COMP H4602 - Computer Vision

Module Title:Computer Vision
Language of Instruction:English
Credits: 5
NFQ Level:8
Module Delivered In 1 programme(s)
Teaching & Learning Strategies: This module will be delivered through a mix of lectures, laboratory assignments, and projects including a professional write-up. It will employ a mixture of active/task-based learning, reflective learning, and problem-based learning.
Module Aim: Computer vision has become commonplace in applications ranging from search to medical application and self-driving cars. This module shall investigate how images are acquired and information extracted by the computer using classical algorithms. The module shall cover how computers represent objects and their alignment and allow students to locate and track feature movement between images.
Learning Outcomes
On successful completion of this module the learner should be able to:
LO1 Assemble an image acquisition system, demonstrating an understanding of its constituent components.
LO2 Design an image acquisition system to demonstrate an understanding of enhancement and pattern matching within images.
LO3 Demonstrate the use of algorithms to track feature movement and displacement between frames of images.
LO4 Collect depth information from multiple (stereo) images and track the location of the feature in the z-plane.
LO5 Complete a project as an individual or in a small group to design and implement a solution for a real world problem.
Pre-requisite learning
Module Recommendations

This is prior learning (or a practical skill) that is recommended before enrolment in this module.

No recommendations listed
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements
This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed.
A high-level language, statistics, linear algebra.
 

Module Content & Assessment

Indicative Content
Acquisition system
Image acquisition system using COTS components. Camera, lenses and lens distortion, focal length, aperture, depth of field, exposure, shutter speed, frame rate affect on quality of the image acquisition. Improvements to image acquisition using passive and active lighting, flashes, radiometry.
Image Enhancement
Introduction to image enhancement in both the spatial and frequency domains. Contrast enhancement and transformations. Histogram processing. Filtering.
Pattern matching
Image convolution and feature detection, e.g. detection of edges and identifying features. Application of feature detectors and descriptors such as MOG, HOG, SIFT, SURF etc.
Feature movement
Track the direction of feature movement using motion estimation, alignment, parametric and layered motion, etc.
Depth interpolation
Extract depth information using, e.g. epipolar geometry techniques and show different styles of correspondence (dense, sparse) to interpret the depth of a set of images.
Ethics and Safety
Ethical use and bias in captured data, reliable use of computer vision in safety systems
Assessment Breakdown%
Continuous Assessment20.00%
Project40.00%
Practical40.00%
Continuous Assessment
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Short Answer Questions n/a 1,2 20.00 Week 4
Project
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Project n/a 1,2,3,4,5 40.00 Sem 2 End
Practical
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Practical/Skills Evaluation n/a 1,2,3,4 40.00 Every Week
No End of Module Formal Examination

ITCarlow reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Frequency Average Weekly Learner Workload
Lecture Every Week 2.00
Laboratory Every Week 3.00
Independent Learning Every Week 5.00
Total Hours 10.00
 

Module Delivered In

Programme Code Programme Semester Delivery
CW_EERAS_B Bachelor of Engineering (Honours) in Robotics and Automated Systems 7 Mandatory