27 results about "Local feature descriptor" patented technology

The invention discloses a point cloud data splicing method, which comprises the following steps of: aiming at point cloud data splicing of an initial pose, performing pre-splicing by adopting a nearest point iterative cutting method combining local features and a nearest point distance, and then performing further accurate splicing on a pre-splicing result by adopting a point-to-surface nearest point iterative algorithm. And thus, an accurate and complete point cloud data splicing model is obtained. The method comprises the following specific steps: scanning a three-dimensional entity to obtain original point cloud data; performing noise reduction processing on the original point cloud data to obtain a to-be-spliced point cloud; calculating a local feature descriptor of each position point in the to-be-spliced point clouds, and obtaining a local feature of each position point; pre-splicing the to-be-spliced point clouds based on the local features of the position points to obtain a pre-splicing result; and the pre-splicing result is precisely spliced, and a point cloud data splicing model is obtained.

The invention discloses a non-directional dynamic patrol street lamp visual coding method and system. The method comprises the following steps: acquiring continuously acquired road images and geographic position and attitude data thereof, and grouping after primary processing and spatial index construction; screening candidate image pairs by calculating image similarity, extracting multi-dimensional features of street lamps and environments thereof, constructing local feature descriptors including street lamp bodies and environment context features, and forming small-range environment fingerprints to determine successfully matched street lamp entities; hybrid codes are generated for the entities, and the codes are combined with geographic coordinates, the fused local feature descriptors, the fused fixed object list and the fused identifier of the nearest fixed object distance, and a final code value is obtained through Hash calculation. By implementing the method provided by the invention, visual features, small-range environment fingerprints and spatial priori knowledge can be effectively fused, so that the problems are solved, and intelligent management of urban lighting facilities is promoted.

This invention discloses a point cloud registration method and system based on statistical local feature description and matching, relating to the field of point cloud registration. The method includes: determining the local feature descriptor of each query point and the local feature descriptors of its corresponding neighboring points in 3D point cloud data; statistically weighting the local feature descriptor of each query point and the local feature descriptors of its corresponding neighboring points to obtain the statistical local feature descriptor of the corresponding query point; acquiring the statistical local feature descriptors of the source point cloud and the target point cloud; and improving the ICP algorithm based on the feature differences between point pairs in the source point cloud and the target point cloud and the average matching distance to perform point cloud registration. This invention can improve the accuracy and robustness of point cloud registration.

This invention provides a method, apparatus, and electronic device for vehicle exterior image registration. The method includes: determining a target region from a 3D point cloud corresponding to a template image; determining a region to be matched from a 3D point cloud corresponding to an image to be detected; obtaining a first local feature descriptor corresponding to points in the target region and a second local feature descriptor corresponding to points in the region to be matched; obtaining a first transformation matrix based on the first and second local feature descriptors; obtaining a third transformation matrix based on the first transformation matrix; performing a two-dimensional perspective transformation on the third transformation matrix to obtain a pseudo-3D transformation matrix corresponding to the 2D image to be detected; and transforming the 2D image to be detected using the pseudo-3D transformation matrix to obtain the registered image to be detected. This invention can accurately register 2D images to be detected, improving the accuracy of vehicle exterior image detection.

The invention relates to the technical field of machine vision detection, in particular to an inner package detection and recognition method based on vision detection, which comprises the following steps: synchronously acquiring a multi-view image sequence of a to-be-detected piece through an annularly arranged acquisition device; independently analyzing each image, and generating an initial defect area mask and a local feature descriptor; a three-dimensional defect space distribution model is constructed by fusing multi-view masks, and geometric topology and depth information of the three-dimensional defect space distribution model are extracted to form a comprehensive feature vector. And performing cross-modal correlation analysis on the local features and the three-dimensional comprehensive features to generate an enhanced defect feature spectrum. And calling a pre-training classification model to analyze the atlas, outputting an inner package type, a defect type and a severity level, and generating a quality evaluation report and a processing instruction according to the inner package type, the defect type and the severity level. According to the method, three-dimensional space accurate positioning and multi-feature deep fusion of the defects are realized, and the recognition accuracy and evaluation comprehensiveness of the complex defects are improved.

