ELEC ENG 7060 - Image Sensors & Processing
North Terrace Campus - Semester 2 - 2022
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 Incompatible ELEC ENG 4061 Assumed Knowledge Familiarity with computer programming (including MATLAB), linear systems & 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 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 hit-or-miss 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 OutcomesOn 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 Stage 1 Competency Standard for the Professional Engineer.
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)
Attribute 1: Deep discipline knowledge and intellectual breadth
Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.
Attribute 2: Creative and critical thinking, and problem solving
Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.
Attribute 3: Teamwork and communication skills
Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.
Attribute 4: Professionalism and leadership readiness
Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.
Attribute 8: Self-awareness and emotional intelligence
Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.
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 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)
• 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.
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
This assessment breakdown is registered as an exemption to the University's Assessment for Coursework Programs Policy. 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 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.
Final results for this course will be made available through Access Adelaide.
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.
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