51 results about "Orientation (computer vision)" patented technology

This invention belongs to the field of image processing and computer vision technology, and discloses a lightweight single-image super-resolution reconstruction method based on a directional stripe hybrid network. It captures directional structural information of the image from both the spatial and frequency domains through horizontal stripes, vertical stripes, local window attention of the directional stripe self-attention module, and direction-specific convolution of the directional high-frequency enhancement module. The directional stripe self-attention module effectively captures long-distance dependencies extending along specific directions; the directional high-frequency enhancement module extracts high-frequency components through frequency domain decomposition, combines them with direction-specific convolution, and introduces a dual-guided adaptive enhancement mechanism. It identifies texture-rich regions through local variance maps and structural boundaries through edge maps, generating adaptive weights to guide feature enhancement, accurately enhancing high-frequency edges and textures with different orientations while effectively suppressing noise. The two work together to achieve spatial-frequency domain dual-level directional modeling, significantly improving the reconstruction quality of images rich in directional textures, such as architectural scenes and cartoon images, while maintaining a lightweight design.

The application provides a monocular three-dimensional object detection method, device, equipment and product, and relates to the technical field of computer vision.The method comprises the following steps: acquiring a to-be-detected image; inputting the to-be-detected image into a monocular three-dimensional object detection model to obtain a two-dimensional detection result, object depth, observation angle and size of the to-be-detected object output by the monocular three-dimensional object detection model; generating a three-dimensional center point of the to-be-detected object in a three-dimensional space according to a two-dimensional center point and the object depth; generating an orientation angle of the to-be-detected object according to the three-dimensional center point and the observation angle; and generating a three-dimensional detection result according to the three-dimensional center point, the orientation angle and the size.The application significantly optimizes the prediction performance of a truncated object in monocular three-dimensional detection, is not limited to the type of a detection object, and can perform monocular three-dimensional detection on normal objects and truncated objects.

The application discloses an image generation method based on a diffusion model, and relates to the field of computer vision and remote sensing image processing. Through a direction and size perception attention mechanism, the application realizes the cooperative and accurate control of the target position, direction and size in remote sensing image generation, and the geometric consistency of the generated image and the input layout is significantly improved. Based on a complete layout rebalancing, conditional generation and quality screening process, the application can automatically and high-quality synthesize a data set, and the accuracy of a downstream rotating target detector is obviously improved on multiple benchmark data sets, effectively solving the problems of data scarcity, uneven distribution and high labeling cost in remote sensing detection tasks, and providing reliable data support for remote sensing target detection tasks. The method can be used for generating synthetic remote sensing data sets with consistent layout and accurate geometric properties, and is especially suitable for generating remote sensing target detection training data containing various orientations and sizes.

The invention discloses an image generation method based on a diffusion model, and relates to the field of computer vision and remote sensing image processing. According to the invention, through a direction and size perception attention mechanism, cooperative accurate control of the position, direction and size of the target is realized in remote sensing image generation, and the geometric consistency of the generated image and the input layout is significantly improved; on the basis of complete layout weight balance, conditional generation and quality screening processes, data sets can be automatically synthesized in a high-quality mode, the precision of downstream rotating target detectors on multiple reference data sets is obviously improved, the problems of data scarcity, unbalanced distribution and high labeling cost in remote sensing detection tasks are effectively solved, and the accuracy of the remote sensing detection tasks is improved. And reliable data support is provided for a remote sensing target detection task. The method can be used for generating a synthetic remote sensing data set with consistent layout and accurate geometric attributes, and is particularly suitable for generating remote sensing target detection training data containing various orientations and sizes.

This invention provides a starting point annotation correction method based on panoramic image and scene graph matching, relating to the fields of computer vision and building information processing technology. The method includes: generating sets of triangles with the same orientation combination—a scene graph triangle set and an actual scene triangle set—based on the actual scene corner point set and the scene graph feature point set, respectively; matching the triangle pair with the highest similarity between the two sets by calculating the similarity of the triangle's interior angles and side length ratios, establishing a correspondence between local obstacles in the scene graph and the actual scene; mapping the physical starting point position of the actual scene to the scene graph coordinate system based on the affine transformation parameters determined by the triangle pair, and outputting the corrected starting point coordinates. This invention can achieve accurate and efficient restoration of the current layer layout of a construction site.

