2-D Feature-Point Selection and Tracking Using 3-D Physics-Based Deformable Surfaces
This paper presents a novel approach for selecting and tracking feature points in video sequences. In this approach, the image intensity is represented by a 3-D deformable surface model. The proposed approach relies on selecting and tracking feature points by exploiting the so-called generalized displacement vector that appears in the explicit surface deformation governing equations. This vector is proven to be a combination of the output of various line- and edge-detection masks, thus leading to distinct, robust features. The proposed method was compared, in terms of tracking accuracy and robustness, with a well-known tracking algorithm, Kanade-Lucas-Tomasi (KLT), and a tracking algorithm based on scale-invariant feature transform (SIFT) features. The proposed method was experimentally shown to be more precise and robust than both KLT and SIFT tracking. Moreover, the feature-point selection scheme was tested against the SIFT and Harris feature points, and it was demonstrated to provide superior results.