115results about How to "Reduce false match rate" patented technology

The invention discloses a binocular stereoscopic vision matching method combining depth characteristics. The binocular stereoscopic vision matching method comprises: obtaining a depth characteristic pattern from left and right images through a convolutional neural network; calculating a truncation similarity measurement degree of pixel depth characteristics by taking the depth characteristics as the standard, and constructing a truncation matching cost function combining color, gradients and depth characteristics to obtain a matched cost volume; processing the matched cost volume by adopting a fixed window, a variable window and a self-adaptive weight polymerization or guide filtering method to obtain a cost volume polymerized by a matching cost; selecting an optimal parallax error of the cost volume by adopting WTA (Wireless Telephony Application) to obtain an initial parallax error pattern; then finding a shielding region by adopting a double-peak test, left-right consistency detection, sequence consistency detection or shielding constraint algorithm, and giving a shielding point to a parallax error value of a same-row point closest to the shielding point to obtain a parallax error pattern; and filtering the parallax error pattern by adopting a mean value or bilateral filter to obtain a final parallax error pattern. By adopting the binocular stereoscopic vision matching method combining the depth characteristics, the incorrect matching rate of three-dimensional matching can be effectively reduced, the images are smooth and image edges including edges of small objects are effectively kept.

The invention discloses an image stitching method based on overlapping region scale-invariant feather transform (SIFT) feature points and belongs to the technical field of image processing. Aiming at the problems that the algorithm computation is large and subsequent matching error and computing redundancy are easily caused due to the non-overlapping region features because of extraction of the features of the whole image in the conventional image stitching algorithm based on features, the invention provides an image stitching method based on the overlapping region SIFT feature points. According to the method, only the feature points in the image overlapping region are extracted, the number of the feature points is reduced, and the algorithm computation is greatly reduced; and moreover, the feature points are represented by employing an improved SIFT feature vector extraction method, the computation during feature point matching is further reduced, and the mismatching rate is reduced. The invention also discloses an image stitching method with optical imaging difference, wherein the image stitching method comprises the following steps of: converting two images with optical imaging difference to be stitched to a cylindrical coordinate space by utilizing projection transformation, and stitching the images by using the image stitching method based on the overlapping region SIFT feature points.

A mobile robot positioning method based on stereoscopic vision specifically comprises the following steps: shooting an image sequence through a binocular camera mounted on a robot; extracting scale invariant features in the stereo image sequence, matching and tracking the features through a circle matching technique; solving rotating translation matrixes among adjacent frames through a unit quaternion method; performing iterative optimization for multiple times through a minimum re-projection error method to obtain an optimal motion parameter. According to the invention, for the condition that the GPS and other traditional positioning methods are in failure in a certain environment, the positioning method based on stereoscopic vision is provided; for the problem that wrong matching easily appears during feature matching, the circle matching technique is provided to match and track the features, so that the accuracy of feature matching is improved; since the unit quaternion method is adopted to solve the rotating translation matrixes, computational accuracy is improved; the minimum re-projection error method is provided finally to obtain the relatively accurate motion parameter by performing iterative optimization for multiple times.

The invention discloses a binocular three-dimensional reconstruction method comprising the following steps of: 1) acquiring an image of an object to be reconstructed by two image acquisition apparatuses with the same model in order to obtain a left image and a right image; 2) calibrating the image acquisition apparatuses by a chessboard method, computing the internal and external parameter and a lens distortion coefficient, processing the left image and the right image to remove distortion in the images; 3) extracting features from the two processed images in the step 2) to obtain the feature points of the two images; 4) matching the feature points by using the feature points of the two images in the step 3) to obtain feature point pairs; 5) performing epipolar geometric constraint detection on the obtained feature point pairs to remove mismatched feature point pairs; and 6) computing three-dimensional coordinates of corresponding points, in a world coordinate system, of the obtained feature points by using the reserved feature point pairs. The binocular three-dimensional reconstruction method may reduce abnormal points and acquires an accurate three-dimensional reconstruction model.

The embodiment of the invention discloses a team battle matching method and server, and relates to the field of the Internet and games. The mismatching rate of teams can be reduced and the matching efficiency of team battles can be improved. The method comprises the following steps: firstly, configuring teams with the same team people numbers in all teams which participate in battle application to the same queues; then selecting a random team from the queue in which the number of team people configured is the maximum; judging whether the team people number of the selected team is equal to a preset number; if the team people number of the selected team is equal to the preset number, configuring the team to carry out battle.

A binocular three-dimensional reconstruction method includes the following steps: step A, image collection: using two color cameras with the same model and arranging the two color cameras horizontally in a parallel mode, and enabling an target object to be arranged within the collecting range of the two cameras, wherein a image collected by a left camera is an original left image and a image collected by a right camera is an original right image; step B, camera calibration, and image rectification and pretreatment: adopting a chessboard method to demarcate and obtain inner and outer diameters of the two cameras, and then rectifying the original left image and the original right image, and next, pretreating the images; step C, hierarchical matching: conducting hierarchical feature extraction and matching to the images according to strong and week of feature lines of the images; step D, three-dimensional reconstruction: after completing the delamination which reaches required precision, according to a parallax principle, enabling obtained matching information of the two images to be calculated into three-dimensional information of the target object, adopting a method of plane interpolation, and then completing the three-dimensional reconstruction. The method has the advantages of being low in algorithm complexity and capable of achieving the three-dimensional reconstruction well and the like.

