Computer Vision (SE 461)
Fall 2014

Dr. Nazar Khan

Lectures:

	Morning Session (Room 15)	Afternoon Session (Room 15)
Monday	09:45 am - 11:10 am	01:00 pm - 02:30 pm
Wednesday	09:45 am - 11:10 am	01:00 pm - 02:30 pm

Office Hours:

Thursday

2:00 pm - 06:00 pm

Programming Environment: Matlab

Grading Scheme/Criteria:

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

Assignments

• Assignment 1 (Due: Monday, 17th November, 2014 before class)
  • MATLAB Basics for Image Handling A nice tutorial on getting started for this assignment by Nausheen Qaiser.
• Assignment 2 (Due: Monday, 1st December, 2014 before class) First group/person to email me the identity of the person in "mystery.png" alongwith the cleaned up image will win this semester's "Fourier Challenge"!
  Winner of Fall2014 semester: Fazeela Tariq (BSEF10M046)
• Assignment 3 (Due: Monday, 12th January, 2015 before class)
• Assignment 4 (Due: Monday, 26th January, 2015 before class)
• Assignment 5 (Due: Monday, 2nd February, 2015 before class)
• Assignment 6 (Due: Sunday, 8th February, 2015 11:59 pm)

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
   • 2D Spatial Transformations
     • Matrix ≡ Linear Transformation
     • Scaling, Shear, Rotation
     • Translation is not linear in ℝ^2
     • Homogenous Coordinates make translation linear in ℙ^2
     • 2D Affine Transformation (Scaling, Shear, Rotation, Translation)
       • Recovering best affine transformation from correspondences
       • Affine Image Warping
     • 2D Projective Transformation (Homography)
       • Recovering best projective transformation from correspondences -- Direct Linear Transform (DLT)
       • Projective Image Warping
   • Optic Flow
     • Greyvalue/Brightness Constancy Assumption
     • Linearised Optic Flow Constraint via Taylor's Approximation
     • Aperture Problem and Normal Flow
     • Local Methods
     • Spatial approach of Lucas and Kanade
     • Spatio-temporal approach of Biguen et al.
     • Global Methods
     • Variational method of Horn and Shunck
   • Global 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
   • Shape from Shading
   • Structure from Motion
5. Machine Learning for Computer Vision
   • The Bag-of-Words Model
   • Support Vector Machines (SVM)
   • Principal Component Analysis
   • Sparse Coding