Robust Edge Detection using Pseudo Voigt and Lorentzian modulated arctangent kernel

Abstract

Convolution Neural Networks have been the standard neural network architecture for Image Classification and Object Segmentation. Convolutional Neural Network involves a fundamental operation for feature learning on the images which is called as Convolution, where a kernel is convoluted with the corresponding pixel values on the image. Various types of kernels exist which serves different purposes from pixel mapping to edge detection and image blurring. Gaussian-Gabor kernels have been the standard filter for edge detection. This paper presents new robust edge detection filters which produce sharper edge representations as compared to the traditional Gaussian Gabor Filters.