Partial shape classification using contour matching in distancetransformation
Liu, H.-C.
Srinath, M.D.
Inst. of Nucl. Energy Res., Lung-Tan;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Nov 1990
Volume: 12,
Issue: 11
On page(s): 1072-1079
ISSN: 0162-8828
References Cited: 17
CODEN: ITPIDJ
INSPEC Accession Number: 3837627
Digital Object Identifier: 10.1109/34.61706
Current Version Published: 2002-08-06
Abstract
An algorithm is presented to recognize and locate partially
distorted 2D shapes without regard to their orientation, location, and
size. The algorithm first calculates the curvature function from the
digitized image of an object. The points of local maxima and minima
extracted from the smooth curvature are used as control points to
segment the boundary and to guide the boundary-matching procedure. The
boundary-matching procedure considers two shapes at a time, one shape
from the template databank, and the other from the object being
classified. The procedure tries to match the control points in the
unknown shape to those of a shape from the template databank, and
estimates the translation, rotation, and scaling factors to be used to
normalize the boundary of the unknown shape. The chamfer 3/4 distance
transformation and a partial distance measurement scheme constitute the
final step in measuring the similarity between the two shapes. The
unknown shape is assigned to the class corresponding to the minimum
distance. The algorithm has been successfully tested on partial shapes
using two sets of data, one with sharp corners and the other with curve
segments. This algorithm not only is computationally simple, but also
works reasonably well in the presence of a moderate amount of
noise
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