New Jersey Institute of Technology
Department of Computer Science
CS780 - Computer Vision -
Fall'2004
Monday, 6:00 - 9:05 PM, KUPF 211
Course
Description | Readings | Tentative Contents | Grading
Policy | Miscellaneous
Chengjun Liu, Ph.D.
Phone: 973-596-5280
Email: chengjun.liu
njit.edu
Office: GITC
4306
Hours: MT 3:30PM-5:00PM or by appointment
Course Description
- This course introduces computational models of computer vision
and their implementation on computers, and focuses on material that is
fundamental and has a broad scope of application. Topics include
contemporary
developments in all mainstream areas of computer vision e.g., Image
Formation,
Feature Detection/Representation, Classification and Recognition,
Motion
Analysis, Camera Calibration, 3D/Stereo Vision, Shape From X (motion,
shading,
texture, etc.), and typical applications such as Biometrics.
Readings
- E. Trucco and A. Verri, Introductory
Techniques for 3D Computer Vision, Prentice Hall, 1998.
- D. Forsyth and J. Ponce, Computer
Vision - A modern approach, Prentice Hall, 2003.
- D. Marr, Vision: A Computational
Investigation into the Human Representation and Processing of Visual
Information, Freeman, San Francisco, 1982.
- Selected
papers.
Tentative
Contents
- Introduction
- Computer Vision Fundamentals
- Related Fields: IP, PR, AI
- Image Formation
- Basic Optics
- Basic Radiometry
- Camera Models
- Camera Parameters
- Geometric Feature Representation
- Noise Reduction
- Edge Detection (Canny)
- Corner Detection
- Line & Curve Detection (Hough
Transform),
- Ellipse Detection
- Deformable Contours (snakes)
- Statistical Feature Representation
- Principal Component Analysis (PCA)
- Independent Component Analysis (ICA)
- Shape and Texture
- Gabor Wavelets
- Classification and Recognition Methods
- Bayes Classifier and the MAP Rule
- LDA/FLD
- Kernel Methods (kernel PCA, kernel FLD,
etc.)
- Motion Analysis
- Motion Field
- Optical Flow
- Tracking (Kalman filter, Condensation)
- Structure From Motion
- Biometrics
- Face Detection
- Face Tracking
- Face Recognition
- Camera Calibration
- Intrinsic/Extrinsic Camera Parameters
- Explicit Parameter Calibration
- Projection Matrix-based Parameter Calibration
- 3D Vision - Stereo Vision
- Correspondence
- Epipolar Geometry (E and F matrices)
- 3D Reconstruction
- Shape From X
- Shape From Shading
- Shape From Texture
- Evolutionary Computation for Computer
Vision
- Genetic Algorithms (GA)
- Evolutionary Strategy (ES)
- Evolutionary Programming (EP)
- Neural Computation for Computer Vision
- Multilayer Perceptrons and BP Algorithm
- Radial-Basis Function Networks
- Machine Learning for Computer Vision
- Bayesian Learning
- Decision Tree
- Reinforcement Learning
- Statistical Learning Theory (STL)
- Structural Risk Minimization (SRM)
- Support Vector Machines (SVM)
Grading
Policy
Projects (topics are related
to our course Contents)
Presentation (~10 mins.)
Paper (~10 pages)
Class attendance
Miscellaneous