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Level >Graduate >FALL_2005 >List >

Computer Vision

Course No. CIS 780
Sections 101
Title Computer Vision
Course Website
Prerequisite(s) CS 610 Data Structures and Algorithms
Instructor Chengjun Liu
  • Office Room No. : GITC 4306
  • Office Phone : 973-596-5280
  • Fax : 973-596-5777
  • Email :
  • Website:
  • Lab : Face Recognition and Video Processing Lab
  • Instructor Office Hours
    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.
    Topics Tentative Contents
    Computer Vision Fundamentals
    Related Fields: IP, PR, NN, ML, 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
    Linear Methods (LDA/FLD)
    Kernel Methods (SVM, kernel PCA, kernel FLD, etc.)
    Motion Analysis
    Motion Field
    Optical Flow
    Tracking (Kalman filter, Condensation)
    Structure From Motion
    Face Detection
    Face Tracking

    Face Recognition (FRGC, FRVT)

    Camera Calibration
    Intrinsic/Extrinsic Camera Parameters
    Explicit Parameter Calibration
    Projection Matrix-based Parameter Calibration

    3D Vision - Stereo Vision
    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)
    Text Book(s) Texbook: E. Trucco and A. Verri, Introductory Techniques for 3D Computer Vision, Prentice Hall, 1998.

    Additional Readings:
    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.
    V. N. Vapnik, The Nature of Statistical Learning Theory, 2nd edition, Springer, 2000.
    B. Scholkopf and A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond, MIT Press, 2002.
    Selected papers
    Time & Place Monday 6:00 PM - 9:05 PM,Kupf 107
    Other Info Miscellaneous
    The Computer Vision Homepage
    CVonline: The Evolving, Distributed, Non-Proprietary, On-Line Compendium of Computer Vision
    Vision Science: An Internet Resource for Research in Human and Animal Vision
    Computer Vision Online Publications
    Sussex Computer Vision Teach Files
    The Face Recognition Home Page
    Face Detection Home Page
    Matlab Primer (Third Edition, By Kermit Sigmon - pdf file)
    Matlab Getting Started (copyright Mathworks - pdf file)
    Matlab Image Processing Toolbox User's Guide (copyright Mathworks - pdf file)

    Grading Policy
    Projects (topics are related to our course Contents)
    Presentation (~10 mins.)
    Paper (~10 pages)
    Class attendance

    NJIT Honor Code will be upheld, and any violations will be brought to the immediate attention of the Dean of Students.
    Students will be consulted with by the instructor and must agree to any modifications or deviations from the syllabus throughout the course of the semester.

    NJIT Honor Code will be upheld, and any violations will be brought to the immediate attention of the Dean of Students.

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