The invention discloses a stereo matching algorithm for computer vision, which is implemented based on a fully connected neural network and Edge-aware Disparity Propagation (EDP). The stereo matchingalgorithm comprises the steps of: firstly, calculating the initial matching cost of an input stereo image pair through a fully connected neural network, and using the WTA algorithm to find a corresponding disparity value, and generating an initial disparity map; performing consistency detection, and using the EDP algorithm and geodetic distance filtering to reconstruct the matching cost of a disparity inconsistent region, obtaining a new disparity value of the inconsistent region, and filling the new disparity value into a hole disparity map to obtain a complete disparity map; and finally, generating a final disparity map through sub-pixel enhancement optimization. Experimental values show that the algorithm can effectively reduce the mismatch rate of stereo matching and improve the accuracy of the disparity map, has lower mismatch rate especially in a non-occlusion region.

The invention relates to a laser-based amphibious three-dimensional visual detection device and a detection method, and belongs to the technical field of three-dimensional visual detection devices. The invention is an amphibious device capable of measuring a three-dimensional object and obtaining the position and attitude of the object by matching dot-line lasers to provide active features and combining land binocular stereo vision measurement with underwater multi-feature structured light measurement. According to the method, 25 pieces of line laser and 1 piece of dot laser are successfully taken as features, the technical problem of binocular stereo vision passive measurement can be solved, and meanwhile, the dot laser is taken as a marker to match the remaining light bars, the misidentification of the light bars caused by the underwater complex environment can be avoided, and the matching accuracy can be improved. Each picture can obtain 25 times more data than traditional structured light measurement, so that the underwater clear object feature data can be obtained, and the three-dimensional position and attitude under various complex conditions such as complexity, high occlusion, point cloud sparseness and the like can be solved with high accuracy.

The invention relates to a panorama stitching method for removing a moving object in a moving video, comprising the following steps: (1) acquiring an appropriate number of video screenshots according to the length of a video, wherein the screenshot selection interval is shorter is the video is shorter, and the screenshot selection interval is longer if the video is longer; (2) projecting an original image to a sphere in order to prevent a final panorama from distortion and deformation; (3) carrying out SURF feature point detection on each frame of image to obtain a feature point description sub vector as the basis of matching; (4) carrying out feature point matching; (5) calculating a perspective (homographic) transformation matrix of every two frames of images based on matching feature points, and calculating forward and reverse homographic matrix transformation of two adjacent frames; (6) obtaining the size of the panorama based on the homographic transformation matrix of adjacent frames; and (7) inversely mapping the panorama to a sequence frame, building a candidate point list for each pixel point of the panorama, and removing a moving object, thus, completing generation of the panorama. The resolution of panorama generation is high, background overlay is accurate, and the 'ghost' phenomenon of the moving object is avoided.

The invention discloses an AUV multi-strategy navigation method based on submarine topography matching. The method comprises the steps: constructing a topography adaptive region selection model basedon a machine learning algorithm, wherein the topography adaptive region selection model includes a suitable topography matching region, a topography matching transition region, and an unsuitable topography matching region; during specific navigation, analyzing a type of a navigation area through collected real-time data, and carrying out navigation based on a multi-beam sonar underwater topographyelevation image matching auxiliary method for the suitable topography matching region; for the topography matching transition region, starting a side-scan sonar, and carrying out underwater side-scansonar image matching by using a multi-beam depth finder and the side-scan sonar data to assist ship position calculation for navigation; and for the unsuitable topography matching region, starting the side-scan sonar, and carrying out navigation based on simultaneous localization and a map construction algorithm. The navigation method has better significance and value in practical application.

The present invention relates to an image synthesis restoration method based on local structure features, comprising the following steps: inputting an image to be restored; initializing confidence coefficients of all pixel points in the image to be restored; selecting a template block of each pixel point by respectively taking each pixel point on a damaged boundary of the image to be restored as the center; calculating the priority of the template block where each pixel point on the damaged boundary of the image to be restored locates, and performing descending sort; searching an area which has the highest similarity with the template block having the greatest priority from an undamaged area in the image to be restored to serve as an optimal matching block; copying pixel values in the optimal matching block to the template block; and updating the confidence coefficients and returning to execute corresponding steps until the image to be restored is completely restored. The image synthesis restoration method based on local structure features provided in the present invention can improve image restoration accuracy for a damaged image with a complicated texture structure. The present invention also relates to an image synthesis restoration system based on local structure features.

