Edge and Contour

This lecture covers edge and contour detection in images, including gradient-based methods, Laplacian operator, and more complex methods like Kirsch, Marr-Heridith, Frei and Chen, and Canny. It explains the representation of edges and contours, ideal edge detection, noise, and edge detection, as well as different edge detection techniques such as differential methods and template methods. The lecture also delves into the concept of gradient, digital gradient, and edge detection using pixel differencing and symmetrical differencing. It discusses the Laplacian operator, Laplacian of Gaussian, and phase congruency for edge detection, along with contour representation methods like chain coding, Fourier descriptors, and geometric approximations.