This paper presents a novel approach to address the problem of generic 2D shape recognition. We propose a morphological method to decompose a binary shape into entities in correspondence with their protrusions. Each entity is associated with a set of perceptual features that can be used in indexing into image databases. The matching process, based on the softassign algorithm, has produced encouraging results, showing the potential of the developed method in a variety of computer vision and pattern recognition domains. The results demonstrate its robustness in the presence of scale, reflection and rotation transformations and prove the ability to handle noise and articulated structures. In order to increase efficiency, the retrieval process is applied after a coarse scale grouping of objects, without sacrificing effectiveness and allowing indexing into large shape databases.

Decomposition of two-dimensional shapes for efficient retrieval

DI RUBERTO, CECILIA;
2009

Abstract

This paper presents a novel approach to address the problem of generic 2D shape recognition. We propose a morphological method to decompose a binary shape into entities in correspondence with their protrusions. Each entity is associated with a set of perceptual features that can be used in indexing into image databases. The matching process, based on the softassign algorithm, has produced encouraging results, showing the potential of the developed method in a variety of computer vision and pattern recognition domains. The results demonstrate its robustness in the presence of scale, reflection and rotation transformations and prove the ability to handle noise and articulated structures. In order to increase efficiency, the retrieval process is applied after a coarse scale grouping of objects, without sacrificing effectiveness and allowing indexing into large shape databases.
Shape decomposition; Morphological skeleton; Shape matching
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/22364
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