ENGEE532-20B (HAM)

Image Processing and Machine Vision

15 Points

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Division of Health Engineering Computing & Science
School of Engineering


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

Placement/WIL Coordinator(s)


Student Representative(s)

Lab Technician(s)


: cheryl.ward@waikato.ac.nz
: debby.dada@waikato.ac.nz

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  • Calling +64 7 838 4466 select option 1, then enter the extension.
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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 Fourier analysis of images and processing in the Fourier domain.

In the second half, modern computer vision will be introduced, including motion analysis in video data, range imaging (measuring distance with cameras), and deep learning for image classification.

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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 course should be able to:

  • Describe image formation and the limitations of imaging systems (exam and projects).
    Linked to the following assessments:
  • Describe and use image processing algorithms (exam and projects).
    Linked to the following assessments:
  • Design image processing solutions to specific problems (projects).
    Linked to the following assessments:
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The internal:external weighting is 50:50.

The external assessment will be a 12 hour take home open book computer assignment and written problems.

The internal assessment will comprise two written assignments and four small laboratory reports. 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.

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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 (there will be two)
  • Online: Submit through Moodle
2. Reports (there will be four).
3. Exam
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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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 project work will require about 6 hours per week.

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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Prerequisites: COMPX203 and (ENGEN301 or MATHS304) and (ENGEN201 or MATHS203)




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