3D Vision (EE 569)

2020 Fall
Faculty of Engineering and Natural Sciences
Electronics Engineering(EE)
3
10.00
Mehmet Keskinöz -keskinoz@sabanciuniv.edu,
English
Doctoral, Master
--
Formal lecture,Laboratory
Interactive,Learner centered,Discussion based learning,Project based learning,Simulation
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CONTENT

This course deals with a central problem in vision -how to recover 3-D structure and motion from a collection of 2-D images-using techniques mainly from linear algebra and matrix theory. Topics included are: geometric image formation, camera models, image feature extraction and tracking, camera calibration, stereo, epipolar geometry, eight-point algorithm, 3D reconstruction from two or more images, motion estimation etc. The aim is to provide graduate students in EE, CS and ME with a solid theoretical and algorithmic background for research.

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 569, 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
- Define and construct segmentation, feature extraction, and visual motion estimation algorithms to extract relevant information from images
- Construct least squares solutions to problems such as camera calibration, stereo vision, structure from motion and 3D reconstruction
- Apply various approaches to object/shape detection and recognition problems

ASSESSMENT METHODS and CRITERIA

  Percentage (%)
Final 35
Midterm 35
Assignment 20
Individual Project 10

RECOMENDED or REQUIRED READINGS

Readings

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
- An Invitation to 3-D Vision: From Images to Geometric Models, Y. Ma, S. Soatto, J. Kosecka, S. S. Sastry, Springer, 2005