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CAP4453 – Fall 2018

Robot Vision (3 Credit Hours)

Course Contents

The study of mechanical vision is one of the few areas of science which blends one’s intuition with formal methods. Vision (whether in humans or machines) is fundamentally a computational process. Visual processes for machines must be able to deliver the kinds of capabilities that humans have: scene recognition, motion processing, navigational abilities. This course will begin by examining some of the elementary concepts in machine vision. Sub-processes to be examined include: edge detection, methods for obtaining shape information from images, object detection, and motion analysis. The student will also be exposed to unsolved problems in these topics, the solutions to which have very high technological pay-offs.

The workload consists of interesting reading, programming, tests and a project. The class project gives the student intense exposure to one sub-area of machine vision. The student will be guided by the instructor in the choice of project and its execution.

This class is suitable for students in Computer Science and the Engineering disciplines, and anyone else who wishes an introduction to machine vision.


None, all notes will be written on the board day-to-day or handed out in class or provided on the class website. The student is responsible for taking good notes.

Meeting Format

The class will meet twice a week for a 75 min face to face lecture, taught by the instructor

Grading Policy

  • 1 in-class test: 35%
  • 3 programming assignments: 30%
  • 1 project: 35%

The only grades assigned will be W, A, A-, B+, B, B-, C+, C, C-, D, F and I (where appropriate).

Important Notes

  1. You are required to attend every class, except in case of an emergency.
  2. The homework is graded ALL or NOTHING. So, you must get a homework fully correct to get any points. No partial credit.
  3. Sharing of homework code or homework solutions is not permitted. Also, when asked to write a program, you must actually write it yourself, and not obtain it from other sources, such as, some website or students from prior offerings of vision classes. Cheating will be dealt with severely. Also, read the Academic Integrity statement below.


  • Perspective and orthographic projections
  • The processing of edges
  • Regions
  • Motion
  • Shading
  • Texture
  • Object Detection
  • Recognition
  • Machine Learning

Student Learning Outcomes

  1. Student will study and understand a variety of algorithms that will perform specific vision sub-tasks.
  2. Student will get experience with implementing algorithms in vision.
  3. Student will be exposed to current research in computer vision.

Important Dates:

  • Assign 1 – Tuesday, September 11
  • Assign 2 – Tuesday, October 2
  • Test 1 – Tuesday, October 9
  • Project proposal due – Tuesday, October 23
  • Last chance Presentation 1- Tuesday, October 30
  • Assign 3 – Thursday, November 15
  • Interim Project Report – Tuesday, November 27
  • Last chance Presentation 2 – Tuesday, December 4
  • Completed Project Report 3pm – Tuesday, December 4


Statement on Academic Integrity:

Students should familiarize themselves with UCF’s Rules of Conduct. According to Section 1, “Academic Misconduct”, students are prohibited from engaging in unauthorized assistance: Using or attempting to use unauthorized materials, information, or study aids in any academic exercise unless specifically authorized by the instructor of record.