A novel correspondence searching strategy in multiocular vision

Correspondence searching among different images is a fundamental problem in computer vision. It is important to find correspondences correctly and rapidly, especially for real-time tracking systems. Therefore, the definition of search areas in images is crucial. Traditional epipolar constraint is not noise-enduring; some reformative methods lack explicit geometric meanings. All of them cannot help defining rational search areas under noises. This paper proposes two new binocular imaging constraints with clear geometric meanings and strong restraining forces. Based on them, a novel searching strategy among multiimages is developed which can define optimal search areas with smallest sizes but best reliability. Practical algorithms for implementation are presented and experiments with real images are performed, validating the effectiveness of the proposed strategy.