  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.
