  Edge detection is widely and fundamental feature used in various algorithms
in computer vision to determine the edges in an image. The edge detection
algorithm is used to determine the edges in an image which are further used by
various algorithms from line detection to machine learning that can determine
objects based on their contour. Inspired by new convolution techniques in
machine learning we discuss here the idea of extending the standard Sobel
kernels, which are used to compute the gradient of an image in order to find
its edges. We compare the result of our custom extended filters with the
results of the standard Sobel filter and other edge detection filters using
different image sets and algorithms. We present statistical results regarding
the custom extended Sobel filters improvements.
