Linear Scale and Rotation Invariant Matching
Matching visual patterns that appear scaled, rotated, and deformed with respect to each other is a challenging problem. We propose a linear formulation that simultaneously matches feature points and estimates global geometrical transformation in a constrained linear space. The linear scheme enables search space reduction based on the lower convex hull property so that the problem size is largely decoupled from the original hard combinatorial problem. Our method therefore can be used to solve large scale problems that involve a very large number of candidate feature points. Without using prepruning in the search, this method is more robust in dealing with weak features and clutter. We apply the proposed method to action detection and image matching. Our results on a variety of images and videos demonstrate that our method is accurate, efficient, and robust.
scale and rotation invariant matching action detection deformable matching linear programming shape matching object matching