Abstract

Tensor voting is an efficient algorithm for perceptual group- ing and feature extraction, particularly for contour extraction. In this paper two studies on tensor voting are presented. First the use of iter- ations is investigated, and second, a new method for integrating curva- ture information is evaluated. In opposition to other grouping methods, tensor voting claims the advantage to be non-iterative. Although non- iterative tensor voting methods provide good results in many cases, the algorithm can be iterated to deal with more complex data configura- tions. The experiments conducted demonstrate that iterations substan- tially improve the process of feature extraction and help to overcome limitations of the original algorithm. As a further contribution we pro- pose a curvature improvement for tensor voting. On the contrary to the curvature-augmented tensor voting proposed by Tang and Medioni, our method takes advantage of the curvature calculation already performed by the classical tensor voting and evaluates the full curvature, sign and amplitude. Some new curvature-modified voting fields are also proposed. Results show a lower degree of artifacts, smoother curves, a high toler- ance to scale parameter changes and also more noise-robustness.