced, and the image recognition efficiency and credibility are improved.

Embodiments of the present application disclose a shadow adjustment method, device and equipment and a storage medium. The method comprises: when a shadow adjustment event is detected, obtaining a to-be-adjusted map in which a generated shadow and an irradiated object irradiated by a light source correspond to the shadow adjustment event; obtaining an outline map obtained by rendering an object outline of the irradiated object extracted from the to-be-adjusted map, and comparing the to-be-adjusted map and the outline map to obtain to-be-adjusted pixel points of the irradiated object covered by the generated shadow in the to-be-adjusted map; adjusting the to-be-adjusted pixel points, and rendering an adjusted map according to a pixel point adjustment result. The technical solution of the embodiments of the present application can present the irradiated object covered by the generated shadow.

The application aims to provide a method and device for jet sorting objects. The application determines real physical coordinates of all points of an object contour according to pixel coordinates of the object; converts the real physical coordinates of all points of the object contour into time coordinates using a time stamp; calculates a range covered by each object on a corresponding time coordinate axis according to the real physical coordinates of all points of the object contour, a speed of a conveying belt and a position of a photographing position to a jet valve position; calculates a jet time and a jet position according to the range covered on the corresponding time coordinate axis; combines the jet position and the jet time; and controls the jet valve to jet sort according to the combined data. Thus, the object can be accurately positioned, the jet time can be calculated with low positioning complexity, the jet is accurate and the object can be effectively sorted.

The invention relates to a modeling machine, an arrangement, and a method for detecting a danger to a person in a current working area of ​​a manipulator during a volume change of an object by means of the manipulator, comprising the steps of selecting a first, in particular memory-stored, field data set from a plurality of field data sets based on a first current position of the manipulator, wherein the first field data set is in particular assigned a first predefined object contour, determining a current second object contour at the first position based on second sensor data from a second measurement by means of a sensor arranged indirectly and / or directly on the manipulator, detecting that the second object contour deviates from the first object contour and, in response thereto, outputting a signal which represents the danger to the person in the current working area of ​​the manipulator.

The present application relates to the technical field of image processing, and especially relates to a computer image processing system based on deep learning, which determines the first contour boundary of a target object according to a deep learning model through a preprocessing module, draws the brightness performance curves of the pixel points of each sub-region in different direction dimensions through a feature extraction module, marks the non-confidence sub-region in the aggregation analysis region through a feature aggregation module, obtains the brightness performance curves of the pixel points after adjusting the exposure in different direction dimensions through a local processing module to determine the contour boundary fitting points, fits the contour boundary section of the non-confidence sub-region, updates the first contour boundary through an output end and outputs the second contour boundary of the target object. The present application performs local region optimization for the local feature deficiency in the contour boundary extraction process of the target object under complex lighting conditions, and realizes high-precision extraction and dynamic updating of the target object contour in a complex environment.

The invention discloses a pure machine vision distance measurement method and system, and relates to the technical field of environmental perception. The method comprises the following steps: a calibration stage: a plurality of concentric arc marks are preset on the flat ground, the marks take the projection of a preset reference point on the ground as the circle center, and the radius is the known distance; a first camera and a second camera are installed on a support with pitch angles capable of being synchronously adjusted at a fixed non-zero relative pitch angle difference, calibration image pairs are synchronously collected at a plurality of preset overall pitch angles, center lines of arc marks in the images are extracted, and a pixel-distance mapping relation pair library in one-to-one correspondence with the overall pitch angles is established. In the distance measuring stage, a first camera and a second camera are fixedly installed on a carrier according to the fixed non-zero relative pitch angle difference, a to-be-measured image pair is collected in real time, the contour of a target object is detected, all preset overall pitch angles are traversed, and a first mapping relation and a second mapping relation under the corresponding overall pitch angles are obtained from a mapping library respectively; and querying the minimum distance of the contour pixels to obtain a first candidate distance and a second candidate distance, selecting the overall pitch angle of the target according to the consistency of the first candidate distance and the second candidate distance, and determining a final distance measurement result based on the two candidate distances corresponding to the overall pitch angle of the target. Through double-view multi-overall pitch angle calibration and consistency matching, the method is adaptive to complex three-dimensional terrains, does not need camera internal reference, does not depend on target priori, does not need any active sensor, and realizes high-precision, low-cost and low-calculation-amount real-time distance measurement.

The embodiment of the invention provides a method for determining a three-dimensional marked area of a target object and a related device. The method comprises the following steps: acquiring a contour set of a target object, wherein the contour set comprises object contours of the target object on a plurality of first sections of a three-dimensional image; determining a plurality of contour point groups based on the contour set, each contour point group comprising a single contour point on each object contour in the contour set, and for a first contour point on a current first tangent plane and a second contour point on an adjacent first tangent plane adjacent to the current first tangent plane in each contour point group; the first contour point is a contour point which is closest to the second contour point in a first target direction parallel to the plurality of first tangent planes in the contour points on the current first tangent plane; generating an envelope line based on each of the plurality of contour point groups; and determining a three-dimensional marking area containing the target object based on the contour set and the generated envelope line. According to the scheme, the accuracy and efficiency of determining the three-dimensional marking area can be improved.

The invention relates to the technical field of computer vision and image processing, in particular to a complex surface-oriented object contour extraction system and method, and the system comprises an object contour extraction center, an image data module, a contour model construction module, a laser construction module and a fusion acquisition module. According to the method, information of a complex surface object can be comprehensively obtained through multi-view image collection, the image quality is improved through preprocessing, meanwhile, the construction precision and accuracy of a visual contour model are improved along with feature fusion analysis and feature point analysis, high-precision point cloud data are obtained through optical scanning, a laser contour model is obtained, and the accuracy of the laser contour model is improved. And meanwhile, the accuracy and integrity of the visual contour model and the laser contour model are ensured by using a multi-dimensional verification mechanism, and the construction of the final contour model is realized through a model overlapping fusion mode, that is, the precise contour extraction of the target object is realized through a laser contour and visual feature combination mode.

The invention provides an end-to-end target object frame extraction method and device, equipment and a medium. The method comprises the following steps: marking an original remote sensing image by using a real frame to obtain sample data, and training a constructed target object frame extraction model by using the sample data to obtain a trained target object frame extraction model; then, testing the trained target object frame extraction model to obtain a final target object frame extraction model; and finally, processing a remote sensing image to be extracted by using the final target object extraction model, and outputting a corresponding target object frame prediction result, so that an accurate target object contour in the remote sensing image to be extracted can be obtained, and the obtained target object frame prediction result is subjected to geographic data marking or analysis.

The application discloses a rolling mill tapping bending state detection method and device, equipment and medium, relates to computer technology field, including determining each single frame image by using video stream, generating reference object image, carrying out coordinate calculation, obtaining reference object contour pixel coordinates and rolling piece contour pixel coordinates; generating reference object axis pixel coordinate point set and rolling piece axis pixel coordinate point set, generating rolling piece axis fitting equation and reference object axis fitting equation; calculating the rolling piece axis fitting equation, obtaining the bending measure, carrying out coordinate conversion on the reference object axis pixel coordinate point set, obtaining the plane size coordinates, calculating the rolling piece chord height according to the plane size coordinates and the bending measure; based on the rolling piece axis fitting equation, the reference object axis fitting equation, the bending measure and the rolling piece chord height, the rolling mill tapping bending state detection result is generated. The application can reduce the labor intensity of artificial visual inspection, reduce the production safety accident rate, improve the rolling mill tapping product quality and efficiency.