A Fuzzy Hough Transform Approach to Shape Description

To use the Hough transform to detect shapes we need to accumulate votes for the edge passing a specific bin. Most existing Hough transform techniques use a sharp (crisp) cutoff to determine whether the bin has received a vote or not. This results in considerable errors. In this paper, we propose a new line Hough transform (LHT) using evidence accumulation and fuzzy aggregation function. The resulting voting process is dependent on the distance ρ from the grid centers. This effectively handles uncertainty in the accumulation process and achieves a better performance. To show the effectiveness this approach, we present our experimental results for a set of 2D parametric and 3D nonparametric objects.