3D Vision (EE 569)

2019 Fall
Faculty of Engineering and Natural Sciences
Electronics Engineering(EE)
3
10
Mehmet Keskinöz keskinoz@sabanciuniv.edu,
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English
Doctoral, Master
--
Formal lecture,Laboratory
Interactive,Learner centered,Discussion based learning,Project based learning,Simulation
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Programs\Type Required Core Elective Area Elective
Computer Science and Engineering - With Bachelor's Degree *
Computer Science and Engineering - With Master's Degree *
Computer Science and Engineering - With Thesis *
Cyber Security - With Bachelor's Degree *
Cyber Security - With Master's Degree *
Cyber Security - With Thesis *
Electronics Engineering and Computer Science - With Bachelor's Degree *
Electronics Engineering and Computer Science - With Master's Degree *
Electronics Engineering and Computer Science - With Thesis *
Electronics Engineering - With Bachelor's Degree *
Electronics Engineering - With Master's Degree *
Electronics Engineering - With Thesis *
Energy Technologies and Management-With Thesis *
Industrial Engineering - With Bachelor's Degree *
Industrial Engineering - With Master's Degree *
Industrial Engineering - With Thesis *
Leaders for Industry Biological Sciences and Bioengineering - Non Thesis *
Leaders for Industry Computer Science and Engineering - Non Thesis *
Leaders for Industry Electronics Engineering and Computer Science - Non Thesis *
Leaders for Industry Electronics Engineering - Non Thesis *
Leaders for Industry Industrial Engineering - Non Thesis *
Leaders for Industry Materials Science and Engineering - Non Thesis *
Leaders for Industry Mechatronics Engineering - Non Thesis *
Manufacturing Engineering - Non Thesis *
Manufacturing Engineering - With Bachelor's Degree *
Manufacturing Engineering - With Master's Degree *
Manufacturing Engineering - With Thesis *
Materials Science and Nano Engineering-(Pre:Materials Science and Engineering) *
Materials Science and Nano Engineering-(Pre:Materials Science and Engineering) *
Materials Science and Nano Engineering - With Thesis (Pre.Name: Materials Science and Engineering) *
Mechatronics Engineering - With Bachelor's Degree *
Mechatronics Engineering - With Master's Degree *
Mechatronics Engineering - With Thesis *
Molecular Biology, Genetics and Bioengineering (Prev. Name: Biological Sciences and Bioengineering) *
Molecular Biology, Genetics and Bioengineering-(Prev. Name: Biological Sciences and Bioengineering) *
Molecular Biology,Genetics and Bioengineering-With Thesis (Pre.Name:Biological Sciences and Bioeng.) *
Physics - Non Thesis *
Physics - With Bachelor's Degree *
Physics - With Master's Degree *

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 OUTCOMES

  • 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