| | Standard Course Syllabus | Course Supervisor | Date of Approval |
| | Dept. of Electrical and Computer Engineering | Boyer | Not yet approved by area |
| | 863 | Computer Vision |
| | 2. | CATALOG DESCRIPTION |
| | Computer vision systems, image models, edge detection, feature extraction, shape representation, morphology, structural |
| | descriptions, object modeling, matching, knowledge bases, semantic knowledge, architectures, and depth perception. |
| | Quarters of Offering | Credits | | Level | Class Meeting |
| | Sp Qtr. | 3 | G | 3 cl. |
| | Course Prerequisites |
| | Prereq: 707 or permission of instructor. |
| | 3. | PREREQUISITES BY TOPIC |
| | Fundamentals of digital image processing, signal processing, probability, linear algebra. |
| | Courses that require this as a direct prerequisite |
| | none |
| | 4. | Text(s) and Other Course Materials | Author(s) | Publisher |
| | Computer Vision: A Modern Approach, 2003 | Forsyth and Ponce | Prentice-Hall |
| | ISBN: 0-13-085198-1 (MSH from 707) |
| | References (supplemental reading) |
| | [1] Image Processing, Analysis, and Machine Vision: Sonka, Hlavac, and Boyle |
| | [2] Computer and Robot Vision, Vols. 1&2: Haralick and Shapiro |
| | [3] Computer Vision: Ballard and Brown |
| | [4] Selected Papers, provided. |
| | 5. | COURSE OBJECTIVES |
| | To teach the fundamental concepts in computer vision and to prepare the student to design simple vision systems, to read |
| | the literature, and to commence a research program in computer vision should he or she so desire. Topics include image |
| | models, edge detection, feature extraction, shape representation, structural descriptions, object modeling, matching, |
| | knowledge bases, perceptual organization, architectures, and depth perception. Practice in computer vision concepts and |
| | system design is provided by a term project, conducted in teams and drawn from real-world applications. The project |
| | requires a formal, written report and an oral presentation to the class. |
| | 6. | TOPICS AND (# OF LECTURES) |
| | Computer Vision Overview (1) |
| | Imaging Geometry and Elementary Photogrammetry (3) |
| | Edge Detection (4) |
| | Discrete Geometry (1) |
| | Grouping and Perceptual Organization (6) |
| | Discrete and Probabilistic Relaxation Labeling (4) |
| | Structural Descriptions and Inexact Matching (5) |
| | Stereopsis (3) |
| | Optical Flow and Motion Understanding (3) |
| | 7. | CLASS MEETING PATTERN | (For example, "3cl." means 3 48-min classes per week.) |
| | 3 cl. |
| | Thursday, August 14, 2008 09:24 AM |
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