The invention belongs to the technical field of image processing, and relates to a protection method and system for the design copyright of an automobile cushion. The method comprises the following steps of: extracting a digital image contour of an automobile cushion design drawing, calculating a normalized Fourier descriptor of the contour as a shape feature, dividing a drawing area into grid units according to radial sectors and concentric rings, converting a digital image into a CIELAB color space in each grid unit, and for each grid unit, carrying out CIELAB color space conversion on the CIELAB color space; extracting a gradient direction histogram of a brightness L * value taking gradient intensity as a weight and a quantization histogram of statistical a * b * two-dimensional color distribution of pixels in the grid unit, combining the two histograms into a local feature descriptor of the grid unit, serializing and splicing the local feature descriptor from inside to outside, and combining shape features with a local feature sequence to form copyright feature data, and performing digital signature by using an asymmetric encryption private key, and storing the signature and a timestamp in a distributed account book together to realize copyright protection.

This invention discloses a global localization method based on multi-layer 3D point cloud bird's-eye view, belonging to the field of mobile robot localization and navigation technology. The method first divides the LiDAR point cloud into multiple overlapping slices along the height direction and projects them to generate a multi-layer bird's-eye view. Then, the multi-layer bird's-eye view is input into a global localization network containing a shared feature extraction module, a vertical slice attention module, a slice feature fusion module, and a NetVLAD aggregation module to extract global and local feature descriptors. Finally, a reference frame is obtained through global feature retrieval, and the pose of the query frame is calculated by combining keypoint matching and random sampling consensus algorithms. This method can preserve the vertical structure information of the point cloud, reduce localization ambiguity in complex scenes, and balance localization accuracy and computational efficiency.

The application discloses a loop closure detection method based on block feature uniform weighting and distance sorting in outdoor complex environment, and belongs to the technical field of visual camera simultaneous localization and mapping and computer vision. The technical scheme of the application comprises the following steps: performing gray processing and Gaussian filtering pretreatment on the collected image pair, uniformly dividing the image into a plurality of sub-image blocks, extracting local feature descriptors from each sub-image block, calculating the distance between the feature vectors of the corresponding sub-image blocks based on cosine similarity, uniformly weighting the feature similarity of all sub-image blocks, sorting the calculated feature distances of all sub-image blocks from small to large, screening out the key matching block with the smallest distance by setting a threshold to eliminate the false matching block caused by illumination change noise, and comprehensively determining the global similarity of the feature distance of the key matching block to confirm the success of loop closure detection and output loop closure information to optimize the global pose of the visual camera.

The application discloses a kind of multi-modal remote sensing image registration method, system, terminal equipment and storage medium, based on the structure similarity between multi-modal remote sensing image pair, local feature descriptor (edge feature) is extracted using pre-trained edge detection network, and feature matching is carried out using traditional template matching method, accurate and efficient registration under multiple modal images is realized.The application obtains higher matching accuracy and better robustness in multi-source image matching.

The application discloses a non-directional dynamic patrol street lamp visual coding method and system. The method comprises the following steps: acquiring continuous road image and its geographical position and attitude data, grouping after preliminary processing and spatial index construction; screening candidate image pairs by calculating image similarity, and extracting multi-dimensional features of street lamps and their environment, constructing local feature descriptor including street lamp body and environmental context features, forming small-range environmental fingerprint to determine the matching successful street lamp entity; generating hybrid coding for the entity, which combines geographical coordinates, fused local feature descriptor, fused fixed object list and fused identifier of the distance from the nearest fixed object, and obtaining the final coding value through hash calculation. Through the implementation of the method of the application, visual features, small-range environmental fingerprint and spatial priori knowledge can be effectively fused to overcome the above problems and promote the intelligent management of urban lighting facilities.

The present invention relates to the technical field of question composition, and discloses an intelligent composition method for medical theory examinations based on artificial intelligence technology, comprising the steps of: question information preprocessing, question similarity calculation, and intelligent composition. In the question processing link, questions are decomposed and structured according to multiple modalities such as text, image, and case analysis, and features are extracted by combining techniques such as keyword extraction and local feature descriptor methods. In question similarity calculation, cosine similarity and weighted summation are used to accurately quantify the similarity of questions of different question types, effectively avoiding question duplication in the examination paper. When composing the examination paper, questions are screened according to preset question type structure, knowledge point coverage, difficulty level and other parameters, and similarity threshold judgment and replacement mechanism are combined to ensure a reasonable difficulty distribution and comprehensive coverage of knowledge points. The replacement object is determined according to the number of historical usage of the question, thereby improving the scientific nature of the composition and the utilization rate of the question bank resources, and optimizing the composition process of the medical theory examination.

