Matching Point Features with Ordered Geometric, Rigidity, and Disparity Constraints
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Global Solution to Sparse Correspondence Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-view Matching for Unordered Image Sets, or "How Do I Organize My Holiday Snaps?"
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
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
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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This paper proposes a novel and efficient feature-point matching algorithm for finding point correspondences between two uncalibrated images. The striking feature of the proposed algorithm is that the algorithm is based on the motion coherence/smoothness constraint only, which states that neighboring features in an image tend to move coherently. In the algorithm, the correspondences of feature points in a neighborhood are collectively determined in a way such that the smoothness of the local motion field is maximized. The smoothness constraint does not rely on any image feature, and is self-contained in the motion field. It is robust to the camera motion, scene structure, illumination, etc. This makes the proposed algorithm texture-independent and robust. Experimental results show that the proposed method outperforms existing methods for feature-point tracking in image sequences.