Multi-View Object Extraction With Fractional Boundaries
This paper presents an automatic method to extract a multi-view object in a natural environment. We assume that the target object is bounded by the convex volume of interest defined by the overlapping space of camera viewing frustums. There are two key contributions of our approach. First, we present an automatic method to identify a target object across different images for multi-view binary co-segmentation. The extracted target object shares the same geometric representation in space with a distinctive color and texture model from the background. Second, we present an algorithm to detect color ambiguous regions along the object boundary for matting refinement. Our matting region detection algorithm is based on the information theory, which measures the Kullback-Leibler divergence of local color distribution of different pixel bands. The local pixel band with the largest entropy is selected for matte refinement, subject to the multi-view consistent constraint. Our results are high-quality alpha mattes consistent across all different viewpoints. We demonstrate the effectiveness of the proposed method using various examples.