Computer Vision (EE 417)

2021 Fall
Faculty of Engineering and Natural Sciences
Electronics Engineering(EE)
3
8.00 / 6.00 ECTS (for students admitted in the 2013-14 Academic Year or following years)
Mustafa Ünel munel@sabanciuniv.edu,
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English
Undergraduate
CS201
Formal lecture,Laboratory
Interactive,Discussion based learning,Project based learning,Simulation,Case Study
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CONTENT

Introduction to Computer Vision, Human Visual System, Image Formation, Pointwise Image Operations, Image Intensity Transformations, Geometric/Coordinate Transforms, Interpolation, Image Neighborhood Operations, Spatial Filtering, Edge Detection, Feature Extraction, Principal Component Analysis and Applications, Morphological Image Processing, Basic Segmentation, Thresholding techniques, Motion/Dynamic Scenes, Color and texture, Object/Shape Modeling / Recognition

OBJECTIVE

To teach the fundamentals of 3D computer vision, which tries to make computers see and interpret the world around us by constructing 3D models from 2D (or 3D) images.

LEARNING OUTCOME

Upon successful completion of EE 417 Computer Vision, students are expected to be able to:

- Discuss the main problems of computer vision, its uses and applications
- Design and implement various image transforms: point-wise transforms, neighborhood operation-based spatial filters, and geometric transforms over images
- Design and implement several feature extraction algorithms including edges and corners
- Design and implement line and circle detection using Hough transform
- Calibrate real cameras and determine both intrinsic and extrinsic parameters,
- Formulate and solve 2D optic flow problem
- Establish correct correspondence for stereo images using a correlation based matching technique
- Estimate the essential/fundamental matrix and determine extrinsic parameters (rotation and translation) of a stereo vision system
- Reconstruct 3D structure from 2D images using estimated extrinsic parameters
- Identify or recognize objects from images

ASSESSMENT METHODS and CRITERIA

  Percentage (%)
Exam 30
Assignment 35
Group Project 35

RECOMENDED or REQUIRED READINGS

Textbook

Concise Computer Vision: An Introduction into Theory and Algorithms, Springer, Series: Undergraduate Topics in Computer Science, by Reinhard Klette, 2014. ISBN 978-1-4471-6319-0

Optional Readings

Computer Vision: Algorithms and Applications, R. Szeliski, Springer, 2010