Spatial Stimuli Gradient Sketch Model

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.Comment: accepted for publication in IEEE Signal Processing Letters, 201

Multiscale Image Matting Based Multi-Focus Image Fusion Technique

“…From the images, it can be clearly seen that after the enhancement algorithm, the gradients of the source image were greatly improved. [26], (e,f) contrast enhancement using non-parametric modified histogram equalization (NMHE) [25], (g,h) gradients of (e,f) achieved by SSGSM [26].…”

“…The edges of the images after contrast enhancement is done by a spatial stimuli sketch model (SSGSM) [26] technique, which principally focuses on focal intensity points and edges in an image, and then the unknown region is calculated in the coarse decision maps by implementing the concentrated information in both the activity level maps. The weight of the local stimuli is deliberated by detecting the local variation in the perceived brightness at the respective positions.…”

Intensity gradient based edge detection for pixelated communication systems

“…In these cases, the edge feature is represented in polar coordinates [26, 27]. Accurately finding the object edges in the presence of pixel noise is a challenge of automated edge detection methods for a gradient sketch model [28]. In [28], authors perceive brightness and neighbourhood similarity to determine the object edges.…”

“…Accurately finding the object edges in the presence of pixel noise is a challenge of automated edge detection methods for a gradient sketch model [28]. In [28], authors perceive brightness and neighbourhood similarity to determine the object edges. This method provides robustness with respect to Sobel, Kirsch, and Prewitt edge detection methods [28].…”

“…In [28], authors perceive brightness and neighbourhood similarity to determine the object edges. This method provides robustness with respect to Sobel, Kirsch, and Prewitt edge detection methods [28]. Single edge detection methods have poor gradient change causing problems while detecting edges [29].…”