The application relates to a pavement crack identification method based on point cloud-RGB heterogenous image multi-stage registration mapping. The method comprises the following steps: collecting pavement point cloud data and pavement image data for time and space synchronization, creating a projection image pair for feature extraction, obtaining a local feature descriptor, matching feature points in the local feature descriptor, obtaining an actual matching point pair, solving the parameters of a direct linear transformation equation according to the actual matching point pair, obtaining the mapping relationship between the RGB image pixel coordinates and the three-dimensional point cloud coordinates, traversing the coordinates of each point in the pavement image data, assigning the mapping relationship depth information to the pavement image data, obtaining a point cloud projection image, generating a depth image from the point cloud projection image, labeling cracks and backgrounds, constructing a data set, training a crack identification model using the data set, obtaining a trained crack identification model, and identifying pavement cracks, thereby improving the efficiency and accuracy of automatic crack identification.

This invention belongs to the field of computer vision technology and discloses a local feature extraction method. A novel local feature learning network framework is established, using a fully convolutional network L2-Net as the backbone. Features from the spatial structure enhancement module are extracted and fused into the intermediate and deep layers of the backbone network. A dataset with depth information is used as the training set. The correspondence between two images is obtained through the inherent depth information of the dataset, serving as the ground truth for local feature discriminative and robust training. The novel local feature learning network framework outputs feature heatmaps for discriminative training and feature detection heatmaps and local feature descriptors for robust training. This invention balances robustness and discriminability; the network still contains rich structural information in its deep layers, thereby improving discriminability. A joint reinforcement learning method is used to ultimately obtain better local feature descriptors. Finally, this method was evaluated on a feature matching task and achieved state-of-the-art results.

The invention relates to a quick identity authentication method and device based on physical unclonable features, equipment and a medium, and the method comprises the steps: taking a query feature descriptor as a retrieval object, and carrying out approximate nearest neighbor matching retrieval in a local feature descriptor set of a global feature library, after screening, determining one or more candidate physical unclonable tag identities through voting; for candidate physical unclonable tag identities, corresponding registered key point space coordinates are called from a geometric feature library, and a re-projection error threshold is combined through a random sampling consistency algorithm; checking whether the space coordinate of the query key point and the space coordinate of the registered key point of the candidate physical unclonable tag meet a preset geometric transformation model or not, and calculating a geometric consistency score according to the preset geometric transformation model; and if it is detected that the geometric consistency score is greater than or equal to a preset score threshold, outputting an identity authentication success result. According to the invention, the identity authentication efficiency is greatly improved, and the false rejection rate of identity authentication is remarkably reduced.

This invention presents an image retrieval method based on the fusion of attention enhancement and autoencoding. It uses an improved ResNet50 network to extract global and local feature maps, obtains global and local feature descriptors based on these maps, calculates image similarity, and derives the target image through similarity comparison. This image retrieval method improves upon traditional residual blocks by employing automaton encoding, effectively unifying local and global features into a single network. It uses an attention mechanism to extract regions of greater interest, avoiding algorithmic overhead and resulting in faster retrieval speed and higher accuracy.

The application belongs to the technical field of three-dimensional point cloud processing, and particularly relates to a VGICP point cloud registration algorithm based on an FPFH feature descriptor. The application comprises the following steps: 1) obtaining point cloud data and performing initialization processing on the point cloud data; 2) calculating normal vectors of each point in the point cloud data, thereby constructing a local feature descriptor of a histogram; 3) performing key point detection on all the point cloud data, thereby obtaining adjacent points; 4) extracting features represented by each point and adjacent points from the point cloud data; 5) performing coarse registration on source point cloud according to the extracted features; 6) performing voxelization on target point cloud; and 7) performing fine registration on the source point cloud. The application is aimed at selection and improvement of a point cloud registration algorithm, so that the speed of point cloud registration is accelerated and the accuracy is increased.

The invention provides an ROS trolley 3D reconstruction method and system based on lightweight deep learning. The ROS trolley 3D reconstruction method comprises the steps of S1, data preprocessing, wherein original data of a sensor are received and preprocessed; the sensor original data comprises laser radar point cloud data and RGB-D camera data; s2, lightweight feature extraction: performing multi-view projection processing and back projection mapping on the preprocessed point cloud data to obtain a local feature descriptor of each point cloud; s3, point cloud registration and key frame management: searching historical key frame features nearest to the current frame features and obtaining feature point matching pairs for coarse registration and fine registration; judging whether the current frame feature is a key frame or not; s4, incremental map construction: updating a local map based on the obtained new key frame, managing the key frame by adopting a sliding window strategy, and executing closed-loop detection and global optimization; and S5, visualization and interaction. The method effectively balances the contradiction between precision and efficiency, and is strong in resource adaptability and high in engineering practicability.

