ELEC ENG 7060 - Image Sensors & Processing

North Terrace Campus - Semester 2 - 2017

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 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

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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	Familiarity with computer programming (including MATLAB), linear systems & signal processing
Assessment	Examination (50%) and assignments (50%)

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 Learning Outcomes

On successful completion of this course students will be able to:

1	Demonstrate a knowledge of a broad range of fundamental image processing and image analysis techniques and concepts (linear and non-linear filtering, denoising, deblurring, edge detection, line finding, detection, morphological operators, compression, shape metrics and feature based recogniton)
2	Identify, Demonstrate and apply their knowledge by analysing image processing problems and recognising and employing (or proposing) effective solutions
3	Design and create practical solutions to a range of common image processing problems and to critically assess the results of their solutions, including shortcomings

The above course learning outcomes are aligned with the Engineers Australia .
The course is designed to develop the following Elements of Competency: 1.1 1.2 1.3 1.5 2.1 2.2 3.2 3.4 3.5

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)	1-3
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	2, 3
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	2
Career and leadership readiness technology savvy professional and, where relevant, fully accredited forward thinking and well informed tested and validated by work based experiences	3
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	2, 3

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, 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 7-12)
• 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 and basic video tracking

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.

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.

Assessment Summary

Assessment Task	Weighting (%)	Individual/ Group	Formative/ Summative	Due (week)*	Hurdle criteria	Learning outcomes
Assignments (4, combination of code and written questions, report format)	50	Individual	Summative	Weeks 5-12	Min 50%	2. 3.
Exam	50	Individual	Summative	Exam Week	Min 50%	1. 2.
Total	100

* The specific due date for each assessment task will be available on MyUni.

This assessment breakdown is registered as an exemption to the University's . The exemption is related to the Procedures clause(s): 1. b. 3.

This course has a hurdle requirement. Meeting the specified hurdle criteria is a requirement for passing the course.

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 .

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
This section contains links to relevant assessment-related policies and guidelines - all university policies.
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鈥檚 disciplinary procedures.

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Authorised by:	Deputy Vice-Chancellor and Vice-President (Academic)
Maintained by:	Course Outlines Administrator

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