Machine recognition of occluded two-dimensional objects in clutter

Abstract

This thesis presents a tree search based approach for recognizing occluded 2-D objects in clutter. The planar shapes are represented in terms of their contour line segments. Each is allowed three degrees of freedom (x, y translation and rotation) and a variable scaling factor. It is shown that incorrect pairings of line segments may be eliminated efficiently by using ordering of line segments, local, and global constraints. It is also shown by simulation and by mathematical bounds that the number of hypotheses consistent with these constraints is small