ENGEE532-22B (HAM)

Image Processing and Machine Vision

15 Points

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Division of Health Engineering Computing & Science
School of Engineering

Staff

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Convenor(s)

Lee Streeter

Lee Streeter
4106
CD.1.03
See Moodle
lee.streeter@waikato.ac.nz

Lecturer(s)

Michael Cree

Michael Cree
4301
DE.2.02
See Moodle
michael.cree@waikato.ac.nz

Administrator(s)

: mary.dalbeth@waikato.ac.nz
: natalie.shaw@waikato.ac.nz

Placement/WIL Coordinator(s)

Tutor(s)

Student Representative(s)

Lab Technician(s)

Librarian(s)

: cheryl.ward@waikato.ac.nz

You can contact staff by:

  • Calling +64 7 838 4466 select option 1, then enter the extension.
  • Extensions starting with 4, 5, 9 or 3 can also be direct dialled:
    • For extensions starting with 4: dial +64 7 838 extension.
    • For extensions starting with 5: dial +64 7 858 extension.
    • For extensions starting with 9: dial +64 7 837 extension.
    • For extensions starting with 3: dial +64 7 2620 + the last 3 digits of the extension e.g. 3123 = +64 7 262 0123.
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Paper Description

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This course introduces the basics of image processing and machine vision. The ultimate goal is to train students to be able to look at a problem requiring sensing (e.g. robotic control or measurement) and be able to determine if machine vision is appropriate, and be able to create a solution.

In the first half, students will learn the fundamental of how images are formed, "image domain" processing and analysis techniques, and deep learning for image classification.

In the second half, processing in the Fourier domain, optimisation, range imaging (measuring distance with cameras), and motion analysis in video data.

The learning outcomes for this paper are linked to Washington Accord graduate attributes WA1-WA11. Explanation of the graduate attributes can be found at: https://www.ieagreements.org/

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Paper Structure

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This course will be taught through lectures and laboratory sessions. Students will have hands on experience acquiring data with cameras and processing image data in the computer.
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Learning Outcomes

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Students who successfully complete the paper should be able to:

  • Describe image formation and the limitations of imaging systems (exam, assignments, and labs). (WK 1-4, WA 1)
    Linked to the following assessments:
  • Apply image processing algorithms (exam, assignments, and labs). (WK 1-4, WA 1,5)
    Linked to the following assessments:
  • Design and implement image processing solutions to specific problems (labs and lab reports). (WK 1-4, WA 1,5)
    Linked to the following assessments:
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Assessment

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This paper will be internally assessed.

The internal assessment will comprise two written assignments, four laboratory reports, and two tests. The students must write a short report for each project including algorithm design, methods and annotated code, explaining how they achieved the set tasks. IEEE conference format will be used in reporting. The two written assignments will be worth 5% of the final grade each. The four reports will be worth 10% of the final grade each. The tests will each contribute 25% of the final grade.

Samples of your work may be required as part of the Engineering New Zealand accreditation process for BE(Hons) degrees. Any samples taken will have the student name and ID redacted. If you do not want samples of your work collected then please email the engineering administrator, Natalie Shaw (natalie.shaw@waikato.ac.nz), to opt out.

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Assessment Components

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The internal assessment/exam ratio (as stated in the University Calendar) is 100:0. There is no final exam. The final exam makes up 0% of the overall mark.

The internal assessment/exam ratio (as stated in the University Calendar) is 100:0 or 0:0, whichever is more favourable for the student. The final exam makes up either 0% or 0% of the overall mark.

Component DescriptionDue Date TimePercentage of overall markSubmission MethodCompulsory
1. Written Assignments 1
5
  • Online: Submit through Moodle
2. Written Assignment 2
5
  • Online: Submit through Moodle
3. Report 1
10
  • Online: Submit through Moodle
4. Report 2
10
  • Online: Submit through Moodle
5. Report 3
10
  • Online: Submit through Moodle
6. Report 4
10
  • Online: Submit through Moodle
7. Test 1
25
8. Test 2
25
Assessment Total:     100    
Failing to complete a compulsory assessment component of a paper will result in an IC grade
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Required and Recommended Readings

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Recommended Readings

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Gonzales and Woods, "Digital Image Processing." Prentice Hall.
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Online Support

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Moodle will be used for distribution of notes and activities.

Students are encouraged to use the online forum available in Moodle for questions and discussion with the teaching staff and with each other.

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Workload

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There will be two lectures per week. We expect the students to self direct about one extra hour per lecture of study.

The workload is heavily weighted towards projects, as reflected in the internal assessment. We anticipate the laboratory work will require about 6 hours per week, only three of which will be contact hours.

Including the examination and filing time, this course will require about 140 to 150 hours over the semester.

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Linkages to Other Papers

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Prerequisite(s)

Prerequisites: COMPX203 and (ENGEN301 or MATHS304)

Corequisite(s)

Equivalent(s)

Restriction(s)

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