Statistical Pattern Recognition Using the Normalized Complex Moment Components Vector
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Auteurs : Stavros Paschalakis [Royaume-Uni] ; Peter Lee [Royaume-Uni]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2000.
Abstract
Abstract: This paper presents a new feature vector for statistical pattern recognition based on the theory of moments, namely the Normalized Complex Moment Components (NCMC). The NCMC will be evaluated in the recognition of objects which share identical silhouettes using grayscale images and its performance will be compared with that of a commonly used moment based feature vector, the Hu moment invariants. The tolerance of the NCMC to random noise and the effect of using different orders of moments in its calculation will also be investigated.
Url:
DOI: 10.1007/3-540-44522-6_55
Affiliations:
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<front><div type="abstract" xml:lang="en">Abstract: This paper presents a new feature vector for statistical pattern recognition based on the theory of moments, namely the Normalized Complex Moment Components (NCMC). The NCMC will be evaluated in the recognition of objects which share identical silhouettes using grayscale images and its performance will be compared with that of a commonly used moment based feature vector, the Hu moment invariants. The tolerance of the NCMC to random noise and the effect of using different orders of moments in its calculation will also be investigated.</div>
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