Comparison of HK and SC Curvature Description Methods

This paper compares two different local surface shape description methods. The general goal of surface shape description methods is to classify different surface shapes from range data. One well-known method to classify patches of various shapes is the HK classification space [2, 1, 10]. Another way to classify patches is the SC method introduced by Koenderink [9]. This paper presents several experiments designed to show the (1) qualitatively different classification, (2) the impact of thresholds and (3) the impact of different noise levels. We conclude that Koenderink's approach has some advantages at low thresholds, complex scenes and at dealing with noise. 1 Description of the algorithms Gaussian (K) and Mean (H) curvatures are the most widely used indicators for surface shape classification in range image analysis. The HK segmentation [2, 1, 10] was introduced by Besl in 1986. He used Gaussian and Mean curvatures, which are calculated from the two principal curvatures

• Type: Conference
• Year: 2001
• Where: 3DIM
• Authors: H. Cantzler, Robert B. Fisher
• Keywords: 3DIM 2001, Image Processing, Shape Description Methods, Surface Shape, Surface Shape Description