Improved Signal To Noise Ratio And Computational Speed For Gradient-Based Detection Algorithms

Abstract

Image gradient-based feature detectors offer great advantages over their standard edge-only equivalents. In driver support systems research, the radial symmetry detection algorithm has given real-time results for speed sign recognition. The regular polygon detector is a scan line algorithm for these features facilitating recognition of other road signs such as stop and give way signs. Radial symmetry has also been applied to real-time face detection, and the polygon detector is showing promising results as a feature detector for SLAM. However, gradient-based feature detection is more sensitive to noise than standard edge-based algorithms. As the total gradient magnitude at a pixel decreases, the component of the gradient at that point that arises from image noise increases. When a pixel votes in its gradient direction out to an extended radius, its position is more likely to be inaccurate if the gradient magnitude is low. In this paper, we analyse the performance of the radial symmetry and regular polygon detector algorithms under changes to the threshold on gradient magnitude. We show that the number of pixels correctly voting on a circle is not greatly reduced by thresholds that decrease the total number of pixels that vote in the image to 20%. This greatly reduces the noise component in the image, with only slight impact on the signal. This improves the performance, particularly for the regular polygon detector where the voting mechanism is complex and constitutes a large amount of the processing per pixel. This facilitates a real-time implementation, which is presented here.