CSE327

Course	CSE327
Title	Fundamentals of Computer Vision
Credits	3
Course Coordinator	Dimitris Samaras
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
Prerequisite	Prerequisites: CSE 214 or CSE 230 or CSE 260; AMS 210 or MAT 211; CSE or ISE or DAS major
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.
Textbook	Computer Vision: Algorithms & Applications by Richard Szeliski; Springer (ISBN # 978-1848829343)
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.
Laboratory	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)
Course Webpage	CSE327