This application belongs to the field of computer vision and 3D point cloud processing technology. It provides a convolution method for anisotropic point clouds based on local geometric adaptation. The embodiments of this disclosure estimate the principal orientation of the local geometric structure by performing principal component analysis on the neighborhood point cloud and constructing an affine transformation matrix. This matrix is ​​then used to dynamically deform the basic kernel points of the convolution operator, dynamically aligning the shape and orientation of the convolution kernel with the local structure of the point cloud. Integrating this convolution operator into a point cloud registration network allows for feature extraction from 3D point clouds, particularly those from underwater multibeam echo sounders. The convolution operator constructed using this method effectively captures the structural priors of strongly anisotropic point clouds, solving the problems of structural information loss and limited feature extraction capabilities caused by isotropic kernels when processing such data.

The invention relates to a sit-up counting detection method and system, and belongs to the technical field of computer vision, and the method comprises the steps: obtaining the position of a terminal device, dividing target persons into three rows based on the position of the terminal device, setting a preset number of point locations from each row close to the terminal device, and determining the head orientation of each row of target persons; under the condition that counting detection is started, first key point coordinates of each frame of target personnel in a counting time range are obtained; calculating the position score of each frame of target personnel based on the first key point coordinates, and calculating the number of sit-ups of the first row of target personnel according to the position score of each frame of target personnel, the position change score, the counting score and the action standard score in each getting-up process; and calculating the number of sit-ups of the second row of target persons based on the second key point coordinates and calculating the number of sit-ups of the third row of target persons based on the third key point coordinates. High-precision and high-robustness automatic counting of sit-ups in a complex real scene is realized.

The application provides a full-scene group activity understanding method based on a mixed hierarchical representation, and mainly relates to a group activity understanding problem in the field of computer vision. The method comprises the following steps: constructing a data set and a neural network framework, training a network, testing a network and the like. The neural network framework firstly extracts time sequence features, spatial environment features and the like to form rich individual features, and classifies individual actions; then, taking the individual features as nodes, information between the nodes in the individual interaction level and the action interaction level is inferred by using a proposed graph reasoning strategy, and is fused into group features; meanwhile, a topological graph structure is constructed by using position coordinates and orientations, and a graph neural network is introduced for representation, and is fused into group structure hierarchical features; finally, the group features and the group structure hierarchical features are spliced to identify a group activity type. The application fully represents a multi-level group activity, and simultaneously identifies individual actions of group members and a whole activity type.

The invention relates to a system and method for estimating room coordinate axes based on computer vision scanning of a room and its content objects and a coordinate axis majority voting process. The invention provides a method comprising: capturing a plurality of images of an indoor environment having one or more walls and a plurality of objects, each wall and object having a surface oriented along a corresponding plane; local coordinate axis orientations of at least a portion of the wall and the object are determined based on information received from the AR engine. The method includes estimating a room coordinate axis of an indoor environment based on a voting process, wherein the room coordinate axis is determined using a majority-matched local coordinate axis orientation; and outputting an indication of the room coordinate axis.

The invention relates to the technical field of computer vision and intelligent information processing, in particular to a machining drawing information extraction method based on YOLO + OCR, which comprises the following steps: sequentially carrying out graying processing, Gaussian filtering noise reduction and Canny edge detection on a machining drawing, and extracting and screening drawing frames and drawing areas; detecting a text group in the drawing by adopting a YOLO-OBB algorithm; and performing discrete classification prediction on the rotation angle of the detected text group image. According to the method, text pixel loss caused by direct zooming is avoided through drawing preprocessing, the 360-degree text direction problem is solved by utilizing a YOLO-OBB algorithm and a four-classification neural network model, PMI information is aligned and recognized in combination with a special symbol model, global parameters are extracted through natural language processing, and finally errors are monitored and corrected. The problems of text pixel loss, insufficient direction processing, poor symbol and text association and the like in the prior art are solved.

