A New Sense for Depth of Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
Depth from Defocus vs. Stereo: How Different Really Are They?
International Journal of Computer Vision - Special issue on computer vision research at the Technion
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Helmholtz Stereopsis: Exploiting Reciprocity for Surface Reconstruction
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Comparison of Graph Cuts with Belief Propagation for Stereo, using Identical MRF Parameters
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Non-photorealistic camera: depth edge detection and stylized rendering using multi-flash imaging
ACM SIGGRAPH 2004 Papers
Adaptive Support-Weight Approach for Correspondence Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Recent progresses in stereo research imply that performance of the disparity estimation depends on the discontinuity localization in the disparity space which is generally predicated on discontinuities in the image intensities. However, these approaches have known limitations at highly textured and occluded regions. In this paper, we propose to employ a layered representation of the scene as an approximation of the scene structure. The layered representation of the scenes was obtained by using partially focused image set of the scene. Although self occlusions are still present in real aperture imaging systems, our approach does not suffer from the occlusion problems as much as stereo and focus/defocus based methods. Our disparity estimation method is based on synchronously optimized two interdependent processes which are regularized with a nonlinear diffusion operator. The amount of diffusion between the neighbors is adjusted adaptively according to information in the layered scene representation and temporal positions of the processes. The system is initialization insensitive and very robust against local minima. In addition, it accurately handles the depth discontinuities. The performance of the presented method has been verified through experiments on real and synthetic scenes.