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FIT3081 Image processing - Semester 1, 2011

This unit covers fundamental techniques in image processing. Topics include image representation and enhancement, thresholding, image algebra, neighbourhood operations on images, Fourier methods, edge detection, feature extraction and representation, shape, texture, segmentation, classification, restoration, image compression, and colour and multiband image processing.

Mode of Delivery

Sunway (Day)

Contact Hours

2 hrs lectures/wk, 1 hr laboratory/wk, 1 hr tutorial/wk

Workload

  • Lectures: Two 1 hour lectures per week
  • Tutorial: 1 hour tutorials starting week 2
  • Lab Session:  1 hour session starting week 3

and up to an additional 8 hours in some weeks for completing lab and project work, private study and revision.

Unit Relationships

Prohibitions

CSE3314

Prerequisites

FIT2004 (or CSE2304) and FIT2014 (or CSE2303)

Chief Examiner

Loke Kar Seng

Campus Lecturer

Clayton

Sid Ray

Learning Objectives

At the completion of this unit students will have -
Developed the ability to:

  • understand the processes of image formation, acquisition, processing and analysis;
  • develop programs for manipulating grey level, colour and multi-spectral images; and
  • use standard image processing software;
  • undertake computer analysis of medical, remotely-sensed, document, and other images.
Developed attitudes that enable them to:
  • understand the role of visual information processing and analysis; and
  • apply the theory and methods in practical problem solving.
Developed the skills to:
  • write programs to carry out basic image processing tasks such as image denoising, image filtering and segmentation of an image in its constituent parts or objects;
  • write programs to carry out advanced image processing and analysis tasks such as image segmentation, image, image classification, image data mining, and robotic vision; and
  • build a software system for processing and analysis of image data.
Demonstrated the communication and teamwork skills necessary to:
  • function as an image processing specialist in a group which is involved in developing a major software system; and
  • produce appropriate documentation.

Graduate Attributes

Monash prepares its graduates to be:
  1. responsible and effective global citizens who:
    1. engage in an internationalised world
    2. exhibit cross-cultural competence
    3. demonstrate ethical values
  2. critical and creative scholars who:
    1. produce innovative solutions to problems
    2. apply research skills to a range of challenges
    3. communicate perceptively and effectively

    Assessment Summary

    Examination (3 hours): 70%; In-semester assessment: 30%

    Assessment Task Value Due Date
    Assignment 1 10% Week 6, Thursday
    Assignment 2 20% Week 12, Thursday
    Examination 1 70% To be advised

    Teaching Approach

    Lecture and tutorials or problem classes
    This teaching and learning approach provides facilitated learning, practical exploration and peer learning

    Feedback

    Our feedback to You

    Types of feedback you can expect to receive in this unit are:
    • Informal feedback on progress in labs/tutes
    • Graded assignments with comments

    Your feedback to Us

    Monash is committed to excellence in education and regularly seeks feedback from students, employers and staff. One of the key formal ways students have to provide feedback is through SETU, Student Evaluation of Teacher and Unit. The University's student evaluation policy requires that every unit is evaluated each year. Students are strongly encouraged to complete the surveys. The feedback is anonymous and provides the Faculty with evidence of aspects that students are satisfied and areas for improvement.

    For more information on Monash's educational strategy, and on student evaluations, see:
    http://www.monash.edu.au/about/monash-directions/directions.html
    http://www.policy.monash.edu/policy-bank/academic/education/quality/student-evaluation-policy.html

    Previous Student Evaluations of this unit

    If you wish to view how previous students rated this unit, please go to
    https://emuapps.monash.edu.au/unitevaluations/index.jsp

    Required Resources

    Software

    • Java Development Kit
    • Netbeans

    These are freely available from:

    JDK - http://www.oracle.com/technetwork/java/javase/downloads/jdk6-jsp-136632.html

