ECEn 631 Syllabus

Course Overview

Vision allows us to perceive and understand the surrounding 3-D world. The main properties of 3-D world are geometric and dynamic. The objectives of 3-D computer vision are to compute these properties from one or multiple digital images and to use them to simulate human vision. Human vision is natural and seems easy but computer mimicry of this is difficult. Camera modeling, image processing techniques, and geometry models of single and multiple view systems are three components of 3-D computer vision and they will be studied in this course.

The primary purpose of this course is not to give an exhaustive review of image processing techniques, but to cover methods commonly found in 3-D vision such as dealing with image noise, feature extraction, 3-D object representation and image matching. A few mathematical concepts that are crucial to understanding of solutions and algorithms will be reviewed to the level of detail necessary to understand the material covered in this course. Computer vision, like other engineering disciplines, has to ground itself in mathematics to study or solve its problems. For each problem analyzed in this course, a theoretical treatment of the problem and the mathematical model will be shown and algorithms to the solutions of the problem will be derived. The practical applicability of the algorithms will also be discussed as case studies.

Course Objectives

Compute 3-D characteristics from one or multiple views in real time with hardware implementation for small autonomous vehicles (air, ground, surface, or underwater).

Study methods of using passive sensors for
    – Stereo Vision
    – Motion Analysis
to Achieve
    – 3-D information extraction
    – Static obstacle avoidance
    – Moving obstacle avoidance
    – Short-range Motion Estimation
    – Long-range Motion Estimation
    – …………………
through

- System on a Chip using FPGA
- Real-time vision algorithms

- Visual C++ and OpenCV

“A rough, quickly calculated motion estimation is arguably more useful for navigation than a more accurate, but slowly calculated estimate”
- J. Kolodko and L Vlacic (authors of "Motion Vision: Design of Compact Motion Sensing Solutions for navigation of Autonomous Systems").

References

Introductory Techniques for 3-D Computer Vision

Emanuele Trucco & Alessandro Verri

Prentice Hall, 1998

An Invitation to 3-D Vision - From Images to Geometric Models
Yi Ma, Stefano Soatto, Jana Kosecka, Shankar Sastry

Springer, 2004

Grading

Participation (5%): class discussion
Assignments (25%): 5 assignments
Two exams (20%): two in-class closed book exams
Tennis ball catcher project (20%): team and individual performance
Semester project (30%): team and individual performance
Class & Instructor Evaluation (5% bonus): online evaluation

>= 95: A >= 90: A- >= 85: B+ >= 80: B >= 75: B- <75 : C

Equal Opportunity Statements

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Student With Disabilities

Brigham Young University is committed to providing a working and learning atmosphere which reasonably accommodates qualified persons with disabilities. If you have any disability which may impair your ability to complete this course successfully, please contact the Services for Students with Disabilities Office at 378-2767. Reasonable academic accommodations are reviewed for all students who have qualified documented disabilities. Services are coordinated with the student with the instructor by the SSD Office. If you need assistance or if you feel you have been unlawfully discriminated against on the basis of disability, you may seek resolution through established grievance policy and procedures. You should contact the Equal Employment Office at 378-5895, D-282 ASB.