1706 results about "Feature matching" patented technology

A camera system comprises an image capturing device, object detection module, object tracking module, and match classifier. The object detection module receives image data and detects objects appearing in one or more of the images. The object tracking module temporally associates instances of a first object detected in a first group of the images. The first object has a first signature representing features of the first object. The match classifier matches object instances by analyzing data derived from the first signature of the first object and a second signature of a second object detected in a second image. The second signature represents features of the second object derived from the second image. The match classifier determine whether the second signature matches the first signature. A training process automatically configures the match classifier using a set of possible object features.

Fingerprint image processing methods for fingerprint security systems, for example, in wireless communications devices, including capturing fingerprint images (320), identifying features from images (330), matching the identified features with reference features (340), and providing access to the device based upon feature matching (350) results. In one embodiment, minutiae features are extracted from the image and matched to reference minutiae features.

A system and process for identifying corresponding points among multiple images of a scene is presented. This involves a multi-view matching framework based on a new class of invariant features. Features are located at Harris corners in scale-space and oriented using a blurred local gradient. This defines a similarity invariant frame in which to sample a feature descriptor. The descriptor actually formed is a bias/gain normalized patch of intensity values. Matching is achieved using a fast nearest neighbor procedure that uses indexing on low frequency Haar wavelet coefficients. A simple 6 parameter model for patch matching is employed, and the noise statistics are analyzed for correct and incorrect matches. This leads to a simple match verification procedure based on a per feature outlier distance.

An intrusion protection system that fuses a network instrumentation classification with a packet payload signature matching system. Each of these kinds of systems is independently capable of being effectively deployed as an anomaly detection system. By employing sensor fusion techniques to combine the instrumentation classification approach with the signature matching approach, the present invention provides an intrusion protection system that is uniquely capable of detecting both well known and newly developed threats while having an extremely low false positive rate.

Disclosed in the invention is an improved method of a RGB-D-based simultaneously localization and mapping (SLAM) algorithm. The method comprises two parts: a front-end part and a rear-end part. The front-end part is as follows: feature detection and descriptor extraction, feature matching, motion conversion estimation, and motion conversion optimization. And the rear-end part is as follows: a 6-D motion conversion relation initialization pose graph obtained by the front-end part is used for carrying out closed-loop detection to add a closed-loop constraint condition; a non-linear error function optimization method is used for carrying out pose graph optimization to obtain a global optimal camera pose and a camera motion track; and three-dimensional environment reconstruction is carried out. According to the invention, the feature detection and descriptor extraction are carried out by using an ORB method and feature points with illegal depth information are filtered; bidirectional feature matching is carried out by using a FLANN-based KNN method and a matching result is optimized by using homography matrix conversion; a precise inliners matching point pair is obtained by using an improved RANSAC motion conversion estimation method; and the speed and precision of point cloud registration are improved by using a GICP-based motion conversion optimization method.

A method of matching image features with reference features comprises the steps of providing a current image, providing a set of reference features, wherein each of the reference features comprises at least one first parameter which is at least partially indicative of a position and/or orientation of the reference feature with respect to a global coordinate system, wherein the global coordinate system is an earth coordinate system or an object coordinate system, or at least partially indicative of a position of the reference feature with respect to an altitude, detecting at least one feature in the current image in a feature detection process, associating with the detected feature at least one second parameter which is at least partially indicative of a position and/or orientation of the detected feature, or which is at least partially indicative of a position of the detected feature with respect to an altitude, and matching the detected feature with a reference feature by determining a similarity measure.

The invention discloses a device for realizing simultaneous positioning and map building of an indoor service robot and the indoor service robot with the device. The device comprises an external sensor, an internal sensor and an information processing module, wherein the robot moves in an external environment, the measured data of the external sensor and the internal sensor is recorded, and the characteristics of the environment are extracted, so a posture and a characteristic map of the robot are obtained by recursive prediction and an updating algorithm; and the corresponding posture and characteristic map are updated under the condition of meeting the characteristic matching. By placing the moving robot in an unknown environment and combining the positioning and the map building into a whole, the device allows the robot to incrementally build a continuous map of the unknown environment and simultaneously determine the position in the map thereof; and thus the one-time working efficiency is effectively improved, the autonomy of the self-moving indoor service robot is improved and the intelligence level of the robot is further prominent.