    Netbeans -http://netbeans.org/

    Examination material or equipment

    Writing tools

    Unit Schedule

    Week Date* Activities Assessment
    0 21/02/11   No formal assessment or activities are undertaken in week 0
    1 28/02/11 Image Processing Fundamentals; Arithmetic Operations on Images  
    2 07/03/11 Introduction to Image Enhancement Techniques; Linear Stretching  
    3 14/03/11 Spatial Filtering Methods; Sharpening Filters  
    4 21/03/11 Histogram Equalization; Line and Edge Detection  
    5 28/03/11 Image Thresholding; Image Segmentation Methods  
    6 04/04/11 Clustering-Based Image Segmentation; Region Growing; Splitting and Merging Assignment 1 due Thursday this week
    7 11/04/11 Texture Characterization; Co-occurrence Matrices; Entropy-Based Thresholding  
    8 18/04/11 Image Filtering in Frequency Domain  
    Mid semester break
    9 02/05/11 Image Data Compression  
    10 09/05/11 Image Representation and Description  
    11 16/05/11 Image Recognition I  
    12 23/05/11 Image Recognition II Assignment 2 due Thursday this week
      30/05/11 SWOT VAC No formal assessment is undertaken SWOT VAC

    *Please note that these dates may only apply to Australian campuses of Monash University. Off-shore students need to check the dates with their unit leader.

    Assessment Policy

    To pass a unit which includes an examination as part of the assessment a student must obtain:

    • 40% or more in the unit's examination, and
    • 40% or more in the unit's total non-examination assessment, and
    • an overall unit mark of 50% or more.

    If a student does not achieve 40% or more in the unit examination or the unit non-examination total assessment, and the total mark for the unit is greater than 50% then a mark of no greater than 49-N will be recorded for the unit

    Assessment Tasks

    Participation

    • Assessment task 1
      Title:
      Assignment 1
      Description:
      Programming and Analysis for Image Processing Tasks
      Weighting:
      10%
      Criteria for assessment:
      Due date:
      Week 6, Thursday
    • Assessment task 2
      Title:
      Assignment 2
      Description:
      Programming and Analysis for Image Processing Tasks
      Weighting:
      20%
      Criteria for assessment:
      Due date:
      Week 12, Thursday

    Examinations

    • Examination 1
      Weighting:
      70%
      Length:
      3 hours
      Type (open/closed book):
      Closed book
      Electronic devices allowed in the exam:
      None

    Assignment submission

    Assignment coversheets are available via "Student Forms" on the Faculty website: http://www.infotech.monash.edu.au/resources/student/forms/
    You MUST submit a completed coversheet with all assignments, ensuring that the plagiarism declaration section is signed.

    Extensions and penalties

    Returning assignments

    Policies

    Monash has educational policies, procedures and guidelines, which are designed to ensure that staff and students are aware of the University's academic standards, and to provide advice on how they might uphold them. You can find Monash's Education Policies at:
    http://policy.monash.edu.au/policy-bank/academic/education/index.html

    Key educational policies include:

    Student services

    The University provides many different kinds of support services for you. Contact your tutor if you need advice and see the range of services available at www.monash.edu.au/students The Monash University Library provides a range of services and resources that enable you to save time and be more effective in your learning and research. Go to http://www.lib.monash.edu.au or the library tab in my.monash portal for more information. Students who have a disability or medical condition are welcome to contact the Disability Liaison Unit to discuss academic support services. Disability Liaison Officers (DLOs) visit all Victorian campuses on a regular basis

    READING LIST

    Recommended Reading

    R. C. Gonzalez and R. E. Woods, Digital Image Processing using MATLAB, Prentice Hall, 2004. A. K. Jain, Fundamentals of Digital Image Processing, Prentice-Hall, 1990. W. Niblack, An Introduction to Digital Image Processing, PHI, 1986. D. H. Ballard and C. M. Brown, Computer Vision, Prentice-Hall, 1982. M. D. Levine, Vision in Man and Machine, McGraw?-Hill, 1995. R. Jain, R. Kasturi, and B. G. Schunck, Machine Vision, McGraw?-Hill, 1995. C. Watkins, A. Sadun, and S. S. Marenka, Modern Image Processing: Warping, Morphing, and Classical Techniques, Academic Press, 1993. H. R. Myer and A. R. Weeks, The Pocket Handbook of Image Processing Algorithms in C, Prentice-Hall, 1993. S. E. Umbaugh, Computer Vision and Image Processing: a practical approach using CVIPtools, Prentice Hall PTR, 1998.