This paper presents an integrated framework for recognizing 3D human posture from 2D images. A flexible combinational algorithm motivated by the novel view expressed by Cyr and Kimia (2004) is proposed to generate the aspects of 3D human postures as the posture prototype using features extracted from the collected 2D images sampled at random intervals from the viewing sphere. Frequency and phase information of the posture are calculated from the Fourier descriptors (FDs) of the sampled points on the posture contour as the main and assistant features to extract the characteristic views as the aspects. Moreover, a modified particle filter is applied to improve the robustness of human posture recognition for continuous monitoring. Experimental trials on synthetic and real sequences have shown the effectiveness of the proposed method.

This paper presents an integrated framework for recognizing 3D objects from 2D images. A flexible combinational algorithm motivated by the novel view expressed by Cyr and Kimia [1] is proposed to generate the aspects of a 3D object as the object prototype using features extracted from the collected 2D images sampled at random intervals from the viewing sphere. Fourier descriptors of the sampled points on the object contour and point-to-point lengths are calculated as the features and similarity metrics are applied to extract the characteristic views as the aspects. Moreover, the object prototype can be integrated from new collected 2D views. Besides, foreground detection with shadow and highlight removal is used to improve the facility of capturing the explicit object efficiently. The effectiveness of the proposed method is demonstrated by experiments with different rigid objects and human postures.