Multiscale Edge Detection via Normal Changes

Chwen-Jye Sze, Hong-Yuan Mark Liao, Hai-Lung Hung, Kuo-Chin Fan, Jun-Wei Hsieh

Abstract: A new edge detection technique based on detection of normal changes is proposed. Most of the existing range image-based edge detection algorithms base their detection criterion on depth or curvature changes. However, the depth change-based approach does not have keen sensitivity in detecting roof ( or crease ) edges, and the curvature change-based approach suffers from a complicated and tedious principal curvature derivation process. Using normal changes as a detecting criterion, on the other hand, the existence of an edge can be easily detected, even when the change across a boundary is slight. Experimental results using both synthetic and real images demonstrate that the proposed method can efficiently detect both step and roof edges.