Iterated tensor voting and curvature improvement

Tensor voting (TV) methods have been developed in a series of papers by Medioni and coworkers during the last years. The method has been proved efficient for feature extraction and grouping and has been applied successfully in a diversity of applications such as contour and surface inferences, motion analysis, etc. We present here two studies on improvements of the method. The first one consists in iterating the TV process, and the second one integrates curvature information. In contrast to other grouping methods, TV claims the advantage to be non-iterative. Although non-iterative TV methods provide good results in many cases, the algorithm can be iterated to deal with more complex or more ambiguous data configurations. We present experiments that 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 TV. Unlike the curvature-augmented TV proposed by Tang and Medioni, our method evaluates the full curvature, sign and amplitude in the 2D case. Another advantage of the method is that it uses part of the curvature calculation already performed by the classical TV, limiting the computational costs. Curvature-modified voting fields are also proposed. Results show smoother curves, a lower degree of artifacts and a high tolerance against scale variations of the input. The methods are finally tested under noisy conditions showing that the proposed improvements preserve the noise robustness of the TV method.