The invention discloses a novel self-adaptive weight stereo matching method. The novel self-adaptive weight stereo matching method comprises: obtaining a reference image and a target image; obtaininga representation method for each pixel supporting weight value; improving the traditional self-adaptive algorithm, selecting a gray scale absolute difference in a CIELab color space to replace a grayscale absolute difference in a RGB space; using the similarity measure method of Census nonparametric transformation to use a relationship between a central pixel point and pixels in the neighborhoodto replace a gray value of the central pixel point; and finally performing parallax post-processing of left and right consistency detection, sub-pixel enhancement, and median filtering. The inventioneffectively improves the matching precision, enhances the robustness of the stereo matching, and improves the block effect generated by the traditional algorithm in a low texture region.

The invention provides a personnel indoor positioning method based on an adaptive SIFI (scale invariant feature transform) algorithm. The method is characterized by: firstly, carrying out feature extraction on an global motion image sequence; then, taking a correct matching point as a sample set to estimate a camera motion model so as to obtain an actual offset of a camera. In the method of the invention, a Lagrange parabola interpolation is introduced. Last three times of model matching results are used to predict an overlapping region of a reference frame image and a current frame image. On the overlapping region, feature points are extracted and feature matching is performed so that a lot of information redundancies in a video image sequence can be eliminated, a processing speed of the each frame image can be accelerated, the validity of the feature points to be matched can be increased and mismatching can be reduced. Therefore, the algorithm is accurate and real-time and can be used for the indoor personnel positioning system.

The invention discloses a method and system for retrieving multimedia information based on a bit vector. The method comprises the steps that the characteristic data of the current multimedia information are extracted to obtain the n-dimensional high-dimensional characteristic vector of the current multi-media information; the n-dimensional high-dimensional characteristic vector is converted through a projection matrix to obtain an m-dimensional middle vector; all the elements of an m-dimensional threshold vector are compared with the corresponding elements of the middle vector respectively, and binaryzation is carried out on the middle vector according to a comparison result to obtain the m-dimensional bit vector of the current multimedia information; m is smaller than n; according to the obtained bit vector, a bit vector similar to the bit vector is searched for in a multimedia characteristic database, and the multimedia information corresponding to the found bit vector is used for being output as a retrieved result. The method ensures original vector identification capacity, and after the high-dimensional characteristic vector of the multimedia information is mapped into a low-dimensional bit vector, the retrieval efficiency based on the bit vector is higher, and the retrieval loss based on the bit vector is smaller.

Embodiments of the present invention provide a method and an apparatus for predicting a motion parameter of a target object. According to the embodiments of the present invention, the method for predicting the motion parameter of the target object comprises: according to detection data, acquiring a first motion parameter of the target object; according to the detection data, determining a neighborhood object positioned around the target object; obtaining a distance between the target object and the neighborhood object; and according to the first motion parameter and the distance between the target object and the neighborhood object, estimating a probability density function of a second motion parameter of the target object at the next moment.

The invention discloses a neuromorphic inspiring robot cognitive map building method, and belongs to the technical field of robot environment cognition and motion navigation. A cognitive map can be built to assist a robot in navigation. Firstly, the robot acquires image information surrounding the environment through a robot. Then, a visual odometer processes the image information to obtain the rotating angle speed and the line speed of the robot, the rotating angle speed and the line speed are input to a head orientation cell model and a position cell model, and cognition of the robot to theposition is formed. A visual feature extraction module processes images through a deep learning network and a principal component analysis algorithm, and outside cognition information of the robot tothe environment is obtained; and finally, information is combined through the experience map, and the map is updated through the closed-loop detection and update algorithm. By means of the method, theproblems that in the current neuromorphic cognitive map building method, the robustness of a visual odometer is poor, and accuracy of closed-loop detection is not high enough can be solved, detectionand updating to closed-loop points in the environment can be completed, and global consistency can be guaranteed.

The invention discloses a data benchmarking method and device and a storage device. The data benchmarking method comprises the steps that original data information is extracted from a data table to bebenchmarked, and the original data information comprises field names and field annotations corresponding to the field names; identifying the field annotation based on a sequence annotation model of deep learning to obtain a characteristic word corresponding to the field name; carrying out first text matching on the characteristic words corresponding to the field names and standard data elements in a standard library; and verifying a result output after the first text is matched. By means of the mode, text matching is conducted on the basis that the feature words are recognized, the credibility of a text matching result is improved, and the mismatching rate in the benchmarking process is reduced.

The invention discloses a monocular vision mileage measuring method based on image color characteristics. The method comprises the steps of using an image acquisition device for transmitting the acquired color image sequence to a computer through a transmission interface; extracting features from an input image frame, carrying out feature matching and calculating a frame descriptor of the image; establishing an initial map point meanwhile, using two initial frames as key frames, projecting the successfully initialized map point into the current frame; carrying out bundling optimization based on a reprojection error between the two frames of images, obtaining a pose transformation relation of a camera between the two frames to track the motion of the camera, finally selecting a new key frame according to the similarity with a previous key frame and an existing key frame in a map, adding the new key frame into the map, and updating the map. The method has a more robust feature matching effect, the used equipment is simple, the cost is relatively lower, and the method has relatively higher engineering application value and a relatively wide application scene.