The invention belongs to the technical field of optical measurement and computer vision, and particularly relates to a photometric three-dimensional deformation monitoring method oriented to a natural light source condition. The method comprises the steps that targets are designed, and a target array is arranged according to the imaging range of a camera and the morphology characteristics of an observation object; measuring three-dimensional coordinates of feature points of a target pattern, and calculating a normal vector of a target surface and internal and external orientation elements of an image; calculating multi-period light source directions and light source intensities based on the target three-dimensional coordinates, the normal and the internal and external orientation elements of the image; calculating a surface normal vector by using a luminosity stereoscopic model based on the multi-period light source direction and intensity; and realizing object surface continuous reconstruction based on the surface normal vector and the imaging geometric constraint. Compared with the traditional method, the method can break through the problem that the traditional photometric stereo depends on an artificial light source and is difficult to adapt to the dynamic change of a natural light source so that the method is not applicable, can realize deformation monitoring in an outdoor environment without the artificial light source, and has the advantages of low cost and wide coverage range.

The invention provides a starting point marking correction method based on panoramic image and scene graph matching, and relates to the technical field of computer vision and building information processing, and the method comprises the steps: respectively generating a triangle set, a scene graph triangle set and an actual scene triangle set which are combined in the same direction according to an actual scene angular point set and a scene graph feature point set; by calculating the similarity of the inner angles and the side length proportions of the triangles, matching the triangle pair with the highest similarity in the two sets, and establishing a local obstacle corresponding relation between the scene graph and the actual scene; and mapping the physical starting point position of the actual scene to the scene graph coordinate system according to the affine transformation parameter determined by the triangle pair, and outputting the corrected starting point coordinate. According to the method, accurate and efficient recovery of the current layer layout of the construction site can be realized.

The invention discloses a tray identification method and device based on structural feature point cloud, equipment and a storage medium, and relates to the technical field of computer vision and detection. Acquiring tray RGB information and tray depth information through a binocular camera; generating a point cloud of the tray based on the depth information, aligning the RGB information with the depth information, and extracting an ROI region of the tray in the point cloud; converting the point cloud data into a two-dimensional plane in a camera coordinate system through the projection matrix; the point cloud data in the two-dimensional plane is rotationally scanned, and the main beam orientation of the tray is calculated; cutting the ROI region in the orientation range of the main beam to obtain point cloud data of the measured surface; projecting the point cloud data of the measured surface to a plane orthogonal to the rotating shaft, and calculating the spatial positioning of the tray through a straight line fitting algorithm; the scheme is high in identification precision, accurate in size measurement, high in processing efficiency, adaptive to a high-beat production line and high in automation degree; the method has strong environmental adaptability and anti-interference capability, and is strong in adaptability and good in robustness.

The invention relates to an azimuth information measurement and transmission system based on computer vision, in particular to an azimuth reference calibration device and method for computer vision measurement equipment, and aims to solve the problem that a method and a device for calibrating the azimuth reference of the vision measurement equipment in the system are lacked at present. And the measurement result of the vision measurement equipment cannot be directly applied to the azimuth transmission system. The calibration device comprises an orientation physical datum piece, a calibration table, a reference datum piece and angle measurement equipment, the orientation physical datum piece is arranged outside the vision measurement equipment to represent the orientation datum of the vision measurement equipment, and the calibration table is used for being erected on the side, along the optical axis of the vision measurement equipment, of the vision measurement equipment to represent the orientation datum of the vision measurement equipment. Comprising a base as well as a collimation measurement cooperative target and an imaging measurement cooperative target which are arranged on the base, and the reference piece is used for measuring the azimuth included angle of the azimuth physical datum piece relative to the collimation measurement cooperative target in cooperation with the angle measurement equipment.

