CMSC 426 - Computer Vision

Section 0201:

This course offers an introduction to Computer Vision and Computational Photography. The course will cover basic principles of Image Processing, Multiple View Geometry for Visual Navigation, and Image Recognition using Classical and Deep Learning . It will explore the topics of image formation , image feature, image stitching, image and video segmentation, motion estimation, tracking, and object and scene recognition. The course is intended for anyone interested in processing images or video, or interested in acquiring general background in real-world perception. The course is , organized around a number of projects. Through these projects you will learn the theory and practical skills required in jobsof computer vision engineering.

Course Information

All concepts will be covered in class lecture, and in the lecture notes. However, we also recommend the following books as good references:

  • Computer Vision: Algorithms and Applications, Richard Szeliski, Springer, 2010 Online version
  • Computer Vision: A Modern Approach: D. Forsythe and J. Ponce, Prentice-Hall, 2003 (available online)
  • Digital Image Processing, Prentice Hall, Rafael Gonzalez, and Richard Woods, 2008.
  • Multiple View Geometry in Computer Vision, Cambridge University Press, Richard Hartley, and Andrew Zisserman, 2003.


Exam Dates:

  • Midterm: Thursday, April 23, during lecture


Week of Tuesday Thursday
01/27 Introduction to Computer Vision Linear Algebra / Least Squares
(normal Equation)
02/03 Ridge Regression /
Eigen values and vectors
Singular Value Decomposition
02/10 Principal Components Analysis (PCA) K-Means Algorithm /
Gaussian Mixture Models (GMM)
02/17 Expectation-Maximization
(Ref: Pattern recognition and
Machine Learning by Bishop)
Intro to Image Processing
02/24 Cross-Correlation / Edge detection Edge detection / Canny edge detection
03/02 Harris corner detection Scale-Invariant Feature
Transform (SIFT)
03/09 Speeded Up Robust Features (SURF) SURF
03/16 Spring Break
03/23 Class Suspended
03/30 Histogram of Gradients (HOG) Support Vector Machines (SVM) /
Mathematics behind SVM's
04/06 SVM Contd. Projective Geometry /
04/13 Homography conclusion /
Hough Transform
Image Motion
04/20 Texture /
Bag of Visual Words (BoVW)
04/27 Bag of Visual Words (BoVW) Contd., /
Image Recognition
Gradient Descent
05/05 Neural Networks /
Convolution Neural Networks
Intro to DeepLearning / Tensorflow Tutorial
05/05 Tensorflow Tutorial Contd. / Wrap Up


Instructor: Mohammad Nayeem Teli (nayeem at

Office: IRB 1128
Office Hours: Tu/Th 2:00 - 3:00 PM

Teaching Assistants

Name Email Office hours*
Inyeob Kim Friday 2:00 - 4:00 PM
Vaishnavi Patil Monday 2:00 - 4:00 PM
Wednesday 4:00 - 6:00 PM

*All TA office hours take place in IRB 5234 open area (across from IRB 5234). Please note that a TA may need to leave 5 minutes before the end of the hour in order to go to his/her class. Please be understanding of their schedules.

Class Resources

Online Course Tools
  • ELMS - This is where you can see your final grades and homework solutions.
  • Piazza - This is the place for class discussions. Please do not post homework solutions here.

Background Material
The following web pages provide some background and other helpful information.

  • Computer Vision Compendium CVonline
  • Fundamentals on image processing pdf

Exam Related Material


Click the name of an assignment below to see its specifications.

Homework Name
Due Date*
Homework 1 Feb. 14, 2020
Project 1 Feb. 28, 2020
Project 2 March 15, 2020     March 28, 2020
Homework 2 April. 13, 2020
Project 3 May 05, 2020
Project 4 May 20, 2020

* All homeworks are due at 11:59 PM on the due date.