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    CoSc684 Computer Vision and Image Processing Course Outline

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    Gute
    This 3 credit hour course on Computer Vision and Image Processing introduces fundamental concepts and techniques in 2D digital image processing with an emphasis on image processing, filtering, and applications. The course aims to give students a clear understanding of key terminology and the mathematical foundations for digital image manipulation. Students will learn to use Matlab to manipulate, process, segment, and compress images as well as understand image enhancement and restoration in both the spatial and frequency domains. Assessment is based on quizzes, assignments, projects, a midterm and final exam. Required textbooks cover foundational concepts in digital image processing.

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    CoSc684 Computer Vision and Image Processing Course Outline

    Uploaded by

    Gute
    87 views3 pages
    This 3 credit hour course on Computer Vision and Image Processing introduces fundamental concepts and techniques in 2D digital image processing with an emphasis on image processing, filtering, and applications. The course aims to give students a clear understanding of key terminology and the mathematical foundations for digital image manipulation. Students will learn to use Matlab to manipulate, process, segment, and compress images as well as understand image enhancement and restoration in both the spatial and frequency domains. Assessment is based on quizzes, assignments, projects, a midterm and final exam. Required textbooks cover foundational concepts in digital image processing.

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    Image Processing

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    This 3 credit hour course on Computer Vision and Image Processing introduces fundamental concepts and techniques in 2D digital image processing with an emphasis on image processing, filtering, and applications. The course aims to give students a clear understanding of key terminology and the mathematical foundations for digital image manipulation. Students will learn to use Matlab to manipulate, process, segment, and compress images as well as understand image enhancement and restoration in both the spatial and frequency domains. Assessment is based on quizzes, assignments, projects, a midterm and final exam. Required textbooks cover foundational concepts in digital image processing.

    Copyright:

    © All Rights Reserved
    Download as PDF, TXT or read online from Scribd
    Download as pdf or txt
    87 views3 pages

    CoSc684 Computer Vision and Image Processing Course Outline

    Uploaded by

    Gute
    This 3 credit hour course on Computer Vision and Image Processing introduces fundamental concepts and techniques in 2D digital image processing with an emphasis on image processing, filtering, and applications. The course aims to give students a clear understanding of key terminology and the mathematical foundations for digital image manipulation. Students will learn to use Matlab to manipulate, process, segment, and compress images as well as understand image enhancement and restoration in both the spatial and frequency domains. Assessment is based on quizzes, assignments, projects, a midterm and final exam. Required textbooks cover foundational concepts in digital image processing.

    Copyright:

    © All Rights Reserved
    Download as PDF, TXT or read online from Scribd
    Download as pdf or txt
    of 3

    Course Title: Computer Vision and Image Processing

    Course Code: CoSc684


    Credit Hours: 3
    Prerequisite(s): None
    Course Description: This course is designed to give students all the fundamentals in 2-D digital
    image processing with emphasis in image processing techniques, image filtering design and
    applications.
    Learning Outcomes: On successful completion of this course, students will:
    x Have a clear understanding of the principals the Digital Image Processing terminology
    used to describe features of images.
    x Have a good understanding of the mathematical foundations for digital manipulation of
    images; image acquisition; preprocessing; segmentation; Fourier domain processing,
    compression and analysis.
    x Be able to write programs using Matlab language for digital manipulation of images;
    image acquisition; preprocessing; segmentation; Fourier domain processing; and
    compression.
    x Have knowledge of the Digital Image Processing Systems.
    x Be able to understand the documentation for, and make use of, the MATLAB library and
    MATLAB Digital Image Processing Toolbox (IPT).
    x Learn and understand the Image Enhancement in the Spatial Domain.
    x Learn and understand the Image Enhancement in the Frequency Domain.
    x Understand the Image Restoration, Compression, Segmentation, Recognition,
    Representation and Description.

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    Course Content:

    1. Introduction
    x Elements of visual perception
    x Image sensing and acquisition
    x Image sampling and quantization
    x Linear and nonlinear representation
    x Digital image representation
    2. Image Enhancement
    x Enhancement in spatial domain
    i. Grey level transformation
    ii. Histogram processing
    iii. Smoothing and sharpening Spatial filters
    x Enhancement in frequency domain
    i. Fourier transform
    ii. Smoothing and sharpening frequency domain filtering
    iii. Homomorphism filtering
    3. Morphological Image processing
    x Dilation and Erosion
    x Morphological algorithms
    4. Image segmentation
    x Detection of discontinuities
    x Boundary detection
    x Thresholding
    5. Object recognition
    x Patterns and pattern classes
    x Decision Theoretic Methods
    x Structural Methods
    6. OCR Identification

    Teaching Strategy: The course will be delivered in the form of lectures, demonstration,
    seminars, student presentations, group discussions, and individual and group project works.
    Assessment Method: The evaluation shall be based on both formative and summative
    assessment which includes:
    x Lecture:
    i. Quizzes / Test / Assignments / others 20 %
    ii. Mid Examination 20%
    iii. Final Examination 30%
    x Practice:
    i. Project 30%

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    Course requirement:

    x Students should:
    i Attend lectures and lab session
    i Work in team on group work
    i Participate in group discussion
    i Discusses with the instructor on topics of interest for project work.
    i Deliver and presents project work.
    i Attend quiz, midterm and final examination.

    Reading Materials:

    1. Gonzalez, R. C. and Woods, R. E. 2002/2008, Digital Image Processing, 2nd/3rd ed.,


    Prentice Hall
    2. Sonka, M., Hlavac, V., Boyle, R. Image Processing, Analysis and Machine Vision (2nd
    edition), PWS Publishing, or (3rd edition) Thompson Engineering, 2007
    3. Gonzalez, R. C., Woods, R. E., and Eddins, S. L. [2009]. Digital Image Processing Using
    MATLAB, 2nd ed., Gatesmark Publishing, Knoxville, TN.
    4. Anil K. Jain 2001, Fundamentals of digital image processing (2nd Edition), Prentice-
    Hall, NJ
    5. Willian K. Pratt [2001], Digital Image Processing (3rd Edition), , John Wiley & Sons,
    NY
    6. Burger, Willhelm and Burge, Mark J. [2008]. Digital Image Processing: An Algorithmic
    Introduction Using Java, Springer

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