Introduction to the principles and practice of computer vision. This course will provide in-depth survey of topics involved in major tasks in modern vision, and offer hands-on experience in implementing some of them.


A good background in linear algebra and probability (undergrad level) and background in machine learning (TTIC 31020 or equivalent) required. CMSC 25040 is recommended.


  • Image formation, representation, and compression
  • Representation and perception of color
  • Filtering and edge detection
  • Image features, detectors, and interest point operators
  • Model fitting, RANSAC and Hough transform
  • Stereo and multi-view geometry
  • Camera calibration
  • Representation and perception of motion
  • Representation and modeling of edges and regions
  • Semantic vision: recognition, detection, and related tasks

Expected Outcomes

  • Familiarity with models for of image formation and image analysis.
  • Familiarity with major methods for image processing, alignment, and matching.
  • Knowledge of principles and methods of 3D reconstruction from images and videos, and ability to build and diagnose implementations of these methods.
  • Grasp of modern methods for object and scene categorization from images, and ability to build and diagnose implementations of such methods.

Time and Venue

Lectures on Mon, Wed 1:30pm - 2:50pm, at TTIC 523. First two weeks will be remote. Afterwards we will resume in-person instruction.


week 1Mon 01-10General intro; what is [computer] vision?
Mon 01-10Assignment 1: Warmup
Wed 01-12Camera models
week 2Mon 01-17MLK day break
Wed 01-19Single view metrology; Homographies
Fri 01-21Assignment 2: Rendering, Homography and Calibration
week 3Mon 01-24Camera Calibration
Wed 01-26SfM Intro: Triangulation and Epipolar Geometry
week 4Mon 01-31Structure from Motion
Wed 02-02SfM; Stereo Matching
Sun 02-06Assignment 3: Structure from Motion
week 5Mon 02-07Depth from Stereo and Mono
Wed 02-09Photometry
week 6Mon 02-14Photometric Stereo; Intro to Filtering
Wed 02-16Image Filtering (bilateral, non-local, etc)
week 7Mon 02-21Edge Detection
Wed 02-23Hough Transform, RANSAC, Keypoints
Fri 02-25Assignment 4: Photometry and Feature Matching
week 8Mon 02-28Scale-space Keypoint Detectors; SIFT, Harris-Laplace
Wed 03-02Recognition; Object Detection
Fri 03-04Project
week 9Mon 03-07Motion and Optical Flow
Wed 03-09Color; Multiview Stereo and NeRF
week 10Mon 03-14Reading Period
Wed 03-16End of Quarter

Made with ❤  by Yours Truly.