The invention discloses a cross-modal image feature matching model, a cross-modal image feature matching method and electronic equipment. Wherein the cross-modal image feature matching model fuses a local feature descriptor generated by a neural network with a high-dimensional semantic feature map which comes from a visual basic model and encodes high-level semantic context information through a feature fusion module, and through the fusion strategy, a novel mixed feature descriptor is generated; the mixed feature descriptors have the sub-pixel-level positioning precision of local features and the strong robustness of a semantic feature map to modal changes at the same time, so that a high-quality initial corresponding relation can be more effectively established between images of different modals, and the huge visual domain difference caused by different imaging technologies can be effectively overcome; the initial matching quality is obviously improved; in combination with a learning type geometric confidence module, wrong matching caused by complex non-rigid deformation can be effectively filtered out, and finally the matching precision far higher than that of an existing method is achieved.

The application relates to a kind of three-dimensional target identification methods based on laser radar, first is to construct point cloud model library, and the model point cloud is extracted using ISS algorithm key point;Then global target identification is carried out;On this basis, whether chi-square distance is less than the set threshold value is judged, if initial identification fails, that is, chi-square distance does not have the target meeting the requirement, turn into using local feature descriptor to carry out target identification, the key point of scene point cloud is extracted and PDSH feature is calculated, and local feature matching is carried out using Euclidean distance, finally constructs descriptor, carries out error matching pair elimination to corresponding feature descriptor and votes and identifies the final target.The application realizes local feature extraction of point cloud by the proposed PDSH local feature descriptor, and realizes three-dimensional target identification by combining the proposed PDSH local feature descriptor with GASD global feature descriptor, which guarantees the correctness of identification and speeds up the identification speed.

The invention discloses a finger vein recognition method and device, a storage medium and equipment, and belongs to the field of biological feature recognition. The method comprises the following steps: firstly, carrying out rotation alignment on a finger vein image through a finger center line, obtaining a region of interest, then obtaining an optimal matching region through a sliding window mode based on Gabor binary coding, and finally, carrying out local feature descriptor extraction and matching on the optimal matching region. According to the invention, the finger vein recognition of the local feature point and feature descriptor method is carried out through the finger midline registration and sliding matching strategy, the invalid comparison is greatly reduced, the effective local descriptor is concerned, and the matching efficiency is greatly improved.

The invention discloses a forest pruning auxiliary decision-making method and system based on artificial intelligence, and relates to the field of forest visual analysis. Images and point cloud data of a target tree are collected through a camera shooting and laser radar device; matching a tree species database by using a target detection model, extracting a target morphological feature, identifying a pruning area, performing feature description and difference comparison by combining a Sobel operator, judging a pruning probability and extracting a two-dimensional image feature; performing three-dimensional feature extraction on the point cloud data by adopting an FPFH local feature descriptor, evaluating differences in combination with morphological features, and generating three-dimensional point cloud features; the two-dimensional and three-dimensional features are subjected to weighted fusion through a DCA feature fusion algorithm, a decision-making tree decision-making model is constructed after the pruning probability is associated, and efficient judgment and decision-making assistance of a pruning mode are carried out on real-time tree data. According to the method, the multi-modal data and the intelligent algorithm are fused, and the accuracy and the automation level of pruning decision making are improved.

The application discloses a kind of based on 3D point cloud ship model fine grain feature identification method, including feature extraction step and category prediction step;The feature extraction step includes the following substeps: obtaining the ship depth image collected by three-dimensional sensor;Conversion into 3D point cloud data;3D point cloud data is sampled to feature point;All feature points obtained by sampling are sampled with K adjacent feature points of fixed number using K nearest neighbor algorithm, as the local region with the feature point as midpoint;Local feature descriptor is used to describe the geometric feature of local region, i.e. fine grain feature, to obtain local feature;High-dimensional feature is obtained;Weight matrix is obtained;The weighted feature of corresponding local region is obtained.The application directly uses point cloud as input, and uses MLP multilayer perceptron to train a permutation matrix, to weight the point cloud, to realize the invariance of permutation, and then uses convolutional neural network for identification;Local feature descriptor is used to describe the fine grain feature of ship in detail.

