Image Registration with Global and Local Luminance Alignment

Inspired by tensor voting, we present luminance voting, a novel approach for image registration with global and local luminance alignment. The key to our modeless approach is the direct estimation of replacement function, by reducing the complex estimation problem to the robust 2D tensor voting in the corresponding voting spaces. No model for replacement function is assumed. Luminance data are first encoded into 2D ball tensors. Subject to the monotonic constraint only, we vote for an optimal replacement function by propagating the smoothness constraint using a dense tensor field. Our method effectively infers missing curve segments and rejects image outliers without assuming any simplifying or complex curve model. The voted replacement functions are used in our iterative registration algorithm for computing the best warping matrix. Unlike previous approaches, our robust method corrects exposure disparity even if the two overlapping images are initially misaligned. Luminance voting is...

• Type: Conference
• Year: 2003
• Where: ICCV
• Authors: Jiaya Jia, Chi-Keung Tang
• Keywords: Computer Vision, Corresponding Voting Spaces, ICCV 2003, Local Luminance Alignment, Luminance Voting, Robust 2D Tensor, Tensor Voting