Robust object extraction using normalized principal component features

A new general purpose method for object extraction and detection, RONPaC (Robust Object Extraction (Detection) using NPaC Features) method is presented. RONPaC employs normalized principal component (NPaC) features as a measure of similarity between corresponding regions of a target image and a background image. No a priori knowledge of objects and no assumptions about the environment are required. The object extraction problem is dealt with as a discriminant problem of two classes, 'object' and 'background', in the feature space. The performance of the method is quantitatively evaluated using various real images and compared with conventional methods using two criteria, separability between two classes in feature space and ease of binarizing expressed by the maximum discriminant criterion. Experimental results confirm the extraction accuracy and applicability of the proposed method. >