An Interactive Approach to Pose-Assisted and Appearance-based Segmentation of Humans

An interactive human segmentation approach is described. Given regions of interest provided by users, the approach iteratively estimates segmentation via a generalized EM algorithm. Specifically, it encodes both spatial and color information in a nonparametric kernel density estimator, and incorporates local MRF constraints and global pose inferences to propagate beliefs over image space iteratively to determine a coherent segmentation. This ensures the segmented humans resemble the shapes of human poses. Additionally, a layered occlusion model and a probabilistic occlusion reasoning method are proposed to handle segmentation of multiple humans in occlusion. The approach is tested on a wide variety of images containing single or multiple occluded humans, and the segmentation performance is evaluated quantitatively.

• Type: Conference
• Year: 2007
• Where: ICCV
• Authors: Zhe Lin, Larry S. Davis, David S. Doermann, Daniel DeMenthon
• Keywords: Coherent Segmentation, Computer Vision, Human Segmentation Approach, ICCV 2007, Layered Occlusion Model, Probabilistic Occlusion Reasoning, Segmentation Performance