Data Dimension Reduction for Efficient Feature Extraction in Posture Recognition


The KIPS Transactions:PartB , Vol. 15, No. 5, pp. 435-448, Oct. 2008
10.3745/KIPSTB.2008.15.5.435,   PDF Download:

Abstract

3D posture recognition is a solution to overcome the limitation of 2D posture recognition. There are many researches carried out for 3D posture recognition using 3D data. The 3D data consist of massive surface points which are rich of information. However, it is difficult to extract the important features for posture recognition purpose. Meanwhile, it also consumes lots of processing time. In this paper, we introduced a dimension reduction method that transform 3D surface points of an object to 2D data representation in order to overcome the issues of feature extraction and time complexity of 3D posture recognition. For a better feature extraction and matching process, a cylindrical boundary is introduced in meshless parameterization, its offer a fast processing speed of dimension reduction process and the output result is applicable for recognition purpose. The proposed approach is applied to hand and human posture recognition in order to verify the efficiency of the feature extraction.


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Cite this article
[IEEE Style]
D. W. Kyoung, Y. L. Lee, K. C. Jung, "Data Dimension Reduction for Efficient Feature Extraction in Posture Recognition," The KIPS Transactions:PartB , vol. 15, no. 5, pp. 435-448, 2008. DOI: 10.3745/KIPSTB.2008.15.5.435.

[ACM Style]
Dong Wuk Kyoung, Yun Li Lee, and Kee Chul Jung. 2008. Data Dimension Reduction for Efficient Feature Extraction in Posture Recognition. The KIPS Transactions:PartB , 15, 5, (2008), 435-448. DOI: 10.3745/KIPSTB.2008.15.5.435.