Course Details

Course Code(s):
Course Start Date:
Autumn 2024
6 Weeks
University Certificate of Study
Faculty: Science and Engineering
Course Type: Professional/Flexible, Online
Fees: For Information on Fees, see section below.


Name: Dr Tony Scanlan

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Brief Description

This course qualifies for 80% funding under the HCI Micro-Credential Course Learner Subsidy. Check fees section for details and eligibility.   Please Note:   Applicants may only apply for and receive, one subsidised course per semester. 

This MicroCred will introduce you to machine vision and image processing principles. Key topics such as linear image processing, feature detection and essential object detection are introduced.

Practical examples of these techniques are included in the laboratories for this module to increase student engagement with this material. This short course is a precursor to advanced vision modules.

Please ensure you enter the Module Code below when applying for this module. Applications without this cannot be processed.

Module Description Module Code NFQ Level ECTS Credits Start Date Fees
Machine Vision and Image Processing CE5011 9 6 Autumn 2024 €750

This micro-credential represents a single module within a larger further award (eg. Certificate, Diploma, Masters). By taking this micro-credential you may be eligible to apply for a credit exemption should you progress to study for a further award. The programmes associated with this MicroCred are: 


1. Introduction to Machine and Computer Vision.

  • Image formation, camera basics, computer representation of images.
  • Linear image processing, morphology operations and basic edge detection.
  • Canny Edge Detection and Hough Transform.
  • Clustering and Image Segmentation (K-means, Watershed & Mean shift Algorithms).
  • Case studies of Automated Inspection with Machine Vision.

2. Feature Detection, Descriptors and Applications.

  • Corner Detection (Harris Algorithm)
  • Laplacian of Gaussian and blob detectors.
  • Feature Descriptors (SIFT & binary descriptors)
  • Feature Matching with Descriptors.

3. Basics of Machine Learning for Vision.

  • Machine Learning Introduction (Types of Classifiers SVM, CNN)
  • Principle Component Analysis and Eigenfaces & Fisher Faces.
  • Classical Methods of Object Detection.
  • Sliding window-based Viola Jones & Histogram of Orientated Gradients algorithms.
  • Bag of Features for image classification and retrieval.

4. Introduction to the use of Deep Learning in Machine & Computer Vision

Learning Outcomes

On successful completion of this module, you will be able to:

  • Demonstrate an understanding of basic image processing, morphology operations and edge detection algorithms.
  • Understand reasons for feature detection, basic detector algorithms and the application of these detectors.
  • Understand the basic principles of machine learning and its application to machine vision.
  • Apply sliding window-based object detection algorithms to different tasks.
  • Be aware of the application of Deep Learning to key problems in machine vision.


There is no terminal exam. You will be assessed through continuous skill-based assignments.

Applicants are normally expected to hold a primary honours degree in a cognate (related) discipline, (minimum H2.2), or equivalent and have at least 5 years of relevant industrial experience.

Entry requirements are established to ensure the learner can engage with the course material and assessments, at a level suitable to their needs, and the academic requirements of the module. By applying to this micro-credential, you are confirming that you have reviewed and understand any such requirements, and that you meet the eligibility criteria for admission.

Successful completion of this module does not automatically qualify you for entry into a further award. All programme applicants must meet the entry requirements listed if applying for a further award.


HCI Micro-Credential Course Learner Subsidy - Candidates who satisfy the eligibility criteria can qualify for 80% funding subject to the availability of places. To clarify eligibility please go to Eligibility Criteria 

Please click here for information on funding and scholarships.