Robust Image Matching Preserving Global Consistency

We present a new method for detecting point matches between two images. The main issue is how to preserve the global consistency of individual matches. Existing methods propagate local smoothness by relaxation or do combinatorial search for an optimal solution. Our method imposes non-local constraints that should be approximately satisfied across the image. We define the “confidence” of such “soft constraints” to all potential matches. The confidence is progressively updated by “mean-field approximation”. Finally, the “hard” epipolar constraint is imposed by RANSAC. Using real images, we demonstrate that our method is robust to camera rotations and zooming changes.