Feature Detection, Part 1: Image Derivatives, Gradients, and Sobel Operator

Feature detection in computer vision is introduced by applying calculus concepts to digital images. It explains how image derivatives measure the rate of change in pixel intensity and can be approximated using convolutional kernels to find edges. The image gradient, a vector combining derivatives in both X and Y directions, is used to determine the magnitude and orientation of these intensity changes. The Sobel operator is presented as a more robust 3x3 kernel for approximating these gradients, making it effective for edge detection while being less sensitive to noise.