EE 6663 - Pattern Recognition

Pattern recognition’s (PR) goal is the recognition and classification of objects, patterns, images, signals, or waveforms into a number of categories or classes. PR is an integral part in most machine intelligence systems designed for decision-making. Rapidly developing technology with cross-disciplinary interest and participation with other areas such as adaptive signal processing, AI, neural net, optimization and estimation, fuzzy sets, structural modeling, and formal languages. PR applications include image and video processing; machine vision; seismic analysis; radar signal classification; face, gait, speech and character recognition; Fingerprint identification; surveillance; navigation; OCR; medicine and biological sciences; CAD; multimedia systems; digital libraries. Addresses three different (statistical, syntactic, and neural-network) approaches to PR problem.

Requisites:
Credit Hours: 3
Repeat/Retake Information: May not be retaken.
Lecture/Lab Hours: 3.0 lecture
Eligible Grades: A-F,WP,WF,WN,FN,AU,I