Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters - Special issue on genetic algorithms
A genetic algorithm for affine invariant recognition of object shapes from broken boundaries
Pattern Recognition Letters
Partial shape matching using genetic algorithms
Pattern Recognition Letters
Pattern Recognition Letters
Genetic algorithm for affine point pattern matching
Pattern Recognition Letters
Efficient contour-based shape representation and matching
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
Coarse-to-Fine Multiscale Affine Invariant Shape Matching and Classification
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Applying Genetic Algorithms on Pattern Recognition: An Analysis and Survey
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
2D Affine-Invariant Contour Matching Using B-Spline Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
New scale invariant template matching technique using hyper space image representation
Pattern Analysis & Applications
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Viewpoint invariant identification of fragmented scene contours can be realized by matching them against a collection of known reference models. For near planar objects, the matching of a pair of contours can be encapsulated as the search for the existence of an affine transform between them. Past research has demonstrated that the search process can be effectively accomplished with the integration of a simple genetic algorithm (SGA) and quality migrant injection (QMI), a method referred to as the quality migrant genetic algorithm (QMGA). Despite the favorable outcome, this method is extremely vulnerable to noise contamination on the image scene. In this paper we provide an explanation on the causes of this problem, and propose a solution known as successive erosion and distance accumulation (SEDA). Experimental evaluation shows that by supplementing the QMGA method with the proposed scheme, higher success rates can be attained in identifying matched contours under moderate amount of noise contamination.