Archived Versions

9.913 Pattern Recognition for Machine Vision

As taught in: Fall 2004

Series of images illustrating color and position clustering.

Example of color and position clustering: Each pixel is represented by a its color/position features (R, G, B, wx, wy), where w is a constant. Clustering is applied to group pixels with similar color and position. (Image by Dr. Bernd Heisele.)

Level:

Graduate

Instructors:

Dr. Bernd Heisele

Dr. Yuri Ivanov

Course Features

Course Description

The applications of pattern recognition techniques to problems of machine vision is the main focus for this course. Topics covered include, an overview of problems of machine vision and pattern classification, image formation and processing, feature extraction from images, biological object recognition, bayesian decision theory, and clustering.

Technical Requirements

Special software is required to use some of the files in this course: .rm.