Recognizing articulated objects with information theoretic methods
Davi Geiger, Tyng-Luh Liu · 2002
This paper addresses the problem of recognizing articulated and deformable objects. In particular we are interested in human arm and leg articulations. Our approach is a Bayesian-Information integration of shape similarity and snakes, and naturally combines top-down and bottom-up algorithms. The bottom-up method extracts edges, then constructs snakes (or contours) by grouping edge elements and feeds the shape analysis. The top-down one uses shape analysis, by comparing the object model with the extracted snakes, to guide/prune the search for other snakes. The optimizations are based on Dijkstra algorithm and further pruning of this algorithm is obtained by "integration by parts". Our approach is general enough to handle three dimensional objects, but our focus here is on two dimensional contours.