Sequential Extraction of Clusters by Fuzzy Hough Transforms.

Modified Hough Transforms for Object Feature Extraction

In this paper, we propose the use of modified Hough transforms to efficiently extract object feature parameters, which are usually contaminated by heavily noisy corrugation and discontinuity. The modified HT (MHT) is developed by introducing spatial and parameter weighting functions to improve the detection performance for the traditional Hough transform (HT), which generally fails to robustly ...

Representing Directions for Hough Transforms

Many algorithms in computer vision operate with directions, i. e. with representations of 3D-points by ignoring their distance to the origin. Even though minimal parametrizations of directions may contain singularities, they can enhance convergence in optimization algorithms and are required e. g. for accumulator spaces in Hough transforms. There are numerous possibilities for parameterizing di...

Generalized Hough Transforms with Flexible Templates

A Probabilistic Algorithm for Computing Hough Transforms

The Hough transform is a common technique in computer vision and pattern recognition for recognizing patterns of points. We describe an efficient probabilistic algorithm for a Monte-Carlo approximation to the Hough transform. Our algorithm requires substantially less computation and storage than the standard Hough transform when applied to patterns that are easily recognized by humans. The prob...

Constrained Hough Transforms for Curve Detection

This paper describes techniques to perform fast and accurate curve detection using constrained Hough transforms, in which localization error can be propagated efficiently into the parameter space. We first review a formal definition of Hough transform and modify it to allow the formal treatment localization error. We then analyze current Hough transform techniques with respect to this definitio...