IEEE Transactions on Pattern Analysis and Machine Intelligence
Optical Flow Estimation: An Error Analysis of Gradient-Based Methods with Local Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching perspective views of a polyhedron using circuits
IEEE Transactions on Pattern Analysis and Machine Intelligence
Surfaces from Stereo: Integrating Feature Matching, Disparity Estimation, and Contour Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion and Structure From Two Perspective Views: Algorithms, Error Analysis, and Error Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Measurement of Visual Motion
Parameter Estimation for Optimal Object Recognition: Theory andApplication
International Journal of Computer Vision
Transitory Image Sequences, Asymptotic Properties, and Estimation of Motion and Structure
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo Matching as a Nearest-Neighbor Problem
IEEE Transactions on Pattern Analysis and Machine Intelligence
Epipolar Geometry and Linear Subspace Methods: A New Approach to Weak Calibration
International Journal of Computer Vision
Intensity- and Gradient-Based Stereo Matching Using Hierarchical Gaussian Basis Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extraction of 2D Motion Trajectories and Its Application to Hand Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal Motion and Structure Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning and Feature Selection in Stereo Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integrating Vision Modules: Stereo, Shading, Grouping, and Line Labeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Occam Algorithms for Computing Visual Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Intrinsic Images for Dense Stereo Matching with Occlusions
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
A Statistical Framework for Long-Range Feature Matching in Uncalibrated Image Mosaicing
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
On-Road Vehicle Detection: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Autonomous robot calibration using vision technology
Robotics and Computer-Integrated Manufacturing
A real-time object detecting and tracking system for outdoor night surveillance
Pattern Recognition
Semi-supervised learning of object categories from paired local features
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Detection of object motion regions in aerial image pairs with a multilayer Markovian model
IEEE Transactions on Image Processing
Geometrical scene analysis using co-motion statistics
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
Use of human motion biometrics for multiple-view registration
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
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A computational approach to image matching is described. It uses multiple attributes associated with each image point to yield a generally overdetermined system of constraints, taking into account possible structural discontinuities and occlusions. In the algorithm implemented, intensity, edgeness, and cornerness attributes are used in conjunction with the constraints arising from intraregional smoothness, field continuity and discontinuity, and occlusions to compute dense displacement fields and occlusion maps along the pixel grids. The intensity, edgeness, and cornerness are invariant under rigid motion in the image plane. In order to cope with large disparities, a multiresolution multigrid structure is employed. Coarser level edgeness and cornerness measures are obtained by blurring the finer level measures. The algorithm has been tested on real-world scenes with depth discontinuities and occlusions. A special case of two-view matching is stereo matching, where the motion between two images is known. The algorithm can be easily specialized to perform stereo matching using the epipolar constraint.