A method for matching fingerprint images is provided. The method includes the steps of acquiring a query image of a fingerprint; extracting a minutiae set from the query image; comparing the minutiae set of the query image with a minutiae set of at least one template image to determine transformation parameters to align the query image to the at least one template image and to determine a minutiae matching score; constructing a ridge feature map of the query image; comparing the ridge feature map of the query image to a ridge feature map of the at least one template image to determine a ridge feature matching score; and combining the minutiae matching score with the ridge feature matching score resulting in an overall score, the overall score being compared to a threshold to determine if the query image and the at least one template image match.

The invention discloses a monocular and IMU fused stable motion tracking method and device based on a mobile terminal, belonging to the technical field of AR/VR motion tracking. The method comprises the following steps of: judging whether the number of tracking feature points of a current frame of an image is greater than a pre-set threshold value or not, if so, performing feature point tracking by adopting an optical flow method so as to obtain the current pose of a camera, if not, obtaining feature points by adopting a FAST feature detection operator, and performing feature matching of the image by adopting a BRIEF algorithm calculation descriptor so as to obtain the current pose of the camera; performing Kalman filtering of the current pose of the camera so as to obtain a visual pose; obtaining acceleration and angular speed values generated by an IMU in a three-dimensional space, and performing integral operation of the acceleration and angular speed values so as to obtain the pose of the IMU; and performing Kalman fusion of the visual pose and the pose of the IMU, and performing motion tracking. Compared with the prior art, more stable and rapid motion tracking can be obtained on mobile terminal equipment.

The invention relates to a comprehensive multi-feature image retrieval method, including extraction, index and feature matching of image features, the image features include color feature, texture feature and shape feature. The color feature of the images includes: (1) normalizing the feature as 128 multiply 128 pixel; (2) dividing an image into m multiply n nubs; (3) calculating the C' value of each pixel in every nub, selectiing the main C' value, forming a corresponding two-dimensional matrix A by each main C' value. The invention improves traditional local color histogram by improving extraction method of traditional image color features, which greatly improves precision ratio comparing with common image retrieval method based on color. Application of the image retrieval method that combines multi image features of color, texture and shape can improve precision ratio of the method effectively.

The invention discloses a monocular vision-combined RGB-D SLAM method. The method comprises the following steps of: respectively carrying out feature extraction, feature matching, motion estimation and optimization on data obtained by a depth sensor by adopting a monocular vision method and an RGB-D depth vision method; establishing a monocular local position map and an RGB-D local position map; carrying out three-dimensional map fusion on the monocular local position map and the RGB-D local position map by a robot in a motion process so as to construct a global map; and carrying out closed loop detection on the fused position maps and carrying out optimization to obtain an optimum global map. The RGB-D SLAM method disclosed by the invention is high in timeliness, strong in adaptability and good in stability, and can be used for solving the problem that RGB-D depth sensors cannot obtain depth information or are insufficient in obtaining of the depth information.

The invention relates to an unmanned aerial vehicle aerial photography sequence image-based slope three-dimension reconstruction method. The method includes the following steps that: feature region matching and feature point pair extraction are performed on un-calibrated unmanned aerial vehicle multi-view aerial photography sequence images through adopting a feature matching-based algorithm; the geometric structure of a slope and the motion parameters of a camera are calculated through adopting bundle adjustment structure from motion and based on disorder matching feature points, and therefore, a sparse slope three-dimensional point cloud model can be obtained; the sparse slope three-dimensional point cloud model is processed through adopting a patch-based multi-view stereo vision algorithm, so that the sparse slope three-dimensional point cloud model can be diffused to a dense slope three-dimensional point cloud model; and the surface mesh of the slope is reconstructed through adopting Poisson reconstruction algorithm, and the texture information of the surface of the slop is mapped onto a mesh model, and therefore, a vivid three-dimensional slope model with high resolution can be constructed. The unmanned aerial vehicle aerial photography sequence image-based slope three-dimension reconstruction method of the invention has the advantages of low cost, flexibility, portability, high imaging resolution, short operating period, suitability for survey of high-risk areas and the like. With the method adopted, the application of low-altitude photogrammetry and computer vision technology to the geological engineering disaster prevention and reduction field can be greatly prompted.