This invention belongs to the fields of cultural relic protection and computer vision technology. Addressing the problems of low efficiency, poor accuracy, and lack of spatial positioning in traditional methods for detecting defects in ancient city walls, this invention discloses an intelligent detection method and system for surface defects of ancient city walls based on 3D reconstruction. The method includes: processing the texture information of a loaded 3D reconstructed model of the ancient city wall using a multi-strategy texture restoration method; analyzing the model's geometric structure to determine the wall's orientation and dimensional parameters; generating virtual camera positions based on geometric features and a meshed serpentine scanning strategy; rendering RGB and depth images at each virtual camera position; performing defect detection using a YOLO model; mapping the 2D detection results to 3D space to generate 3D defect marker color blocks; and fusing multi-viewpoint results to output the detection results of surface defects on the ancient city wall. This invention achieves efficient, accurate, and automated detection of defects in ancient city walls, providing effective technical support for cultural relic protection.

Apparatus and methods relate to dynamic hazard exposure dosimetry (HED). In an illustrative example, a HED computation engine (HEDGE) may automatically determine a relative three- dimensional (3D) position of a person to a hazard source by analyzing an image feed, such as by a computer vision engine. The 3D position of the person may, for example, be determined by evaluating the size and orientation in the image feed of one or more visual indicia worn by the person and / or disposed on the hazard source. A dosage prediction engine may, for example, generate a hazard dosage based on the relative position over time, such as by using predetermined hazard dosage distributions correlated with the 3D position of the hazard source. Various embodiments may advantageously provide a reduced complexity, easily installed and / or maintained real-time monitoring of hazard exposure (e.g., radiation exposure) in controlled environments (e.g., medical operating rooms).

Systems and methods are provided for estimating an axis of a room based on a computer vision scan of the room and its contents, and a process of axis majority voting. A method is provided that includes capturing a plurality of images of the interior environment having one or more walls and a plurality of objects, each wall and object having a surface oriented along a corresponding plane; determining, based on information received from an AR engine, local axis orientations for at least a portion of the walls and objects. The method includes estimating a room axis of the interior environment based on a voting process, wherein a majority of matching local axis orientations are utilized to determine the room axis; and outputting an indication of the room axis.

The invention discloses a point pricking control method for a PTZ camera image set, a medium and electronic equipment, and the method comprises the steps: extracting and describing feature points from the image set, and carrying out the matching of the extracted feature points; based on a matching result, estimating motion between the images by using an essential matrix, and performing rotation averaging on all the images to obtain an image relative orientation result; performing bundle adjustment to optimize a relative orientation result; querying a relative orientation result, and obtaining an internal parameter matrix and an external parameter between the images; calculating a conversion relation of corresponding points between the images by using the internal reference matrix and the external reference between the images; and according to the conversion relation, calculating and marking the predicted position of the control point on the non-pricking point image. Through the computer vision technology and the SFM technology, automatic control point pricking of the PTZ camera image set is achieved, the working efficiency and precision are improved, and the method is suitable for various monitoring and measurement application scenes.

Workstation for performing a task on a workpiece using a tool. Some implementations of the invention allow an adjustable shelf to be used to position a workpiece on the workstation. Some implementations of the invention include a support bar for partially supporting a tool as the task is performed on a workpiece coupled to the workstation. Some implementations of the invention allow a workpiece to be positioned at a reference location or orientation relative to the workstation. Some implementations of the invention allow a tool to perform an action based on determining the position of the tool relative to the workstation using computer vision techniques to analyze a feature of the workstation.

The invention relates to the technical field of computer vision, and discloses a book checking method and system based on computer vision. The method comprises the following steps: acquiring multi-source perception data of the book shelf through three-dimensional scanning equipment, and processing the multi-source perception data to generate standardized point cloud data; the multi-modal fusion network is utilized to jointly extract book surface features and identifier information, and an identification result containing a unique identifier is output; the book orientation angle and the coordinate offset are solved by combining a bookshelf topology model, and accurate pose data are obtained; based on the pose data, a robot motion track is generated by adopting a probability route map algorithm, and a robust inventory path is obtained through online correction; and tracking book loss or misplacement events in real time according to the path, and associating a time sequence log to generate an incremental inventory report. According to the invention, high-precision book identification and state diagnosis are realized, and the inventory automation level is improved.

