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.

Important Dates
  • Midterm : Thursday, April 21, during lecture.

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:

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.

Schedule


Lectures


Week of Tuesday Thursday
01/24 Introduction to Computer Vision
 Linear
Algebra Primer
01/31 Least Squares Optimization Ridge Regression /
Eigen values and vectors /
Singular Value Decomposition /
(Quiz)
02/07 Principal Components Analysis (PCA) /
Intro to Image Processing		 Cross-correlation
02/14 Cross-correlation Contd. /
Edge Detection		 Canny Edge Detection
02/21 Harris Corner Detection Scale-Invariant Feature
Transform (SIFT)
02/28 Projective Geometry Projective Geometry Contd. / Homography
03/07 RANSAC / HOG Support Vector Machine (SVM)
03/14 Bag of Visual Words Optical flow
03/21 Spring Break
03/28 Optical flow Contd. Gradient Descent Algorithm
04/04 Logistic Regression Intro to Neural Networks
04/11 Neural Networks Contd. Convolutional Neural Networks
04/18 Convolutional Neural Networks Contd. Midterm
04/25 Deeplearning training Deeplearning Optimizations

Staff

Instructor

Mohammad Nayeem Teli (nayeem at umd.edu)

Office: IRB 1128
Office Hours: MW 3:30 - 4:45 PM


Teaching Assistants


  • Hyekang (Kevin) Joo, hkjoo at umd.edu
  • Kangming Luo, kluo1 at umd.edu
Day
Office hours (AVW 4160 )
Tuesday Kangming: 10:00 - 11:00 AM
Kangming: 1:00 - 2:00 PM
Wednesday Kevin: 11:00 AM - 12:30 PM
Thursday Kangming: 10:00 - 11:00 AM
Kangming: 1:00 - 2:00 PM
Friday Kevin: 11:00 AM - 1:30 PM

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.



Exam Related Material

Homeworks

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


Homework Name
Due Date*
Homework 1 Feb. 15, 2021
Project 1 February 26, 2022
Project 2 March 30, 2022
Project 3 April 15, 2022
Homework 2 May 04, 2022
Homework 3 May 18, 2022

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