This work presents a flexible framework for recognizing 3D objects from 2D views. Similarity-based aspect-graph, which contains a set of aspects and prototypes for these aspects, is employed to represent the database of 3D objects. An incremental database construction method that maximizes the similarity of views in the same aspect and minimizes the similarity of prototypes is proposed as the core of the framework to build and update the aspect-graph using 2D views randomly sampled from a viewing sphere. The proposed framework is evaluated on various object recognition problems, including 3D object recognition, human posture recognition and scene recognition. Shape and color features are employed in different applications with the proposed framework and the top three matching rates show the efficiency of the proposed method.

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 work describes a new 3D cone-shape illumination model (CSIM) and a robust background subtraction scheme involving shadow and highlight removal for indoor-environmental surveillance. Foreground objects can be precisely extracted for various post-processing procedures such as recognition. Gaussian mixture model (GMM) is applied to construct a color-based probabilistic background model (CBM) that contains the short-term color-based background model (STCBM) and the long-term color-based background model (LTCBM). STCBM and LTCBM are then proposed to build the gradient-based version of the probabilistic background model (GBM) and the CSIM. In the CSIM, a new dynamic cone-shape boundary in the RGB color space is proposed to distinguish pixels among shadow, highlight and foreground. Furthermore, CBM can be used to determine the threshold values of CSIM. A novel scheme combining the CBM, GBM and CSIM is proposed to determine the background. The effectiveness of the proposed method is demonstrated via experiments in a complex indoor environment

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.