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Pruning Process Influences on Recognition Rate of Skeletal Shapes


Affiliations
1 Department of Computer Science, College of Science, University of Mustansiriyah (UOM), Baghdad, Iraq
 

This paper describes effect of Skeleton pruning process on recognition shapes or objects, Pruning is accomplished by removing branches which is redundant or unnecessary branches , The removal of these small branches does not alter the shape information. It is still quite a challenging problem because of the lack of standard measurements for the importance or significance of a branch. A pruning technique is adopted to deal with the large amount of data that results in further improvement in recognition accuracy. Experimental results showed that our method can achieve good retrieval results .The moderate pruning ratio has improved the features of the shape and improved recognition accuracy.

Keywords

Pruning, Skeletonization, KNN Classification, Wavelet.
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  • Pruning Process Influences on Recognition Rate of Skeletal Shapes

Abstract Views: 190  |  PDF Views: 0

Authors

Amir S. Almallah
Department of Computer Science, College of Science, University of Mustansiriyah (UOM), Baghdad, Iraq
Esraa Mohammed Hassoon
Department of Computer Science, College of Science, University of Mustansiriyah (UOM), Baghdad, Iraq

Abstract


This paper describes effect of Skeleton pruning process on recognition shapes or objects, Pruning is accomplished by removing branches which is redundant or unnecessary branches , The removal of these small branches does not alter the shape information. It is still quite a challenging problem because of the lack of standard measurements for the importance or significance of a branch. A pruning technique is adopted to deal with the large amount of data that results in further improvement in recognition accuracy. Experimental results showed that our method can achieve good retrieval results .The moderate pruning ratio has improved the features of the shape and improved recognition accuracy.

Keywords


Pruning, Skeletonization, KNN Classification, Wavelet.