Enforcing stochastic inverse consistency in non-rigid image registration and matching

This paper presents a new method to enforce inverse consistency in nonrigid image registration and matching. Conventional approaches assume diffeomorphic transformation, implicitly or explicitly. However, the inherent smoothness constraint discourages discontinuity consideration. We propose a post-processing algorithm that integrates the input forward and backward fields, which are output by existing registration/matching algorithms, to produce more robust results. Given such a pair of input fields, our algorithm alternately refines the fields by tensor belief propagation, and enforces inverse consistency in stochastic sense by generalized total least squares fitting. To show the efficacy of our stochastic inverse consistency approach, we first present results on very noisy fields. We then demonstrate improvement on existing stereo matching where occlusion is naturally handled by localizing violations of inverse consistency. Finally, we propose a novel application on image stitching, ...

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Sai Kit Yeung, Chi-Keung Tang, Pengcheng Shi, Josien P. W. Pluim, Max A. Viergever, Albert C. S. Chung, Helen C. Shen
• Keywords: Computer Vision, Constraint Discourages Discontinuity, CVPR 2008, Input Fields, Inverse Consistency Approach, Stochastic Inverse Consistency, Stochastic Sense