Pattern Recognition for Machine Vision

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.)

Instructor(s)

MIT Course Number

9.913

As Taught In

Fall 2004

Level

Graduate

Cite This Course

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.

Archived Versions

Heisele, Bernd, and Yuri Ivanov. 9.913 Pattern Recognition for Machine Vision, Fall 2004. (MIT OpenCourseWare: Massachusetts Institute of Technology), http://ocw.mit.edu/courses/brain-and-cognitive-sciences/9-913-pattern-recognition-for-machine-vision-fall-2004 (Accessed). License: Creative Commons BY-NC-SA


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