Practical algorithms for image analysis: description, examples, and code
Practical algorithms for image analysis: description, examples, and code
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
Fast Stereo Matching Using Rectangular Subregioning and 3D Maximum-Surface Techniques
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-camera Scene Reconstruction via Graph Cuts
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
A Maximum-Flow Formulation of the N-Camera Stereo Correspondence Problem
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
A Dense Stereo Matching Using Two-Pass Dynamic Programming with Generalized Ground Control Points
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Multi-label Depth Estimation for Graph Cuts Stereo Problems
Journal of Mathematical Imaging and Vision
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Stereo vision is one of the central research problems in computer vision. The most difficult and important issue in this area is the stereo matching process. One technique that performs this process is the Graph-Cuts based algorithm and which provides accurate results [1]. Nevertheless, this approach is too slow to use due to the redundant computations that it invokes. In this work, an Adaptive Graph-Cuts based algorithm is implemented. The key issue is to subdivide the image into several regions using quadtrees and then define a global energy function that adapts itself for each of these subregions. Results show that the proposed algorithm is 3 times faster than the other Graph-Cuts algorithm while keeping the same quality of the results.