The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recovering local surface structure through local phase difference measurements
CVGIP: Image Understanding
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Maximum-Likelihood Image Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
ECCV '90 Proceedings of the First European Conference on Computer Vision
The Geometry and Matching of Curves in Multiple Views
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
Regularizing Phase-Based Stereo
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
Multi-scale phase-based local features
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
A compact harmonic code for early vision based on anisotropic frequency channels
Computer Vision and Image Understanding
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In this study, a multi-scale phase based sparse disparity algorithm and a probabilistic model for matching uncertain phase are proposed. The features used are oriented edges extracted using steerable filters. Feature correspondences are estimated using phase-similarity at multiple scale using a magnitude weighting scheme. In order to achieve sub-pixel accuracy in disparity, we use a fine tuning procedure which employs the phase difference between corresponding feature points. We also derive a probabilistic model, where phase uncertainty is trained using data from a single image pair. The model is used to provide stable matches. The disparity algorithm and the probabilistic phase uncertainty model are verified on various stereo image pairs.