A Pixel Dissimilarity Measure That Is Insensitive to Image Sampling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
A Cooperative Algorithm for Stereo Matching and Occlusion Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo Matching with Segmentation-Based Cooperation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Stereo Matching Using Belief Propagation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Maximum-Flow Formulation of the N-Camera Stereo Correspondence Problem
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
High-quality video view interpolation using a layered representation
ACM SIGGRAPH 2004 Papers
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion Layer Extraction in the Presence of Occlusion Using Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
View synthesis using stereo vision
View synthesis using stereo vision
Exact optimization for Markov random fields with convex priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Surfaces with occlusions from layered stereo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Representing moving images with layers
IEEE Transactions on Image Processing
Graph Cut Based Point-Cloud Segmentation for Polygonal Reconstruction
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Object distance estimation based on stereo vision and color segmentation with region matching
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
Mixed color/level lines and their stereo- matching with a modified Hausdorff distance
Integrated Computer-Aided Engineering
Image segmentation using fuzzy logic, neural networks and genetic algorithms: survey and trends
Machine Graphics & Vision International Journal
3-D Video based Disparity Estimation and Object Segmentation
International Journal of Advanced Pervasive and Ubiquitous Computing
Dense scene flow based on depth and multi-channel bilateral filter
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Computer Vision and Image Understanding
User assisted disparity remapping for stereo images
Image Communication
Enhancing a disparity map by color segmentation
Integrated Computer-Aided Engineering
Hi-index | 0.00 |
This paper describes a dense stereo matching algorithm for epipolar rectified images. The method applies colour segmentation on the reference image. Our basic assumptions are that disparity varies smoothly inside a segment, while disparity boundaries coincide with the segment borders. The use of these assumptions makes the algorithm capable of handling large untextured regions, estimating precise depth boundaries and propagating disparity information to occluded regions, which are challenging tasks for conventional stereo methods. We model disparity inside a segment by a planar equation. Initial disparity segments are clustered to form a set of disparity layers, which are planar surfaces that are likely to occur in the scene. Assignments of segments to disparity layers are then derived by minimization of a global cost function. This cost function is based on the observation that occlusions cannot be dealt with in the domain of segments. Therefore, we propose a novel cost function that is defined on two levels, one representing the segments and the other corresponding to pixels. The basic idea is that a pixel has to be assigned to the same disparity layer as its segment, but can as well be occluded. The cost function is then effectively minimized via graph-cuts. In the experimental results, we show that our method produces good-quality results, especially in regions of low texture and close to disparity boundaries. Results obtained for the Middlebury test set indicate that the proposed method is able to compete with the best-performing state-of-the-art algorithms.