Course |
CSE327 |
Title |
Fundamentals of Computer Vision |
Credits |
3 |
Course Coordinator |
Dimitris Samaras
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Description |
Introduces fundamental concepts, algorithms, and techniques in visual information processing. Covers image formation, binary image processing, image features, model fitting, optics, illumination, texture, motion, segmentation, and object recognition.
Bulletin Link
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Prerequisite |
CSE 214 or CSE 230 or CSE 260; AMS 210 or MAT 211; CSE or ISE major
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Course Outcomes |
- Working knowledge of fundamental concepts and computational techniques in visual information processing.
- An ability to design algorithms for understanding images and video, such as segmentation, edge detection, and reflectance analysis.
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Textbook |
Computer Vision: Algorithms & Applications by Richard Szeliski; Springer (ISBN # 978-1848829343)
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Major Topics Covered in Course |
- Computational algorithms and their implementation for the following applications: automatic inspection and measurement based on binary image processing (two-dimensional machine vision).
- Gray-level image processing and analysis techniques (e.g. image segmentation, edge detection, image filtering, curve fitting).
- Three-dimensional shape recovery through stereo image analysis.
- Object recognition based on feature vector classification and template matching.
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Laboratory Projects |
- Image processing: mask-based filtering, median filtering, subsampling, edge-detection (2 weeks)
- Feature detection, Morphing and Mosaicing images (2 weeks)
- Stereo Reconstruction (2 weeks)
- Final project. Select from suggested topcs (4 weeks)
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Course Webpage |
CSE327
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