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% 
MidTerm 
35% 
Final 
40% 
Assignments
 Assignment
1 (Due: Monday, 17th November, 2014 before class)
 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
 Introduction
 Computer Vision vs. Image Processing vs. Computer
Graphics
 Computer Vision vs. Biological Vision  The Grand
Deception!
 Successful Computer Vision solutions.
 Image Processing
 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
Spatiotemporal approach of Biguen et al.
 Global Methods
 Variational method of Horn and Shunck
Global
Flow
SIFT and its applications
 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
 Machine Learning for Computer Vision
