Artificial Intelligence - Special volume on computer vision
Modal Matching for Correspondence and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A direct method for stereo correspondence based on singular value decomposition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wide-baseline multiple-view correspondences
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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This paper proposed a robust point matching method for wide baseline in order to achieve a large number of correct correspondences and high accuracy. To cope with large variations of scale and rotation, a feature descriptor, that is robust to scale and view-point, is added to the feature detection phase and it is included in the equations of the correspondence matrix that is central to the matching algorithm. Furthermore, the image window for normalized cross correlation is modified with adaptive scale and orientation. At the same time we remove from the matrix all the proximity information about the distance between points' locations which is the source of mismatches. Thus, the proposed algorithm is invariant to changes of scale, rotation, light and partially invariant to viewpoint. Experimental results show that the proposed algorithm can be used for large scene variations and provide evidence of better performance.