Model-guided attributed string matching by split-and-merge for shape recognition.

ASIAIR > College of Computer Science > Department of Information Communication > Journal Article > Item 310904400/3376

Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/3376

Title:	Model-guided attributed string matching by split-and-merge for shape recognition.
Authors:	YT Tsay;WH Tsai
Contributors:	Department of Information Communication
Keywords:	strings;matching;polygons
Date:	1989
Issue Date:	2009-11-17 11:12:54 (UTC+0)
Publisher:	Asia University
Abstract:	Due to noise and distortion, segmentation uncertainty is a key problem in structural pattern analysis. In this paper the authors propose the use of the split operation for shape recognition by attributed string matching. After illustrating the disadvantage of attributed string matching using the merge operation, the split operation is proposed. Under the guidance of the model shape, an input shape can be reapproximated, using the split operation, into a new attributed string representation. By combining the split and the merge operations for shape matching it is unnecessary to apply any type of edit operation to a model shape. This makes the distance between the input shape and the model shape more meaningful and stable, and improves recognition results. An algorithm for attributed string matching by split-and-merge is proposed.
Relation:	International Journal of Pattern Recognition and Artificial Intelligence 3(2):159-179
Appears in Collections:	[Department of Information Communication] Journal Article

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