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Computer Vision

Enrollment is Closed

About This Course

Computer vision is concerned with the image processing, geometry, and statistical inference tools necessary for extracting useful information about the world from two-dimensional images. After decades of research, machine vision systems have reached human-level performance in some areas, while other areas are still wide open. This course is an advanced survey of the state of the art in machine vision, focused primarily on applications to robot vision, intelligent video analytics, optical character recognition, and human-computer interfaces. The course is a mixture of lectures on fundamentals, student presentations of research from the primary academic literature, and group projects involving application of machine vision technology to real-world problems. The course prepares students to do thesis research in the field.

On completion of this course, you should be able to:

  • Write computer programs to find point correspondences between different images of a 3D scene; Use noisy point correspondences between images to estimate projective transformations between planes, camera positions and orientations, and the 3D structure of a scene;
  • Apply machine learning techniques for classification to problems involving segmentation, detection, and recognition of people and other objects in video sequences as well as optical character recognition;
  • Apply sequential state estimation techniques to problems involving tracking of people and other objects in video sequences;
  • Implement and evaluate state-of-the-art machine vision algorithms described in the primary academic literature;
  • Integrate the necessary statistical estimation, image processing, and machine learning tools with a custom-designed algorithm to provide a complete solution to an image or video processing problem.


There are no specific prerequisites, but knowledge of linear algebra, probability and statistics, machine learning, deep learning, and computer programming will be helpful.

Course Staff

Matthew Dailey

Matthew Dailey (Main Instructor)

Matthew Dailey is a Professor in the Information and Communication Technologies Department at the Asian Institute of Technology and the Director of the AIT AI Center. He obtained the Ph.D. in Computer Science and Cognitive Science from the University of California, San Diego and the B.S. and M.S. in Computer Science from North Carolina State University. Before entering academia, he worked in several U.S. technology companies involved in practical applications of machine learning, including Vision Robotics Corp., Burning Glass Technologies, and HNC Software.

Matthew can be contacted by email. For discussion, during online learning due to COVID-19, feel free to make an appointment in Google Calendar.

Alisa Kunapinun

Alisa Kunapinun (Teaching Assistant)

Alisa Kunapinun is a Ph.D. candidate in Mechatronics at the Asian Institute of Technology. Her research interests lie in machine learning, especially reinforcement learning, computer vision, and robotics.

Frequently Asked Questions

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See our list of supported browsers for the most up-to-date information.

What programming language will we use?

We will use Python, C++, and Octave (an open source programming environment similar to Matlab).

  1. Course Number

  2. Classes Start

  3. Classes End

  4. Estimated Effort

    25 hours per week