The application discloses a weak inertial navigation dependence difference mapping and track mapping method, comprising the following steps: S1, data acquisition: using a data acquisition system to collect and acquire point cloud data around a track; S2, defining a system coordinate system L and a world coordinate system W; S3, track point cloud mileage estimation: point cloud re-projection; extracting a local feature descriptor; calculating a pose transformation matrix; calculating a pose transformation matrix; converging the pose transformation matrix and the pose transformation matrix to obtain a final pose transformation matrix; iterative convergence solving; S4, mapping track mapping: S41, world coordinate system W unification; S42, track prior mapping. The method has the advantages of high sparse tolerance, low drift, low complexity and high detail restoration degree, greatly reduces the dependence on auxiliary hardware, and has higher track scene reconstruction accuracy, economic efficiency and application prospect.

This invention relates to the field of industrial machine vision inspection technology, specifically disclosing an intelligent detection system for surface defects in mechanical parts. The system acquires bright-field and dark-field image sequences of mechanical parts, and filters valid images based on image sharpness and integrity evaluation. It aligns and corrects the initial images with a standard template through multi-scale spatial registration. Based on the structural tensor, it constructs a local feature descriptor, calculates the geodesic distance of the Riemannian manifold to construct a global differential energy field, and solves for a dense motion vector field by functional inversion including data fidelity terms and vector field regularization terms. Geometric deformation normalization is achieved through vector field structure enhancement and divergence-curl decomposition. Finally, multi-directional contour wave transform is used to enhance gradient features, and adaptive threshold segmentation and morphological tomography are combined to generate a defect segmentation map. This invention effectively eliminates the interference of part posture differences and systematic deformation, achieving accurate identification and quantitative analysis of surface defects.

A self-adaptive virtual-real registration method based on a semantic environment map relates to the technical field of virtual-real registration, and comprises the following steps: a cloud server pre-constructs and stores a semantic environment map; wherein the semantic environment map comprises a plurality of semantic element nodes; the terminal device captures real-time environment sensing data of the current view; the terminal equipment performs local processing on the real-time environment sensing data; the terminal device uploads the semantic tag and the local feature descriptor to a cloud server; the cloud server calculates initial pose estimation of the terminal equipment; the cloud server solves the high-precision six-degree-of-freedom pose of the terminal equipment based on the initial pose estimation; and the cloud server issues the high-precision six-degree-of-freedom pose back to the terminal device, and the terminal device completes superposition rendering. According to the invention, high-precision real-time registration in a large-range unmarked environment can be realized without relying on pre-marking; and unified coordinate framework and natural collaborative interaction among multiple devices is realized.

The application provides a bridge structure crack position identification method bridging deep learning and computer vision, comprising: acquiring multi-view visible light images and depth images, fusing the visible light images and the depth images according to registration parameters to generate a fusion image containing texture and depth information of a bridge; for the fusion image, a self-adaptive threshold segmentation algorithm is used to extract a crack region, a threshold size is dynamically adjusted, and a crack binary image is obtained; according to the crack binary image, a three-dimensional coordinate of a crack pixel point is calculated through a three-dimensional reconstruction algorithm, and three-dimensional point cloud data containing spatial position and morphological information of the crack are reconstructed; based on the three-dimensional point cloud data, a three-dimensional local feature descriptor is used to describe the geometric properties of the crack surface, crack segment parameters are calculated, and a three-dimensional crack model is constructed; based on a deep learning model, a transfer learning strategy is used to form an adaptive and transferable three-dimensional bridge crack detection method, and model parameters are updated to adapt to characteristics of a target bridge.

The present invention discloses a method, system and medium for creating a three-dimensional feature descriptor based on local surface change information. The method is used to realize the joint encoding of local surface geometry and spatial information, including local feature descriptor calculation, encoding the geometry and spatial information of the neighborhood of key points in the point cloud into a high-dimensional vector; correspondence estimation, finding matching point pairs on different point clouds based on the similarity of feature descriptors, and filtering out erroneous matching points using certain constraint screening conditions. The present invention establishes an LRA on the local surface of the point cloud, and then simultaneously encodes the spatial and geometric information of the local surface of the point cloud. The spatial information is encoded by radially dividing the local space on the LRA; the geometric information is encoded by counting five geometric attributes with strong robustness, so that the LSVSH feature descriptor shows the best performance in various evaluation indicators, while achieving a good balance in efficiency, descriptiveness and robustness.