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Rotation Invariant 3D Star Skeleton Feature Extraction  

Chun, Sung-Kuk (숭실대학교 미디어학과)
Hong, Kwang-Jin (숭실대학교 미디어학과)
Jung, Kee-Chul (숭실대학교 미디어학과)
Publication Information
Journal of KIISE:Software and Applications / v.36, no.10, 2009 , pp. 836-850 More about this Journal
Abstract
Human posture recognition has attracted tremendous attention in ubiquitous environment, performing arts and robot control so that, recently, many researchers in pattern recognition and computer vision are working to make efficient posture recognition system. However the most of existing studies is very sensitive to human variations such as the rotation or the translation of body. This is why the feature, which is extracted from the feature extraction part as the first step of general posture recognition system, is influenced by these variations. To alleviate these human variations and improve the posture recognition result, this paper presents 3D Star Skeleton and Principle Component Analysis (PCA) based feature extraction methods in the multi-view environment. The proposed system use the 8 projection maps, a kind of depth map, as an input data. And the projection maps are extracted from the visual hull generation process. Though these data, the system constructs 3D Star Skeleton and extracts the rotation invariant feature using PCA. In experimental result, we extract the feature from the 3D Star Skeleton and recognize the human posture using the feature. Finally we prove that the proposed method is robust to human variations.
Keywords
posture recognition; gesture recognition; PCA; feature extraction; clustering algorithm; multi-view environment;
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