Lectures
Lecture slides and supporting materials will be added here throughout the semester.
Each lecture is available as an interactive presentation and as a PDF for downloading, printing, or taking notes.
Lecture 01 — Introduction to Computer Vision
Course overview, computer vision applications, and important course logistics.
Lecture 02 — Image Formation and Digital Images
Images as signals, sampling, sensor arrays, resolution, quantization, image arrays, color images, and basic Python image representation.
Lecture 03 — Image Filtering
Local image filtering, correlation, convolution, Gaussian and Sobel filters, separability, template matching, and nonlinear median filtering.
Lecture 04 — Image Pyramids
Image downsampling, aliasing, anti-aliasing, Gaussian image pyramids, Laplacian image pyramids, and multiscale image representations.
Lecture 05 — Fourier Transform
Fourier series, spatial frequency, the discrete Fourier transform, frequency-domain visualization, filtering, convolution, and image sampling.
Lecture 06 — Edge Detection
Image edges, gradients, Gaussian derivatives, and Canny edge detection.