We present a novel algorithm for performing integrated segmentation and 3D pose
estimation of a human body from multiple views. Unlike other related state of the art
techniques which focus on either segmentation or pose estimation individually, our
approach tackles these two tasks together. Normally, when optimizing for pose, it is
traditional to use some fixed set of features, eg edges or chamfer maps. In contrast, our
novel approach consists of optimizing a cost function based on a Markov Random Field …

This paper presents a novel algorithm for performing integrated segmentation and 3D pose
estimation of a human body from multiple views. Unlike other state of the art methods which
focus on either segmentation or pose estimation individually, our approach tackles these two
tasks together. Our method works by optimizing a cost function based on a Conditional
Random Field (CRF). This has the advantage that all information in the image (edges,
background and foreground appearances), as well as the prior information on the shape …