  Matching deformable objects using their shapes is an important problem in
computer vision since shape is perhaps the most distinguishable characteristic
of an object. The problem is difficult due to many factors such as intra-class
variations, local deformations, articulations, viewpoint changes and missed and
extraneous contour portions due to errors in shape extraction. While small
local deformations has been handled in the literature by allowing some leeway
in the matching of individual contour points via methods such as Chamfer
distance and Hausdorff distance, handling more severe deformations and
articulations has been done by applying local geometric corrections such as
similarity or affine. However, determining which portions of the shape should
be used for the geometric corrections is very hard, although some methods have
been tried. In this paper, we address this problem by an efficient search for
the group of contour segments to be clustered together for a geometric
correction using Dynamic Programming by essentially searching for the
segmentations of two shapes that lead to the best matching between them. At the
same time, we allow portions of the contours to remain unmatched to handle
missing and extraneous contour portions. Experiments indicate that our method
outperforms other algorithms, especially when the shapes to be matched are more
complex.
