PROJECT TITLE :

Spatial Stimuli Gradient Sketch Model

ABSTRACT:

The inability of automated edge detection methods inspired from primal sketch models to accurately calculate object edges beneath 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 technique and formulation that use perceived brightness and neighbourhood similarity calculations within the determination of robust object edges. The robustness of the detected edges is benchmark against Sobel, SIS, Kirsch, and Prewitt edge detection strategies in an example face recognition downside showing statistically important improvement in recognition accuracy and pixel noise tolerance.