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, March 14, during lecture.
  • Final : Thursday, May 16, 1:30 - 3:30 PM, location:IRB 0318

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


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Week of Tuesday Thursday
01/22 Introduction to Computer Vision
01/29 Linear Algebra Review Prinicpal Components Analysis (PCA)
02/05 SVD and Image Processing Cross-correlation & Convolution
02/12 Edge Detection Canny Edge Detection
02/19 Harris Corner Detection Scale-Invariant Feature
Transform (SIFT)
02/26 Projective Geometry &
Homography		 RANSAC
03/04 Face Detection Histogram of Oriented Gradients
03/11 SVM I Midterm
03/18 Spring Break
03/25 SVM II Optical flow
04/01 Neural Networks Neural Networks Contd.
04/08 NN Contd. Convolutional Neural Networks (CNN)
04/15 CNN Architectures Object detection
04/22 R-CNN Faster R-CNN
04/29 U-Net & Mask R-CNN Autoencoders and Variational Autoencoders
05/08 VAE & GANs Diffusion Models

Staff

Instructor

Mohammad Nayeem Teli (nayeem at umd.edu)

Office: IRB 2224
Office Hours: T TH 3:30 - 4:30 PM


Teaching Assistants


  • Jiayi Wu , jiayiwu at umd.edu
  • Ruibo Chen, rbchen at umd.edu
Day
Office hours (AVW 4140 )
Tuesday Jiayi Wu: 10:00 - 12:00 PM
Wednesday Ruibo: 3:00 PM - 5:00 PM
Thursday Jiayi Wu: 10:00 - 12:00 PM
Friday Ruibo: 3:00 PM - 5:00 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.
  • Gradescope - You will submit your assignments here.


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