Deformable shape finding with models based on kernel methods

Abstract

In this paper, a new kernel-based deformable model is proposed for detecting deformable shapes. To incorporate valuable information for shape detection, such as edge orientations into the shape representation, a novel scheme based on kernel methods has been utilized. The variation model of a deformable shape is established by a set of training samples of the shape represented in a kernel feature space. The proposed deformable model consists of two parts: a set of basis vectors describing the sample subspace, including the shape representations of the training samples, and a feasibility constraint generated by the one-class support vector machine to describe the feasible region of the training samples in the sample subspace. The aim of the proposed feasibility constraint is to avoid finding some invalid shapes. By using the proposed deformable model, an efficient algorithm without initial solutions is developed for shape detection. The proposed approach was tested against real images. Experimental results show the effectiveness of the proposed deformable model and prove the feasibility of the proposed approach