Phase-based disparity measurement
CVGIP: Image Understanding
Hypergeometric Filters for Optical Flow and Affine Matching
International Journal of Computer Vision
A Pixel Dissimilarity Measure That Is Insensitive to Image Sampling
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Stability of Phase Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo Matching Using Belief Propagation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Finding the Largest Unambiguous Component of Stereo Matching
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
On Occluding Contour Artifacts in Stereo Vision
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
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
Sampling the Disparity Space Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Towards automatic visual obstacle avoidance
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
Dense stereomatching algorithm performance for view prediction and structure reconstruction
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
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
Multi-Parameter Simultaneous Estimation on Area-Based Matching
International Journal of Computer Vision
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A traditional solution of area-based stereo uses some kind of windowed pixel intensity correlation. This approach suffers from discretization artifacts which corrupt the correlation value. We introduce a new correlation statistic, which is completely invariant to image sampling, moreover it naturally provides a position of the correlation maximum between pixels. Hereby we can obtain sub-pixel disparity directly from sampling invariant and highly discriminable measurements without any postprocessing of the discrete disparity map. The key idea behind is to represent the image point neighbourhood in a different way, as a response to a bank of Gabor filters. The images are convolved with the filter bank and the complex correlation statistic (CCS) is evaluated from the responses without iterations.