| Summary: | 博士 === 國立交通大學 === 電機與控制工程系所 === 96 === This work presents a framework for robust recognizing 3D objects from 2D views. The proposed framework comprises of two stages: the pre-processing stage and the incremental database construction stage. In the pre-processing stage, foreground objects is extracted from 2D views and applied for building 3D database and recognizing. In the incremental database construction stage, a 3D object database is built and updated using 2D views randomly sampled from a viewing sphere.
A background subtraction scheme involving highlight and shadow removal (BSHSR) is proposed as the pre-processing stage of the framework. Foreground regions can be precisely extracted from 2D views using the BSHSR despite illumination variations and dynamic background. The BSHSR comprises three models, called the color-based probabilistic background model (CBM), the gradient-based version of the color-based probabilistic background model (GBM) and a cone-shape illumination model (CSIM). The Gaussian mixture model (GMM) is applied to construct the CBM using pixel statistics. Based on the CBM, the short-term color-based background model (STCBM) and the long-term color-based background model (LTCBM) can be extracted and applied to build the GBM. Furthermore, a new dynamic cone-shape boundary in the RGB color space, called the CSIM, is proposed to distinguish pixels among shadow, highlight and foreground.
An incremental database construction method based on similarity-based aspect-graph (ISAG) is proposed for building the 3D object database using 2D views. Similarity-based aspect-graph, which contains a set of aspects and characteristic views 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 imitate the ability of human cognition, 2D views randomly sampled from a viewing sphere are applied for building and updating a 3D object database. The effectiveness of the BSHSR is demonstrated via experiments with several video clips collected in a complex indoor environment. The BSHSR is applied in the proposed framework to extract foreground object from 2D views. The proposed framework is evaluated on various 3D object recognition problems, including 3D rigid recognition, human posture recognition, and scene recognition. Shape and color features are employed in different applications with the proposed framework to show the efficiency of the proposed method.
|
|---|
