Course Description
Low-level and high-level vision including edge detection, connected component labeling, boundary detection, segmentation, stereopsis, motion analysis, and object recognition. Knowledge representation, knowledge retrieval and reasoning techniques in computer vision. Parallel computing, parallel architectures and neural computing for computer vision.
Athena Title
COMPUTER VISION
Prerequisite
CSCI 4810/6810 or permission of department
Semester Course Offered
Not offered on a regular basis.
Grading System
A - F (Traditional)
Course Objectives
The student is expected to develop a clear understanding of the algorithms and paradigms involved in extracting high-level semantic information from low-level image data. Although the course is aimed at computer science students, students in other disciplines that entail automated analysis of visual/image data would also benefit from this course.
Topical Outline
Introduction to Computer Vision and Pattern Recognition, Image processing basics, Region analysis, Edge detection, Image segmentation, Feature Extraction, Techniques for extracting 3D shape information from 2D images, Image texture analysis, Motion analysis, and Object recognition. In addition to the above topics covered from the textbook, there will be papers assigned from the current research literature. Students are also expected to work on a self-directed project either individually or in a small group. The students are expected to present their project towards the end of the semester and also submit a technical report/term paper.