Spatial Stimuli Gradient Sketch Model

TL;DR

This paper introduces a novel edge detection method based on perceptual laws, improving robustness against noise and enhancing face recognition accuracy compared to traditional methods.

Contribution

It extends primal sketch models by incorporating Weber-Fechner and Shepherd laws to improve edge detection under noisy conditions.

Findings

Significant improvement in face recognition accuracy.

Enhanced noise tolerance over traditional edge detectors.

Statistically validated robustness of the proposed method.

Abstract

The inability of automated edge detection methods inspired from primal sketch models to accurately calculate object edges under the influence of pixel noise is an open problem. Extending the principles of image perception i.e. Weber-Fechner law, and Sheperd similarity law, we propose a new edge detection method and formulation that use perceived brightness and neighbourhood similarity calculations in the determination of robust object edges. The robustness of the detected edges is benchmark against Sobel, SIS, Kirsch, and Prewitt edge detection methods in an example face recognition problem showing statistically significant improvement in recognition accuracy and pixel noise tolerance.