Introduction to Computer Vision

Instructor: Subhransu Maji, Offering: Spring 2019

University of Massachusetts, Amherst


This introductory computer vision class will address fundamental questions about getting computers to "see" like humans. We investigate questions such as -- How are images represented in a computer? How can we detect simple structures in images? How can we write algorithms to recognize an object? How can humans and computers "learn to see better" from experience? We will write a number of basic computer programs to do things like recognize handwritten characters, align images to create panoramas, and understand the structure of images.

The course will introduce a number of key concepts, techniques and algorithms. The focus will be on the mathematical foundations rather than the use of software packages as a black box. The course requires appropriate mathematical background in probability, statistics, calculus, linear algebra, and programming. Prior familiarity with Matlab or Python will be helpful, but not required. Students will be taught basic programming in these languages during the course. The course has the following official prerequisites: CMPSCI 240 or CMPSCI 383 with a 'C' or better.


Subhransu Maji

  • Office hours: Tue 12:45-1:45, CS 274

Tsung-Yu Lin (TA)

  • Office hours, CS207:
    • Wed 4:00-5:00
    • Thu 2:00-3:00


  • Class hours: Tuesday/Thursday 11:30AM - 12:45PM
  • Location: Engineering Laboratory (ELAB) 323
  • Moodle for homework.
  • Piazza for announcements and discussions.
  • Gradescope for submissions and grades.


  • 55% Mini projects (5 in total)
  • 15% Mid-term exam
  • 25% Final exam
  • 5% Class participation

Accommodation statement

The University is committed to providing an equal educational opportunity for all students. If you have a documented physical, psychological, or learning disability on file with Disability Services (DS), you may be eligible for reasonable academic accommodations to help you succeed in this course. If you have a documented disability that requires an accommodation, please notify me within the first two weeks of the semester so that we may make appropriate arrangements.

Academic honesty

Since the integrity of the academic enterprise of any institution of higher education requires honesty in scholarship and research, academic honesty is required of all students. Students are expected to be familiar with this policy and the commonly accepted standards of academic integrity (


Many of the slides and homework assignments are based on excellent computer vision courses taught elsewhere by Svetlana Lazebnik, Alyosha Efros, Alexander Berg, Steven Seitz, James Hays, Charless Fowlkes, Kirsten Grauman and many others. Many thanks to Richard Szeliski for making the textbook available online for free.