Module Database Search



MODULE DESCRIPTOR
Module Title
Computer Vision
Reference CM4126 Version 1
Created February 2021 SCQF Level SCQF 10
Approved June 2021 SCQF Points 15
Amended ECTS Points 7.5

Aims of Module
Computer vision aims at developing computational solutions to understand the content of images and video in a way similar to human perception.

Learning Outcomes for Module
On completion of this module, students are expected to be able to:
1 Describe a range of image processing and image manipulation techniques.
2 Evaluate a range of image features extraction and features representation methods.
3 Analyse different machine learning and deep learning methods for image classification and object detection and recognition tasks.
4 Create an end-to-end intelligent computer vision solution by applying underlying concepts and theories of modern computer vision.

Indicative Module Content
This module will cover image and video analysis, including image processing methods, classification, object recognition and detection, and object tracking. Core image processing tasks such as image enhancement, sampling, noise removal, filtering and morphological operations. Modern computer vision methods such as Convolutional Neural Networks, deep learning methods for handling images and videos. Object Detection, Localisation and Recognition. Object tracking and motion estimation. Deploying computer-vision solutions for real world problems. Working with relevant tools and technologies such as Python, OpenCV, and Tensorflow.

Module Delivery
Key concepts will be introduced and illustrated through the medium of lectures and lab sessions. Lab sessions will include introductory tutorials to help students in applying advanced computer vision methods and lab tasks will be given to help students reinforce their understanding and technical knowledge of the core subject and develop proficiency in applying it to various practical computer vision scenarios.

Indicative Student Workload Full Time Part Time
Contact Hours 30 N/A
Non-Contact Hours 120 N/A
Placement/Work-Based Learning Experience [Notional] Hours N/A N/A
TOTAL 150 N/A
Actual Placement hours for professional, statutory or regulatory body    

ASSESSMENT PLAN
If a major/minor model is used and box is ticked, % weightings below are indicative only.
Component 1
Type: Practical Exam Weighting: 100% Outcomes Assessed: 1, 2, 3, 4
Description: The practical assessment will be based on applying computer vision techniques to a case study from the public domain.

MODULE PERFORMANCE DESCRIPTOR
Explanatory Text
The calculation of the overall grade for this module is based on 100% weighing of C1. An overall minimum grade D is required to pass the module.
Module Grade Minimum Requirements to achieve Module Grade:
A The student needs to achieve an A in C1
B The student needs to achieve a B in C1
C The student needs to achieve a C in C1
D The student needs to achieve a D in C1
E The student needs to achieve an E in C1
F The student needs to achieve an F in C1
NS Non-submission of work by published deadline or non-attendance for examination

Module Requirements
Prerequisites for Module CM3111 or equivalent
Corequisites for module None.
Precluded Modules None.

INDICATIVE BIBLIOGRAPHY
1 J Howse and J Minichino, 2020, “Learning OpenCV 4 Computer Vision with Python 3: Get to grips with tools, techniques, and algorithms for computer vision and machine learning”, 3rd Edition.
2 Krishnendu Kar, 2020, “Mastering Computer Vision with TensorFlow 2.x: Build Advanced Computer Vision Applications Using Machine Learning and Deep Learning Techniques.
3 Aurelien Geron 2019, Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems.

Robert Gordon University, Garthdee House, Aberdeen, AB10 7QB, Scotland, UK: a Scottish charity, registration No. SC013781