The invention relates to a target object recognition and positioning method based on color images and depth images. The method is characterized by comprising the following steps that (1), a target region is confirmed by a robot by the adoption of the remote HSV color recognition, the distance between the robot and the target region is obtained according to the RGB color images and the depth images, and the robot conducts navigation and path planning and moves to the portion near the target region; (2), when the robot reaches the portion near the target region, through the SURF feature point detection, the RGB feature information of the target object is obtained, feature matching is conducted on the RGB feature information and the pre-stored RGB feature information of the target object, and if the feature of the target object accords with an existing object model, the target object is positioned; and (3), the RGB color images are collected to an imaging plane, the two-dimensional coordinates of the target object in the imaging plane are obtained, and the relative distance between the target object and a camera is obtained through the depth images, so that the three-dimensional coordinates of the target object are obtained. By the adoption of the target object recognition and positioning method, the category of the object can be judged quickly, and the three-dimensional coordinates of the object can be determined quickly.

The method for efficient target detection from images robust to occlusion disclosed by the present invention detects the presence and spatial location of a number of objects in images. It consists in (i) an off-line method to compile an intermediate representation of detection probability maps that are then used by (ii) an on-line method to construct a detection probability map suitable for detecting and localizing objects in a set of input images efficiently. The method explicitly handles occlusions among the objects to be detected and localized, and objects whose shape and configuration is provided externally, for example from an object tracker. The method according to the present invention can be applied to a variety of objects and applications by customizing the method's input functions, namely the object representation, the geometric object model, its image projection method, and the feature matching function.

The invention discloses an indoor positioning technology based on inertial data and visual features and discloses a corresponding implementation device to implement the steps. The indoor positioning technology specifically comprises: (1) multi-sensor data processing: a camera calibration and image feature extraction method; an IMU data modeling and filtering method; (2) multi-sensor coordinate system calibration: a system modeling, relative attitude calibration and relative position and attitude joint calibration method; (3) an indoor positioning and tracking technology fusing the inertial data and the visual features. Compared with an existing traditional single camera tracking technology, the indoor positioning technology disclosed by the invention has the advantages that single camera tracking is a simple assumption based on constant-speed motion; a better prediction can be provided by using the inertial data of an IMU, so that a search area is smaller during feature matching, the matching speed is higher, the tracking results are more accurate, and the camera tracking robustness in the image degradation and un-textured areas is greatly improved.

An image processing system which processes, in real time, multiple images, which are different views of the same object, of video data in order to match features in the images to support 3 dimensional motion picture production. The different images are captured by multiple cameras, processed by digital processing equipment to identify features and perform preliminary, two-view feature matching. The image data and matched feature point definitions are communicated to an adjacent camera to support at least two image matching. The matched feature point data are then transferred to a central computer, which performs a multiple-view correspondence between all of the images.

A method (100) and apparatus (700) are disclosed for detecting and tracking human faces across a sequence of video frames. Spatiotemporal segmentation is used to segment (115) the sequence of video frames into 3D segments. 2D segments are then formed from the 3D segments, with each 2D segment being associated with one 3D segment. Features are extracted (140) from the 2D segments and grouped into groups of features. For each group of features, a probability that the group of features includes human facial features is calculated (145) based on the similarity of the geometry of the group of features with the geometry of a human face model. Each group of features is also matched with a group of features in a previous 2D segment and an accumulated probability that said group of features includes human facial features is calculated (150). Each 2D segment is classified (155) as a face segment or a non-face segment based on the accumulated probability. Human faces are then tracked by finding 2D segments in subsequent frames associated with 3D segments associated with face segments.

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.

The invention discloses a real human body three-dimensional modeling method specific to personalized virtual fitting. According to the method, a video is used as input data; and several steps of feature matching, key frame selection, three-dimensional point cloud generation, template mapping, and texture mapping and the like are carried out to obtain a human body surface three-dimensional model. Because of the motion recovery structure technology, the reconstruction process is substantially simplified, the burdens of the data collection people and the collected people can be reduced, and the requirements on the instrument and equipment are reduced. Meanwhile, a precise reconstruction result can be obtained. A complete human body surface model is obtained based on template mapping; and thus the method has high robustness on the human body texture information missing and self-shielding. Therefore, only a plurality of control points need to be marked manually before template mapping from the data input to model output and other process is completed automatically. The model that is obtained based on reconstruction can be applied to the game and animation design, movie and television special efficacy, and personalized virtual fit and the like.

