Computer Vision, Graphics, and Image Processing
A Stereo Vision Technique Using Curve-Segments and Relaxation Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical stereo and motion correspondence using feature groupings
International Journal of Computer Vision
Stereo Matching with Nonlinear Diffusion
International Journal of Computer Vision
A Variable Window Approach to Early Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Images as Embedded Maps and Minimal Surfaces: Movies, Color, Texture, and Volumetric Medical Images
International Journal of Computer Vision - Special issue on computer vision research at the Technion
Optimal Algorithm for Shape from Shading and Path Planning
Journal of Mathematical Imaging and Vision
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo Correspondence with Compact Windows via Minimum Ratio Cycle
IEEE Transactions on Pattern Analysis and Machine Intelligence
Variational Restoration and Edge Detection for Color Images
Journal of Mathematical Imaging and Vision
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
The Geometry and Matching of Curves in Multiple Views
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Stereo Matching Using Belief Propagation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Advances in Computational Stereo
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Energy Minimisation Approach to Stereo-Temporal Dense Reconstruction
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Vector-Valued Image Regularization with PDEs: A Common Framework for Different Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Symmetric Stereo Matching for Occlusion Handling
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Locally Adaptive Support-Weight Approach for Visual Correspondence Search
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A General Framework and New Alignment Criterion for Dense Optical Flow
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Region-Tree Based Stereo Using Dynamic Programming Optimization
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Segment-Based Stereo Matching Using Belief Propagation and a Self-Adapting Dissimilarity Measure
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Efficient Beltrami flow using a short time kernel
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
Fast variable window for stereo correspondence using integral images
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
A general framework for low level vision
IEEE Transactions on Image Processing
Modified curvature motion for image smoothing and enhancement
IEEE Transactions on Image Processing
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Every stereovision application must cope with the correspondence problem. The space of the matching variables, often consisting of spatial coordinates, intensity and disparity, is commonly referred as the data term (space). Since the data is often noisy a-priori, preference is required to result a smooth disparity (or piecewise smooth). To this end, each local method (e.g. window correlation techniques) performs a regularization of the data space. In this paper we propose a geometric framework for anisotropic regularization of the data space seeking to preserve the discontinuities in this space when filtering out the noise. On the other hand, the global methods consider a non-regularized data term with a smoothing constraint imposed directly on the disparity. This paper also proposes a new idea where the data space is regularized in a global method prior to the disparity evaluation. The idea is implemented on the state of the art variational method. Experimental results on the Middlebury real images demonstrate the advantages of the proposed approach.