ELEC ENG 7060 - Image Sensors & Processing

North Terrace Campus - Semester 2 - 2015

This course is an introduction to image processing and image analysis techniques and concepts. Areas examined include, imaging sensors and principles; Image representation and storage, coding and compression techniques, lossy versus lossless; Techniques for reducing noise in images. Image enhancement including contrast manipulation, histogram equalization, edge highlighting; Filtering and transform techniques for image processing including two dimensional Fourier transforms, wavelets and convolution; Spational transformations and registration. Segmentation and thresholding techniques; Applications of morphology to image processing including erosion and dilation operations for binary and grey scale images; Image feature estimation such as edges, lines, corners, texture and simple shape measures. Object classification, template matching techniques and basic image based tracking will also be examined

  • General Course Information
    Course Details
    Course Code ELEC ENG 7060
    Course Image Sensors & Processing
    Coordinating Unit School of Electrical & Electronic Engineering
    Term Semester 2
    Level Postgraduate Coursework
    Location/s North Terrace Campus
    Units 3
    Contact Up to 4 hours per week
    Available for Study Abroad and Exchange Y
    Assumed Knowledge Basic knowledge of linear systems, transform theory & signal processing
    Course Description This course is an introduction to image processing and image analysis techniques and concepts.
    Areas examined include, imaging sensors and principles; Image representation and storage, coding and compression techniques, lossy versus lossless; Techniques for reducing noise in images. Image enhancement including contrast manipulation, histogram equalization, edge highlighting; Filtering and transform techniques for image processing including two dimensional Fourier transforms, wavelets and convolution; Spational transformations and registration. Segmentation and thresholding techniques; Applications of morphology to image processing including erosion and dilation operations for binary and grey scale images; Image feature estimation such as edges, lines, corners, texture and simple shape measures. Object classification, template matching techniques and basic image based tracking will also be examined
    Course Staff

    Course Coordinator: Dr Danny Gibbins

    Course Co-ordinator & lecturer: Dr. Danny Gibbins
    Email: danny.gibbins@adelaide.edu.au
    Office: Ingkarni Wardli 2.24
    Phone: 8313 3162
    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    Course Learning Outcomes
    After completion of this course, students should be able to:

    1.  Demonstrate a broad understanding of the standard image processing issues and analysis techniques used in the commercial and scientific community.

    2.  Perform techniques to enhance contrast and colour, and thereby the visual perception, of contrast degraded imagery.   

    3. Remove noise and other imaging artefacts from real-world imagery using a variety of filtering techniques in both the spatial and frequency domain.

    4.  Demonstrate an understanding of spatial re-sampling, linear spatial transforms and optical flow techniques.

    5.  Employ such techniques to re-sample imagery and accurately register pairs of images.

    6.   Apply and understand image analysis techniques to imagery in order to detect structures such as edges, lines and corners.

    7.   Detect/Extract regions of interest from an image using various thresholding and segmentation techniques and employ morphological filtering techniques to clean up and cluster such regions for further analysis.

    8.  Understand representations of shapes of regions in image using various  shape and texture measures which could then be used to either classify or recognize an object.

    9. Demonstrate the use of region classification techniques using various shape descriptions.

    10. Identify and apply these techniques to solve real-world real-world image processing problems.

    University Graduate Attributes

    This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:

    University Graduate Attribute Course Learning Outcome(s)
    Knowledge and understanding of the content and techniques of a chosen discipline at advanced levels that are internationally recognised. 1-10
    An ability to apply effective, creative and innovative solutions, both independently and cooperatively, to current and future problems. 2-10
    A proficiency in the appropriate use of contemporary technologies. 1,7,10
  • Learning Resources
    Required Resources
    All essential materials such as lecture notes and slides provided by the course presenter.
    Recommended Resources
    Textbook:

    • R.C. Gonzales & R.E. Woods “Digital Image Processing” (2nd or 3rd edition), Prentice Hall, ISBN 0-201-18075-8

    Supporting Texts:

    • K.R. Castleman “Digital Image Processing”, Prentice Hall.
    • J.C. Russ “The Image Processing Handbook”, IEEE Press.
    Online Learning
    Extensive use will be made of the MyUni web site for this course, https://myuni.adelaide.edu.au/webapps/login Course.

