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Computer Vision Basics

University at Buffalo and State University of New York via Coursera


By the end of this course, learners will understand what computer vision is, as well as its mission of making computers see and interpret the world as humans do, by learning core concepts of the field and receiving an introduction to human vision capabilities. They are equipped to identify some key application areas of computer vision and understand the digital imaging process. The course covers crucial elements that enable computer vision: digital signal processing, neuroscience and artificial intelligence. Topics include color, light and image formation; early, mid- and high-level vision; and mathematics essential for computer vision. Learners will be able to apply mathematical techniques to complete computer vision tasks.

This course is ideal for anyone curious about or interested in exploring the concepts of computer vision. It is also useful for those who desire a refresher course in mathematical concepts of computer vision. Learners should have basic programming skills and experience (understanding of for loops, if/else statements), specifically in MATLAB (Mathworks provides the basics here: Learners should also be familiar with the following: basic linear algebra (matrix vector operations and notation), 3D co-ordinate systems and transformations, basic calculus (derivatives and integration) and basic probability (random variables).

Material includes online lectures, videos, demos, hands-on exercises, project work, readings and discussions. Learners gain experience writing computer vision programs through online labs using MATLAB* and supporting toolboxes.

* A free license to install MATLAB for the duration of the course is available from MathWorks.


  • Computer Vision Overview
    • In this module, we will discuss what computer vision is, the fields related to it, the history and key milestones of it, and some of its applications.
  • Color, Light, & Image Formation
    • In this module, we will discuss color, light sources, pinhole and digital cameras, and image formation.
  • Low-, Mid- & High-Level Vision
    • In this module, we will discuss the three-level paradigm of computer vision that was proposed by David Marr. We will also discuss low, mid, and high level vision.
  • Mathematics for Computer Vision
    • In this lecture, we will discuss the Mathematics used in Computer Vision, which includes linear algebra, calculus, probability, and much more.

Taught by

Radhakrishna Dasari and Junsong Yuan


5.0 rating, based on 1 Class Central review

4.2 rating at Coursera based on 1749 ratings

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  • Shubham Purohit completed this course.

    My knowledge

    Mscit was completed and java + caa
    And msoffic are also completed so if you want to explain someone else to say the least expensive than usual to respond ohk

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