Summary
	Things to take away from this lecture • What is an edge and where does it come from • Edge detection by differentiation • Image gradients – continuous and discrete – filters (e.g., Sobel operator) • Effects of noise on gradients • Derivative theorem of convolution • Derivative of Gaussian (DoG) operator • Laplacian operator – Laplacian of Gaussian (LoG) • Canny edge detector (basic idea) – Effects of varying sigma parameter • Approximating an LoG by subtraction • Hough Transform