A pose of an object is estimated by first defining a set of pair features as pairs of geometric primitives, wherein the geometric primitives include oriented surface points, oriented boundary points, and boundary line segments. Model pair features are determined based on the set of pair features for a model of the object. Scene pair features are determined based on the set of pair features from data acquired by a 3D sensor, and then the model pair features are matched with the scene pair features to estimate the pose of the object.

The invention discloses a somatosensory-based natural interaction method for a virtual mine. The method comprises the steps of applying a Kinect to acquire gesture signals, depth information and bone point information of a user; carrying out smoothing filtering on images, depth information and bone information of the gesture signals; dividing gesture images by using a depth histogram, applying an eight neighborhood outline tracking algorithm to find out a gesture outline, and identifying static gestures; planning feature matching identification of dynamic gestures by improving dynamic time according to the bone information; triggering corresponding Win32 instruction information by using a gesture identification result, and transmitting the information to a virtual reality engine, respectively mapping the instruction information to the primary keyboard mouse operation of a virtual mining natural interaction system, so as to realize the somatosensory interaction control of the virtual mine. According to the method provided by the invention, the natural efficiency of man-machine interaction can be improved, and the immersion and natural infection represented by the virtual mine can be improved, and the application of the virtual reality and a somatosensory interaction technology can be effectively popularized in coal mines and other fields.

The invention discloses a non-overlapping field-of-view camera gesture calibration method based on point cloud feature map registration. The method comprises the following steps that: (1) carrying outbasic calibration on a plurality of cameras of a non-overlapping field of view to obtain an internal reference; (2) utilizing the plurality of cameras to carry out environment detection and synchronous positioning and mapping, constructing a point cloud map, and extracting a key frame to solve the pose matrix of the camera; (3) extracting an image frame from the key frame of one camera, carryingout similarity detection on the key frames of other cameras, constructing a matching frame point set and a matching point pair set, and carrying out projection error minimization on the projection ofthe point cloud map point on the image frame and the practical pixel coordinate; and (4) carrying out feature matching on a frame near the matched frame, blending all feature points, carrying out global optimization and iterative solution on a relative pose matrix, selecting a correction parameter according to a practical situation, and carrying out final gesture calibration on the camera. By useof the method, the problems of high calibration work intensity, low work efficiency and low accuracy of a traditional calibration method are solved.

The invention provides a feature extraction and matching method and device for a digital image based on PCA (principal component analysis), belonging to the technical field of image analysis. The method comprises the following steps of: 1) detecting scale space extreme points; 2) locating the extreme points; 3) distributing directions of the extreme points; 4) reducing dimension of PCA and generating image feature descriptors; and 5) judging similarity measurement and feature matching. The device mainly comprises a numerical value preprocessing module, a feature point extraction module and a feature point matching module. Compared with the existing SIFI (Scale Invariant Feature Transform) feature extraction and matching algorithm, the feature extraction and matching method has higher accuracy and matching speed. The method and device provided by the invention can be directly applied to such machine vision fields as digital image retrieval based on contents, digital video retrieval based on contents, digital image fusion and super-resolution image reconstruction.

An image processing system, and a method of operation thereof, including: a feature selection module for determining subsets of point clouds, the subsets selected based on key points of a three-dimensional object; a feature matching module, coupled to the feature selection module, for generating matched results based on a matching transformation of the subsets; and a point registration module, coupled to the feature matching module, for refining the matched results based on a refinement transformation to optionally align different data sets of the point clouds for displaying the aligned data sets on a device, wherein the refinement transformation includes a refinement error less than a matching error of the matching transformation.

A method for stabilizing an image during suspension of a video on an air-based platform comprises the steps as follows: first, selecting a frame image in a video series as a reference frame; extracting characteristic points of a reference image and a current image of the video series by using such a characteristic extraction method as invariant scale and feature transformation; preliminarily matching the characteristics by taking a euclidean space distance as a characteristic match evidence so as to form characteristic match point pairs; further selecting the characteristic match points according to invariability of relative positions of characteristic points of an image background and removing wrong matched characteristic match point pairs and the characteristic match point pairs positioned on a movement target; performing least square calculation by using the characteristic match point pairs in a six-parameter affine transformation module so as to obtain module parameters; and performing correction compensation to the current image so as to obtain stable video series output with fixed visual field. During the process, the invention also provides an idea of changing a new reference frame with an interval of a certain number of frames, thereby reducing errors and improving the stability accuracy; and the invention can be applied to traffic monitoring, target track and other fields and has wide market prospect and application value.

