Parallax-aware local alignment for image stitching under large occlusion/disocclusion
Wang Yangxin, Kai Li, Ping An, Liquan Shen, Xuemei Zou · 2016
This paper presents a parallax-aware image stitching approach under large occlusion/disocclusion. Different from previous research, we explore the image stitching issue in a parallax-aware perspective via local alignment. Specifically, we first label each feature point with a probability of being large parallax by developing a graph-based optimization framework. Afterwards, an integer programming model is built to pick out a group of feature matches free from parallax in a local region. Finally, by enforcing a stitching seam passing through such a locally aligned area, we are able to generate a high-quality stitching result under large parallax. Experimental results demonstrate the effectiveness and superiority of the proposed parallax-aware approach.