Neural disparity computation for dense two-frame stereo correspondence
Pattern Recognition Letters
Performance characterization in computer vision: A guide to best practices
Computer Vision and Image Understanding
Real-time disparity map computation module
Microprocessors & Microsystems
Robotics and Autonomous Systems
FPGA based disparity map computation with vergence control
Microprocessors & Microsystems
A real-time fuzzy hardware structure for disparity map computation
Journal of Real-Time Image Processing
Use of human motion biometrics for multiple-view registration
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
A new algorithm to get the correspondences from the image sequences
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
A neural approach for obstacle detection with a linear stereoscopic sensor
Mathematical and Computer Modelling: An International Journal
Stereoscopic neuro-vision for three-dimensional object recognition
Mathematical and Computer Modelling: An International Journal
Segmentation of moving observer frame sequences
Pattern Recognition Letters
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An algorithm for matching images of real world scenes is presented. The matching is a specification of the geometrical disparity between the images and may be used to partially reconstruct the three-dimensional structure of the scene. Sets of candidate matching points are selected independently in each image. These points are the locations of small, distinct features which are likely to be detectable in both images. An initial network of possible matches between the two sets of candidates is constructed. Each possible match specifies a possible disparity of a candidate point in a selected reference image. An initial estimate of the probability of each possible disparity is made, based on the similarity of subimages surrounding the points. These estimates are iteratively improved by a relaxation labeling technique making use of the local continuity property of disparity that is a consequence of the continuity of real world surfaces. The algorithm is effective for binocular parallax, motion parallax, and object motion. It quickly converges to good estimates of disparity, which reflect the spatial organization of the scene.