An efficient edge detection model based on Multi-Scale and Multi-Directional analysis in the Human Visual System

Edge detection is one of the most commonly used operations in image analysis and machine vision. This means that if the edges in an image can be identified accurately and localized properly, all of the objects and elements can be located and basic properties such as area, perimeter, and shape can be measured. One new trend in edge detection starts from knowledge about the Human Visual System (HVS) in order to mimic some of its properties. In this paper we propose a biologically inspired model for efficient edge detection. Our model combines the Gabor filters and the traditional Canny operator. The model consists of applying Multi-Scale and Multi-Directional (MSMD) Gabor filter to the input image, which mimics the processing by simple cells in the primary visual cortex (V1),and can be used to model receptive fields in the retina and primary visual cortex to perform edge detection in computer vision. The proposed method is in good agreement with some classical results in human vision, such as Weber's law and Ricco's law.