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Two-dimensional object recognition through two-stage string matching
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DOI：10.1109/83.772245.png)
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Abstract
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A two-stage string matching method for the recognition of two-dimensional (2-D) objects is proposed in this work. The first stage is a global cyclic string matching. The second stage is a local matching with local dissimilarity measure computing. The dissimilarity measure function of the input shape and the reference shape is obtained by combining the global matching cost and the local dissimilarity measure. The proposed method has the advantage that there is no need to set any parameter in the recognition process. Experimental results indicate that the two-stage string matching approach significantly improves the recognition rates while comparing to the one-stage string matching method.
Keywords:
compactness
cost function
cyclic string
edit graph
feature extraction
object recognition
two-stage string matching
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