The invention discloses a main direction rotating target detection method for deformable quadrilateral attention, and belongs to the technical field of computer vision. The method comprises the following steps: 1) loading a rotating target data set, preprocessing an image, and converting the image into a model training format; 2) through a detection head of the deformable quadrilateral attention module, mapping the fixed window into a quadrilateral adaptive to the geometrical shape of the target through a dynamic prediction transformation matrix; 3) a principal component analysis module is integrated on the detection head, and a principal direction and a rotation bounding box are calculated based on vertex coordinates of the target quadrangle; and 4) inputting the test set into the trained model, and outputting an angle-containing bounding box, category and confidence of the rotating target. According to the method, the mAPs on DOTA-v1.0 and HRSC2016 data sets are 79.38% and 98.90% respectively, compared with an existing method, the precision of the method is improved by 3.2%-5.6%, particularly, the detection robustness of irregular and multi-attitude rotating targets (such as ships and airplanes) is remarkably enhanced, and the method is suitable for remote sensing image and aerial photographing target detection scenes.

A computer vision system provided herein may determine a condition (e.g., pose, movement, pose, orientation, shape, color, etc.) of an object using a neural network and a pair of trained modules. The pair of trained models may be trained using progressive learning to convert the graph representation of the object into an up-sampled grid representation of the object. The graph representation may include one or more nodes and one or more edges. The node may represent a feature of the object. The edge may connect the two nodes and represent a topological relationship between the two features represented by the two nodes. The grid representation may include grid cells arranged in a grid structure. The grid structure may be a two-dimensional structure in which grid cells are arranged in rows and columns. A grid representation of the object may be input into a neural network, and the neural network may output information indicative of the determined condition of the object.

The present application relates to the field of computer vision, and particularly relates to a method for obtaining an offline quantization calibration set of a face detection model, after obtaining a face detection dataset, each sample in the face detection dataset is classified according to different scenes, and the scene quality label, the face density label, the face average area label, the face average orientation label and the face average quality label corresponding to each sample in the face detection dataset are determined, then a plurality of samples meeting the requirements are screened out from the face detection dataset to form the offline quantization calibration set of the face detection model. By marking and storing each sample in the face detection dataset, the present application automatically selects a plurality of representative and diverse samples to form the offline quantization calibration set of the face detection model, which greatly improves the work efficiency compared with manually preparing the offline quantization calibration set of the face detection model, and is simple to operate and has strong scalability.

The invention is suitable for the technical field of computer vision, and provides a time self-correlation imaging method and system based on an adaptive orientation space window. The speckle contrast imaging and self-correlation imaging method comprises the following steps: acquiring continuous multi-frame speckle images; the method comprises the following steps: presetting a basic space window by taking a target pixel as a center, and giving a plurality of orientations; selecting a certain frame of speckle image, respectively extracting speckle light intensity sequences of corresponding orientations in the basic space window, and calculating the spatial domain variance of the light intensity sequence of each orientation; selecting the orientation with the maximum light intensity variance, and determining the orientation as an effective calculation window of the target pixel; and performing time autocorrelation function calculation based on the effective calculation window so as to solve a blood flow velocity parameter of the target pixel. According to the method, the noise of the blood flow area can be reduced, and the real-time monitoring requirement is met while the high spatial resolution is guaranteed.

