37results about How to "Reduce false matches" patented technology

A method and a system for progressive stereo matching of digital images representing a scene. In general, the present invention uses a progressive iterative technique that includes a disparity gradient limit principle and a least commitment strategy. Generally unambiguous pixel matches are found by beginning with a few reliable pixel matches and finding progressively more unambiguous pixel matches. Unambiguous pixel matches are used to define the search ranges for each pixel to guide matching in the current iteration. Unambiguous pixel matches then are found using a novel correlation technique and based on a correlation score associated with a pixel match. The search range is capable of being rotated, and is part of a novel correlation technique that provides a more robust estimate of pixel match reliability. Potential pixel matches found in the search ranges are tested for ambiguity and unambiguous matches are selected and added to the reliable pixel matches.

The invention provides a fingerprint minutiae matching method syncretized to global information and a system thereof. The system realizes the entire matching process by an image acquiring unit, an image pre-processing unit, a feature extracting unit, a template storing unit and a feature matching unit, which specifically comprises the following steps: from the feature extracting unit, extracting the feature including the globe information, that is, minutiae handedness, and regarding the minutiae handedness, minutiae information, and minutiae local direction description as the feature to represent the fingerprint; measuring the similarity between the minutiaes by the minutiae handedness and the minutiae local direction description; selecting several pairs of minutiaes having the greatest similarity as an initial dot pair; registering the fingerprint feature and obtaining the corresponding matching fractions with each group of initial dot pair as a reference; selecting the maximum matching fraction in matching fractions as the finial matching fraction; judging whether the input fingerprint feature and the template fingerprint feature are from the same finger based on the final matching fraction, thereby finishing the minutiae matching of the fingerprint.

A fingerprint matching method disclosed in the present invention comprises the following steps: processing the fingerprint image to be matched and the template fingerprint image to find matching feature points between the fingerprint image to be matched and the template fingerprint image; obtaining the amount of rotation and translation according to the matched feature points , and adjust the position of the fingerprint image to be matched according to the amount of rotation and translation to obtain the overlapping area of ​​the two images; compare the ridges of the pixels in the overlapping area to obtain the ridge score; compare the direction field of the pixels in the overlapping area to obtain the direction field score; calculate the comparison score according to the ridge score and the direction field score, and judge whether the two fingerprint images match according to the comparison score, thus, through the combination of the ridge recognition and the direction field comparison, the reduction of The occurrence of wrong matching reduces the false recognition rate and improves the accuracy of fingerprint identification.

The invention discloses a multi-image matching method based on low-rank tensor recovery, which includes the following steps: S1: preprocessing each frame of image and performing feature extraction, that is, extracting the feature of interest points; S2: interest of each frame of image Points are processed, and their high-order information features are extracted according to the topological relationship of the interest points; S3: Based on the multi-map cycle consistency, a multi-map high-order feature information tensor is constructed according to the global correspondence between the permutation matrix and image features; S4: Ranked Constraints as a standard, based on Alternating Direction Multiplier Method (ADMM) algorithm to solve the low-rank representation of multi-image high-order feature information tensor, can effectively calculate the corresponding optimal permutation matrix between multiple images, that is, the matching result matrix. The invention proposes a multi-image matching method based on low-rank tensor recovery, which realizes the consistency of image matching and improves the matching accuracy, and has important significance for image matching application research, target recognition and target tracking technology.

The invention discloses an ORB key frame closed-loop detection SLAM method capable of improving the consistency of the position and the pose of a robot. The ORB key frame closed-loop detection SLAM method comprises the following steps of, firstly, acquiring color information and depth information of the environment by adopting an RGB-D sensor, and extracting the image features by using the ORB features; then, estimating the position and the pose of the robot by an algorithm based on RANSAC-ICP interframe registration, and constructing an initial position and pose graph; and finally, constructing BoVW (bag of visual words) by extracting the ORB features in a Key Frame, carrying out similarity comparison on the current key frame and words in the BoVW to realize closed-loop key frame detection, adding constraint of the position and pose graph through key frame interframe registration detection, and obtaining the global optimal position and pose of the robot. The invention provides the ORB key frame closed-loop detection SLAM method with capability of improving the consistency of the position and the pose of the robot, higher constructing quality of an environmental map and high optimization efficiency.