A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching Two Perspective Views
IEEE Transactions on Pattern Analysis and Machine Intelligence
Error Analysis of a Real-Time Stereo System
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Stereo Without Search
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Advances in Computational Stereo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov random field approach to region extraction using Tabu Search
Journal of Visual Communication and Image Representation
An effective stereo matching algorithm with optimal path cost aggregation
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Background updating with the use of intrinsic curves
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
Toward visually inferring the underlying causal mechanism in a traffic-light-controlled crossroads
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Using normal vectors for stereo correspondence construction
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Hi-index | 0.14 |
We propose a representation of images, called intrinsic curves, that transforms stereo matching from a search problem into a nearest-neighbor problem. Intrinsic curves are the paths that a set of local image descriptors trace as an image scanline is traversed from left to right. Intrinsic curves are ideally invariant with respect to disparity. Stereo correspondence then becomes a trivial lookup problem in the ideal case. We also show how to use intrinsic curves to match real images in the presence of noise, brightness bias, contrast fluctuations, moderate geometric distortion, image ambiguity, and occlusions. In this case, matching becomes a nearest-neighbor problem, even for very large disparity values.