`Region-growing' algorithm for matching of terrain images
Image and Vision Computing
Stereo Matching with Nonlinear Diffusion
International Journal of Computer Vision
International Journal of Computer Vision
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Match Propogation for Image-Based Modeling and Rendering
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
Visual Correspondence Using Energy Minimization and Mutual Information
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
The Amsterdam Library of Object Images
International Journal of Computer Vision
A Quasi-Dense Approach to Surface Reconstruction from Uncalibrated Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
High-accuracy stereo depth maps using structured light
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Correlation matching has been widely accepted as a rudimentary similarity measure to obtain dense 3D reconstruction from a stereo pair. In particular, given a large overlapping area between images with minimal scale differences, the correlation results followed by a geometrically constrained global optimisation delivers adequately dense and accurate reconstruction results. In order to achieve greater reliability, however, correlation matching should correctly account for the geometrical distortion introduced by the different viewing angles of the stereo or multi-view sensors. Conventional adaptive least squares correlation (ALSC) matching addresses this by modifying the shape of a matching window iteratively, assuming that the distortion can be approximated by an affine transform. Nevertheless, since an image captured from different viewing angle is often not practically identical due to scene occlusions, the matching confidence normally deteriorates. Subsequently, it affects the density of the reconstruction results from ALSC-based stereo region growing algorithms. To address this, we propose an advanced ALSC matching method that can progressively update matching weight for each pixel in an aggregating window using a relaxation labelling technique. The experimental results show that the proposed method can improve matching performance, which consequently enhances the quality of stereo reconstruction. Also, the results demonstrate its ability to refine a scale invariant conjugate point pair to an affine and scale invariant point pair.