Computer Vision (CS 565)
Spring 2014

Dr. Nazar Khan

Lectures:

Monday	2:30 pm - 4:00 pm	Al Khwarizmi Lecture Theater
Wednesday	2:30 pm - 4:00 pm	Al Khwarizmi Lecture Theater

Office Hours:

Tuesday	03:00 pm - 05:00 pm
Thursday	03:00 pm - 05:00 pm

Programming Environment: Matlab

Grading Scheme/Criteria:

Assignments and Quizes	10%
Project	15%
Mid-Term	35%
Final	40%

Assignments

• Assignment 1 (Due: Wednesday, 2nd April, 2014 before class)
  • MATLAB Basics for Image Handling A nice tutorial on getting started for this assignment by Nausheen Qaiser.
• Assignment 2 (Due: Wednesday, 16th April, 2014 before class)
• Assignment 3 (Due: Wednesday, 28th May, 2014 before class)
• Assignment 4 (Due: Wednesday, 18th June, 2014 before class)

Content

1. Introduction
   • Computer Vision vs. Image Processing vs. Computer Graphics
   • Computer Vision vs. Biological Vision -- The Grand Deception!
   • Successful Computer Vision solutions.
2. Image Processing
   • Image Types and their degradations
   • Color Perception and Color Spaces
   • Filtering and Convolutions
     • Gaussian Smoothing
     • Derivative Filtering
   • The Fourier Transform
     • Basic Idea and Basic Math
     • Euler's Formula for Circular Motion
     • Projection onto Circular Motion Basis
     • Frequency Domain Filtering
   • Image Adjustment
   • Mathematical Morphology
3. 2D Computer Vision
   • Edge Detection
   • Corner Detection
     • Moravec Corner Detector
     • Harris Corner Detector
     • Structure Tensor -- Geometry and Algebra
   • Hough Transform
   • Line Segment Detection
   • 2D Spatial Transformations
   • Optical Flow
   • SIFT and its applications
4. 3D Computer Vision
   • Projective Geometry and Camera Models
   • Pinhole Camera Geometry
   • Camera Matrix = Intrinsic x Projection x Extrinsic
   • Camera Models
   • Camera Matrix Anatomy
   • Camera Calibration
   • Stereo Reconstruction
   • Orthoparallel Cameras
   • Converging Cameras
   • Epipolar Constraint and Fundamental Matrix
   • Estimation of Fundamental Matrix
   • Disparity Estimation
   • Structure from Motion
5. Machine Learning for Computer Vision
   • The Bag-of-Words Model
   • Support Vector Machines (SVM)
   • Principal Component Analysis
   • Sparse Coding