Parametric and Non-parametric Methods for Linear Extraction

Abstract

This article presents two new approaches, one parametric and one non-parametric, to the linear grouping of image features. They are based on the Bayesian Hough Transform, which takes into account feature uncertainty. Our main contribution are two new ways to detect the most significant modes of the Hough Transform. Traditionally, this is done by non-maximum suppression. However, in truth, Hough bins measure the likelihoods not of single lines but of collection of lines. Therefore finding lines by non-maxima suppression is not appropriate. This article presents two alternatives. The first method uses bin integration, automatic pruning and fusion to perform mode detection. The second approach detects dominant modes using variable bandwidth mean shift. The advantages of these algorithms are that: (1) the uncertainties associated with feature measurements are taken into account during voting and mode estimation (2) dominant modes are detected in ways that are more correct and less sensitive to errors and biases than non-maxima suppression. The methods can be used with any feature type and any associated feature detection algorithm provided that it outputs a feature position, orientation and covariance matrices. Results illustrate the approaches.