    Notes, tutorial and assignment problems and solutions, laboratory exercises and practice problems will all be available for downloading from the web site. Where the lecture theatre facilities permit, audio or video recordings of lectures will also be available for downloading.
  • Learning & Teaching Activities
    Learning & Teaching Modes
    This course relies on lectures as the primary delivery mechanism for the material. Tutorials supplement the lectures by providing exercises and example problems to enhance the understanding obtained through lectures. Practicals and assignments are used to provide hands-on experience for
    students to reinforce the concepts encountered in lectures. Continuous assessment activities via programming assignments provide the formative assessment opportunities for students to gauge their progress and understanding.
    Workload

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    Actvity Contact Hours Workload Hours
    Lecture 24 lectures 36 48
    Tutorials 12 tutorials 12 12
    Assignments 4 (coding+written) 60
    TOTALS 60 140
    Learning Activities Summary
    LecturesPart A – Processing (Weeks 1-6)
    • Sensors, image representation & storage
    • Basic image processing (contrast enhancement, simple noise reduction, color balancing)
    • Spatial transformations and image registration (affine, projective, re-sampling methods, optical flow)
    • Image Filtering in the spatial and frequency domains (FIR filter, Fourier transforms, high-pass/low-pass, Wiener filters etc)
    • Transform representations (DCT, Wavelets) and Image compression.

    Part B – Analysis (Weeks 7-11)
    • Thresholding and segmentation
    • Binary image filtering – Morphological Filters (opening, closing, watershed)
    • Feature Extraction – Edges, lines and corners
    • Feature Extraction – Texture and shape measures
    • Template matching and video tracking techniques (cross correlation, MACH filters generalized Hough transforms etc)
    • Feature based object classification and recognition (feature selection, KNN, NPDA, GMM)

    Part C – Advanced Topics (week 12, time permitting)
    • Super-resolution
    • Scene Analysis

    Assignments (times, topics are only approximate)
    1. Basic image processing (week 3)
    2. Spatial transforms and/or registration (week 6)
    3. Edge detection and line finding (week 8)
    4. Segmentation and Object Classification (week 10)

    Other
    • Informal Quiz (week 8)
    • Revision (week 12)
    • Consulting (times to be advised)
    Specific Course Requirements
    Students are required to have access to Matlab software. This is available at various facilities such as the CATS suite or the undergraduate computer labs of the School of Electrical & Electronic Engineering. It is the individual student’s responsibility to ensure his or her access to these facilities at appropriate times is available.
  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary

    Activity Type Weighting Due Date Learning Outcomes Assessed
    Assignment Summative 50% Weeks 5, 8, 10, 12 approx All
    Exam Summative 50% End of Semester All
    Assessment Related Requirements
    The examination and assignments are prescribed summative assessment exercises in which students must obtain at least a total of 50% in both the assignment and exam. Failure to achieve at least 50% in either the exam or the practical work will mean that the student will obtain a final total mark of no more than 49%.
    Assessment Detail
    Details of individual assessment tasks will be provided during the semester.
    Submission
    All assignment submissions to formative assessment activities are to be submitted electronically via the links provided in the assignments Folder of this course on MyUni.Any late submissions will receive
    penalties. All formative assessments will have a 2-3 week turn-around time for provision of feedback to students.
    Course Grading

    Grades for your performance in this course will be awarded in accordance with the following scheme:

    M10 (Coursework Mark Scheme)
    Grade Mark Description
    FNS   Fail No Submission
    F 1-49 Fail
    P 50-64 Pass
    C 65-74 Credit
    D 75-84 Distinction
    HD 85-100 High Distinction
    CN   Continuing
    NFE   No Formal Examination
    RP   Result Pending

    Further details of the grades/results can be obtained from Examinations.

    Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.

    Final results for this course will be made available through Access Adelaide.

  • Student Feedback

    The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.

    SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.

  • Student Support
  • Policies & Guidelines
  • Fraud Awareness

    Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student’s disciplinary procedures.

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