Contextual and non-combinatorial approach to feature extraction

Abstract. Extracting features from an image is the first step in many computer vision applications. As nearby features are closely correlated, the joint distribution among features is highly constrained. Feature extraction techniques can take advantage of the correlation to extract a good set of features that satisfies the correlation constraints. Furthermore, they can refine the representation of the extracted features in terms of a set of attributes. To reduce the dimension of the joint PDF, we consider a set of conditional PDFs and maximize them iteratively. It can be shown that the process finds a local maximum of the joint PDF when it is differentiable with respect to each feature attribute. We can apply the approach to many feature extraction tasks. In this paper, we demonstrate our approach with sub-pixel contour representation and surface reconstruction problems.