Automatic Multi-categorical Objects Recognition Using Shape Statistical Models
Sun Xian①②③; Wang Hong-qi①②; Yang Zhi-feng②
①Institute of Electronic, Chinese Academy of Sciences, Beijing 100190, China; ②Key Laboratory of Spatial Information Processing and Application System Technology, Chinese Academy of Sciences, Beijing 100190, China; ③Graduate University, Chinese Academy of Sciences, Beijing 100190, China
Abstract：Contour features are powerful cues for human vision system to analyze and identify objects. A new method for automatic multi-categorical objects recognition using shape statistical models is proposed to improve the disadvantages existing in most of the relative methods. This method defines firstly the shape base pairs as feature descriptors, and extracts typical shape base pairs from sample images to build a feature codebook. Then, unsupervised learning is performed to calculate the feature distribution and design class-specific shape models. After detecting the regions and determining the categories quickly, segmentation could be applied to obtain the precise outlines. Experimental results demonstrate that proposed method can achieve high efficiency and accuracy in extracting manifold and complicated objects, and resolve the problems of noise disturbance, rotations at a certain extent.
2009, Vol. 31 