The present invention provides an improved closed-loop detection algorithm-based mobile robot vision SLAM (Simultaneous Location and Mapping) method. The method includes the following steps that: S1,Kinect is calibrated through a using the Zhang Dingyou calibration method; S2, ORB feature extraction is performed on acquired RGB images, and feature matching is performed by using the FLANN (Fast Library for Approximate Nearest network); S3, mismatches are deleted, the space coordinates of matching points are obtained, and inter-frame pose transformation (R, t) is estimated through adopting thePnP algorithm; S4, structureless iterative optimization is performed on the pose transformation solved by the PnP; and S5, the image frames are preprocessed, the images are described by using the bagof visual words, and an improved similarity score matching method is used to perform image matching so as to obtain closed-loop candidates, and correct closed-loops are selected; and S6, an image optimization method centering cluster adjustment is used to optimize poses and road signs, and more accurate camera poses and road signs are obtained through continuous iterative optimization. With the method of the invention adopted, more accurate pose estimations and better three-dimensional reconstruction effects under indoor environments can be obtained.

The invention discloses an ORB-SLAM based high-precision vehicle positioning method, which mainly solves a problem of low accuracy of the positioning result of the current ORB-SLAM classical positioning algorithm. The implementation of the method comprises the steps of selecting a calibration board to calibrate a binocular camera, and carrying out stereo correction on a captured image; detecting ORB feature points in the corrected image, and completing the feature point extraction; matching the extracted feature points by using a binocular sparse feature matching method, then acquiring the current camera pose information by using an adjacent frame feature tracking method, and constructing a local map; carrying out closed-loop detection and global optimization on the constructed local map so as to complete the establishment of a visual map, and preserving the visual map; and selecting a vehicle positioning scheme according to the number of matched feature points in the image, and determining the final position of the target vehicle through reading the visual map. The method disclosed by the invention can improve the vehicle positioning accuracy and robustness, and can be used for artificial intelligence management and safety disposal of unmanned automobiles.

An automatic medical coding system is provided. The system parses features of natural language diagnosis and procedure information. The features are compared to elements of a medical coding system. Medical codes corresponding to medical coding system elements that match features of the diagnosis and procedure information are mapped to the received diagnosis and procedure information. The mapped medical code is assigned a score reflecting the estimated reliability of the mapped medical code based on the amount of manipulation of the received diagnosis and procedure information leading to the match. The scored medical code may be submitted to a workflow making use of medical codes. The scored medical code may optionally be presented to a user for review prior to further utilization of the scored medical code.

The invention discloses an image matching method comprising the steps of performing global template matching of a template image in a source image, controlling the template image to slide in the source image to find out an optimal matching region; calculating a feature point and a feature vector of the template image and the source image; based on the feature point and the feature vector, calculating the visual similarity between the optimal matching region and the template image; if the visual similarity is zero, determining a mismatch between the optimal matching region and the template image; if the visual similarity is not zero, obtaining feature matching point pairs between the template image and the source image; and calculating a positioning coordinate of the optimal matching image based on the feature matching point pairs. The invention further provides a mobile phone application test platform based on the image matching. The invention can reduce the complexity of a matching algorithm, thereby improving the image matching efficiency and the mobile phone application test efficiency based on the image matching.

The invention discloses a generated encrypting method based on a face feature key, comprising the following stages of: an encrypting stage: generating a stable encrypting key by using a face feature method based on statistical optimization and carrying out the encrypting operation of data or files by using the stable encrypting key and a corresponding encrypting algorithm; a deciphering stage: according to a provided face image, adopting a method which is consistent to the generated key of the encrypting stage to extract features and generate a key for the face image; then, carrying out a feature matching operation, a feature error-collection step and a corresponding deciphering algorithm to decipher encrypted content; by selecting an optimally distinguishable face feature, effectively solving the problem of internal difference in biological feature recognition; and meanwhile, the encrypting and deciphering process is equal to a face recognizing an authenticating process, and the successful decryption means successful identification. The generated encrypting method is simple, is easy to operate, is convenient to apply, and the like.