The present disclosure provides a multi-angle facial data acquisition method, apparatus, device, and storage medium, which relates to the field of image processing technology, specifically to the technical fields of computer vision, artificial intelligence, autonomous driving, data acquisition, and facial recognition. A specific implementation scheme is as follows: acquiring a current image acquired by a camera and determining a first facial orientation of a face in the current image; when the first facial orientation is a frontal orientation, acquiring facial feature values ​​of the face in the current image to obtain a frontal feature value; after obtaining the frontal feature value, continuing to acquire images acquired by the camera and determining a second facial orientation of the face in the current image until acquiring facial feature values ​​of a face in an image corresponding to a second facial orientation of at least one side face orientation to obtain at least one side face feature value; based on the frontal feature value and at least one side face feature value, determining and saving the target facial feature value, thereby achieving multi-angle facial data acquisition.

The disclosure provides a method, device, electronic equipment, computer readable storage medium and computer program product for determining a guide point of a point of interest, and relates to the technical field of artificial intelligence such as computer vision, intelligent transportation and electronic map. A specific embodiment of the method comprises: acquiring a plurality of initial selection images within a preset distance range of a point of interest and corresponding shooting coordinates and shooting angles, then determining an orientation angle of the point of interest, and determining an initial selection image with the highest voting score as a first target image and an initial selection image closest to the point of interest in the orientation angle as a second target image, in response to the similarity between the first target image and the second target image exceeding a similarity threshold, determining a guide point of the point of interest based on the projection point coordinates of a fusion image of the first target image and the second target image. The embodiment can accurately determine the guide point of the point of interest based on the scene image around the point of interest.

The invention discloses an image processing method, system and device and a computer readable storage medium, and relates to the technical field of computer vision and target detection, and the method comprises the steps: obtaining a to-be-detected image; in the to-be-detected image, determining an initial orientation bounding box of the to-be-detected object; determining a main axis direction of the to-be-detected image and a stability coefficient of the main axis direction; generating a critical deflection angle of the initial directional bounding box based on the stability coefficient; determining a direction included angle between the initial directional bounding box and the main axis direction; and in response to the fact that the direction included angle is smaller than the critical deflection angle, deflecting the initial orientation bounding box to the main axis direction to obtain a target orientation bounding box of the to-be-detected object. The stability coefficient reflects the deflection strength in the main axis direction, so that the critical deflection angle is matched with the deflection strength in the main axis direction, the accuracy is higher, then the target orientation bounding box consistent with the main axis direction can be obtained, the direction of the to-be-detected object can be adaptively adjusted, and the stability and precision of direction detection of the image are improved.

The present application belongs to the technical field of helicobacter pylori identification, and discloses a helicobacter pylori intelligent identification method and system based on computer vision processing; including: collecting a gastric mucosa sample, obtaining an HP fluorescent staining image through staining treatment; performing split processing on the HP fluorescent staining image to obtain multiple sub-pictures, and taking the coordinate information of a predetermined orientation of each sub-picture as first coordinate information; performing multi-process operations on each sub-picture to generate a first sub-picture with a rectangular frame and a confidence level; splicing and combining the position coordinates of the rectangular frame in the first sub-picture with the first information coordinates on the corresponding sub-picture to generate a labeled image; comprehensively analyzing the labeled image and outputting a structured detection report. Through distance transformation and adhesion separation of the watershed, and consistent double-layer verification, the present application can effectively reduce the false detection rate of various artifacts such as bright spots, debris, bubbles and the like, thereby improving the accuracy and robustness of HP bacterial body identification.

This application relates to the field of computer vision technology and discloses a method, apparatus, device, and storage medium for constructing a three-dimensional scene. The method comprises: acquiring data of a real three-dimensional scene to obtain image data and point cloud data; performing target detection based on the image data and point cloud data to determine the object category and three-dimensional bounding box of each object in the real three-dimensional scene; for each object, determining target image data including the object from the image data; generating a first three-dimensional model of the object based on the object category and target image data of the object; adjusting the size of the first three-dimensional model of the object based on the size information carried by the three-dimensional bounding box of each object to obtain a second three-dimensional model of the object; and constructing a virtual three-dimensional scene corresponding to the real three-dimensional scene based on the position information and orientation angle information carried by the three-dimensional bounding box of each object and the second three-dimensional model of the object. This application enables the rapid and accurate construction of three-dimensional scenes.