Are Iterations and Curvature Useful for Tensor Voting?

Tensor voting is an efficient algorithm for perceptual grouping and feature extraction, particularly for contour extraction. In this paper two studies on tensor voting are presented. First the use of iterations is investigated, and second, a new method for integrating curvature information is evaluated. In opposition to other grouping methods, tensor voting claims the advantage to be non-iterative. Although noniterative tensor voting methods provide good results in many cases, the algorithm can be iterated to deal with more complex data configurations. The experiments conducted demonstrate that iterations substantially improve the process of feature extraction and help to overcome limitations of the original algorithm. As a further contribution we propose 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 tolerance to scale parameter changes and also more noise-robustness.