ELEC ENG 7060 - Image Sensors & Processing
North Terrace Campus - Semester 2 - 2016
General Course Information
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 their principles; Image representation and storage, coding and compression techniques, lossy versus lossless; Techniques for noise reduction 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; Spatial transformations and image registration. Segmentation and thresholding techniques; Applications of morphology to image processing including erosion, dilation and hitormiss 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 Coordinator: Dr Danny GibbinsCourse Co-ordinator & lecturer: Dr. Danny Gibbins
Office: Ingkarni Wardli 2.24
Phone: 8313 3162
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesAfter 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.
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.
11. Propose and ealuate solutions to a real-world image processing or analysis problem.
12. Further develop their knowledge and understanding of image processing based on the ideas and concepts presented in the course.
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) Deep discipline knowledge
- informed and infused by cutting edge research, scaffolded throughout their program of studies
- acquired from personal interaction with research active educators, from year 1
- accredited or validated against national or international standards (for relevant programs)
1-11 Critical thinking and problem solving
- steeped in research methods and rigor
- based on empirical evidence and the scientific approach to knowledge development
- demonstrated through appropriate and relevant assessment
1-11 Teamwork and communication skills
- developed from, with, and via the SGDE
- honed through assessment and practice throughout the program of studies
- encouraged and valued in all aspects of learning
1,4,8,11 Career and leadership readiness
- technology savvy
- professional and, where relevant, fully accredited
- forward thinking and well informed
- tested and validated by work based experiences
1-11 Self-awareness and emotional intelligence
- a capacity for self-reflection and a willingness to engage in self-appraisal
- open to objective and constructive feedback from supervisors and peers
- able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
Required ResourcesAll essential materials such as lecture notes and slides provided by the course presenter.
• R.C. Gonzales & R.E. Woods “Digital Image Processing” (2nd or 3rd edition), Prentice Hall, ISBN 0-201-18075-8
• K.R. Castleman “Digital Image Processing”, Prentice Hall.
• J.C. Russ “The Image Processing Handbook”, IEEE Press.
Online LearningExtensive 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 ModesThis 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.
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 SummaryLecturesPart 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 7-12)
• 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)
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)
• Informal Quiz (week 8)
• Revision (week 12)
• Consulting (times to be advised)
Specific Course RequirementsStudents 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.
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
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 RequirementsThe 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 DetailDetails of individual assessment tasks will be provided during the semester.
SubmissionAll 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.
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.
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