Using Dynamic Programming for Solving Variational Problems in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parallel and Deterministic Algorithms from MRFs: Surface Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cooperative Vision Integration Through Data-Parallel Neural Computations
IEEE Transactions on Computers - Special issue on artificial neural networks
Depth Discontinuities by Pixel-to-Pixel Stereo
International Journal of Computer Vision
COMVIS: A Communication Framework for Computer Vision
International Journal of Computer Vision
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Multimodal Estimation of Discontinuous Optical Flow using Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Relaxing Symmetric Multiple Windows Stereo Using Markov Random Fields
EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Markov Random Fields with Efficient Approximations
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
High-precision stereo disparity estimation using HMMF models
Image and Vision Computing
Motion segmentation using an occlusion detector
WDV'05/WDV'06/ICCV'05/ECCV'06 Proceedings of the 2005/2006 international conference on Dynamical vision
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Integration of several vision modules is likely to be one of the keys to the power and robustness of the human visual system. We suggest that integration is best performed at the location of discontinuities in early processes, such as discontinuities in image brightness, depth, motion, texture, and color. Coupled Markov Random Field models can be used to combine vision modalities with their discontinuities. We derive a scheme to integrate intensity edges with stereo depth and motion field information and show results from a Connection Machine algorithm on synthetic and natural images. The use of intensity edges to integrate other visual cues and to help discover discontinuities emerges as a general and powerful principle.