Computational framework for determining stereo correspondence from a set of linear spatial filters
Image and Vision Computing - Special issue: 2nd European Conference on Computer Vision
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Occlusions and binocular stereo
International Journal of Computer Vision
A Bayesian approach to binocular stereopsis
International Journal of Computer Vision
Stereo Without Epipolar Lines: A Maximum-Flow Formulation
International Journal of Computer Vision - Special issue on computer vision research at NEC Research Institute
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Modeling of Uncertainty in Low-Level Vision
Bayesian Modeling of Uncertainty in Low-Level Vision
Artificial Vision for Mobile Robots: Stereo Vision and Multisensory Perception
Artificial Vision for Mobile Robots: Stereo Vision and Multisensory Perception
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
On Binocularly Viewed Occlusion Junctions
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Occlusions, Discontinuities, and Epipolar Lines in Stereo
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
A Maximum-Flow Formulation of the N-Camera Stereo Correspondence Problem
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Image based reconstruction using hybrid optimization of simulated annealing and genetic algorithm
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Total absolute Gaussian curvature for stereo prior
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Simultaneous image interpolation for stereo images
Signal Processing
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Sometimes called the smoothing assumption, the prior model of a stereo matching algorithm is the algorithm's expectation on the surfaces in the world. Any stereo algorithm makes assumptions about the probability to see each surface that can be represented in its representation system. Although the past decade has seen much continued progress in stereo matching algorithms, the prior models used in them have not changed much in three decades: most algorithms still use a smoothing prior that minimizes some function of the difference of depths between neighboring sites, sometimes allowing for discontinuities. However, one system seems to use a very different prior model from all other systems: the human vision system. In this paper, we first report the observations we made in examining human disparity interpolation using stereo pairs with sparse identifiable features. Then we mathematically analyze the implication of using current prior models and explain why the human system seems to use a model that is not only different but in a sense diametrically opposite from all current models. Finally, we propose two candidate models that reflect the behavior of human vision. Although the two models look very different, we show that they are closely related.