Polygon-Invariant Generalized Hough Transform for High-Speed Vision-Based Positioning

Abstract

The generalized Hough transform (GHT) is widely used for detecting or locating objects under similarity transformation. However, a weakness of the traditional GHT is its large storage requirement and time-consuming computational complexity due to the 4-D parameter space voting strategy. In this paper, a polygon-invariant GHT (PI-GHT) algorithm, as a novel scale- and rotation-invariant template matching method, is presented for high-speed object vision-based positioning. To demonstrate the performance of PI-GHT, several experiments were carried out to compare this novel algorithm with the other five popular matching methods. Experimental results show that the computational effort required by PI-GHT is smaller than that of the common methods due to the similarity transformations applied to the scale- and rotation-invariant triangle features. Moreover, the proposed PI-GHT maintains inherent robustness against partial occlusion, noise, and nonlinear illumination changes, because the local triangle features are based on the gradient directions of edge points. Consequently, PI-GHT is implemented in packaging equipment for radio frequency identification devices at an average time of 4.13 ms and 97.06% matching rate, to solder paste printing at average time nearly 5 ms with 99.87%. PI-GHT is applied to LED manufacturing equipment to locate multiobjects at least five times improvement in speed with a 96% matching rate.