Image Processing and Analysis

Lecture 10: The instructor explains using neighborhood operations to access different pixels. Also covers implementation of a correlation filter.

Lecture 08: The second of two methods is presented for grouping pixels in an image. The lecture covers deformable contours, parameterizations and gradient descent.

Lecture 07: One method for grouping pixels in an image is presented. Carmichael discusses pairwise coherence, cluster modeling and modeling with metric spaces.

Lecture 06: Carmichael explains how to go about detecting certain objects within an image if those objects cannot be outlined with just rectangles.

Lecture 05: Carmichael discusses finding certain rectangular objects within an image, and explains various methods for doing so, such as PCA (principal component analysis) and dimensionality reduction.

Lecture 04: What it means to have a region of an image be 'salient' and the algorithms for finding such areas.

Lecture 03: The lecture covers edge and corner detection using the Canny and Harris corner detector methods.

Lecture 02: Introduction to Fourier analysis, as well as the subject of wavelets.

Lecture 01: An introduction to image processing and analysis; covers image processing on a broad scope as well as course logistics.