IMAGE PROCESSING AND DEEP LEARNING - 2021/2
Module code: EEEM063
In light of the Covid-19 pandemic, and in a departure from previous academic years and previously published information, the University has had to change the delivery (and in some cases the content) of its programmes, together with certain University services and facilities for the academic year 2020/21.
These changes include the implementation of a hybrid teaching approach during 2020/21. Detailed information on all changes is available at: https://www.surrey.ac.uk/coronavirus/course-changes. This webpage sets out information relating to general University changes, and will also direct you to consider additional specific information relating to your chosen programme.
Prior to registering online, you must read this general information and all relevant additional programme specific information. By completing online registration, you acknowledge that you have read such content, and accept all such changes.
Module purpose: This course offers an introduction to image processing and computer vision for those interested in the science and technology of machine vision. It provides background and the theory for building artificial systems that manipulate videos and images and alter or analyse their information content. This is done by various computer algorithms that are discussed, implemented and demonstrated.
Electrical and Electronic Engineering
COLLOMOSSE John (Elec Elec En)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 7
JACs code: I113
Module cap (Maximum number of students): N/A
Prerequisites / Co-requisites
Indicative content includes the following.
[1-2] Introduction. Image Representation and Colour. Geometric Image Transformations. Homogeneous Coordinates.
[3-4] Image Warping. Interpolation. Image Quality metrics.
[5-7] Image Filtering. Aliasing. Blurring, Sharping and Edge Detection. Gaussian kernel and its derivatives. Scale-space pyramids. Thresholding.
[8-10] Image Completion and Enhancement. Brightness and Contrast. Poisson Image Editing. Patch based in-painting. Super-resolution.
[11-12] Classical object recognition. Interest points, Gradient domain features and Bag of Words.
[13-15] Convolutional Neural Networks (CNNs). Siamese Networks. CNN Interpretatability.
[16-18] Fully convolutional networks. Up-convolution. Optical flow. FCNs for Stylization. Segmentation.
[19-20] Genereative Adversarial Networks (GANs). Image transformation networks. Super-resolution.
[21-22] Revision lectures
|Assessment type||Unit of assessment||Weighting|
|Coursework||MATLAB-BASED EXERCISE AND REPORT||25|
|Examination||2 HOUR CLOSED-BOOK WRITTEN EXAMINATION||75|
Not applicable: students failing a unit of assessment resit the assessment in its original format.
The assessment strategy for this module is designed to provide students with the opportunity to demonstrate the following.
Problem Classes, assignments, examination:
Knowledge of image processing and computer vision, understanding of the engineering methodologies for design and implementation of software algorithms.
Knowledge of state-of-the art solutions to image processing and computer vision problems, their limitations and requirements for better solutions.
Skills of identifying, classifying and describing the performance of software systems and components through the use of analytical methods and modelling techniques.
Laboratory based assessment: Practical knowledge of designing and assessment of the performance of image processing and computer vision algorithms, such as filtering, clustering, motion estimation, matching, recognition, image enhancement. Assessed by laboratory exercises.
Knowledge of engineering principles for software design in signal processing. This involves integration of software components related to verification and validation.
Skills of applying quantitative methods, mathematical and computer-based models, and use computer software (Matlab) to solve image-processing problems.
Skills of identifying and analyse problems in images, and develop software solutions to remove problems, for example noise. Assessed by laboratory exercises.
Programming skills and understanding of software tools, development environments, libraries and reusable components such as matlab or open-CV libraries. They use these to perform filtering, image geometry, and other computer vision tasks such as matching and clustering.
Thus, the summative assessment for this module consists of the following.
· Examination 75%
· Matlab exercise and report 25% (weeks 5-10)
The examination consists of 2h closed-book written examination. There are 4 questions each from different area of the course. Each question consists of several subquestions testing knowledge, analytical, and design skills.
The coursework assessments consists of using the Caffe framework (e.g. via DIGITS) to perform an experiment e.g. image recognition, with a report summarizing theoutcomes. The purpose of the coursework is for students to practically apply the theoretical knowledge gained from the lectures on a topic from each of the core themes of this module: image processing, and computer vision.
Formative assessment and feedback
For the module, students will receive formative assessment/feedback in the following ways.
· During lectures, by question and answer sessions
· During tutorials/tutorial classes
· During supervised computer laboratory sessions
- The aim of this module is to offer an in depth course on the principles of Image Processing and Computer Vision which form the foundation for a variety of disciplines like Robotics, Machine Vision, Remote Sensing, Surveillance, Medical Imaging, Multimedia Technologies etc. In particular the course focuses upon Deep Learning - a form of AI/Machine Learning that has emerged in recent years with broad applicability across these domains.
|1||Be able to demonstrate an understanding of a number of principles in image processing and computer vision and the theory behind those||K|
|2||Be able to choose an appropriate method to an image processing or computer vision problem at hand and predict the outcome of the applied processing||KCP|
|3||Be able to formulate problems in Image Processing and Computer Vision in a Mathematical way and solve them to achieve optimality in performance||KCT|
|4||Be able to analyse some non-trivial problems in image processing and computer vision, understand the concepts behind them and come up with possible algorithmic solutions.||CP|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Overall student workload
Independent Study Hours: 117
Lecture Hours: 22
Laboratory Hours: 11
Methods of Teaching / Learning
The learning and teaching strategy is designed to achieve the following aims.
- To introduce image processing and computer vision to promote deep understanding of the engineering methodologies for design and implementation of software algorithms. The topics include: algebraic manipulation; probability & statistical analysis; discrete probability; Fourier analysis; vector algebra; differential & integral calculus.
- To introduces engineering principles for software design in signal processing, which involves integration of software components related to verification and validation.
- Students learn to identify, classify and describe the performance of software systems and components through the use of analytical methods and modelling techniques. These allow them to employ performance trade-offs and the use of appropriate metrics, to predict and evaluate performance of computer vision algorithms.
- Students learn skills of applying quantitative methods, mathematical and computer-based models, and use computer software (Matlab) to solve image-processing problems. Skills involve identifying and analyse problems in images, and develop software solutions to remove problems, for example noise.
- Students learn basic programming skills and understanding of software tools, development environments, libraries and reusable components such as matlab or open-CV libraries. They use these to perform filtering, image geometry, and other computer vision tasks such as matching and clustering.
Learning and teaching methods include the following.
- Lectures: 11 weeks, 11x 2h
- Labs: Matlab exercises to consolidate the lecture material, 11 x 1h
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Reading list for IMAGE PROCESSING AND DEEP LEARNING : http://aspire.surrey.ac.uk/modules/eeem063
Programmes this module appears in
|Automotive Engineering MEng||2||Optional||A weighted aggregate of 40% overall and a pass on the pass/fail unit of assessment is required to pass the module|
|Electronic Engineering with Computer Systems MEng||2||Compulsory||A weighted aggregate mark of 50% is required to pass the module|
|Electronic Engineering MEng||2||Optional||A weighted aggregate mark of 50% is required to pass the module|
|Computer and Internet Engineering MEng||2||Optional||A weighted aggregate mark of 50% is required to pass the module|
|Electronic Engineering with Professional Postgraduate Year MSc||2||Optional||A weighted aggregate mark of 50% is required to pass the module|
|Medical Imaging MSc||2||Compulsory||A weighted aggregate mark of 50% is required to pass the module|
|Computer Vision, Robotics and Machine Learning MSc||2||Compulsory||A weighted aggregate mark of 50% is required to pass the module|
|Electronic Engineering MSc||2||Optional||A weighted aggregate mark of 50% is required to pass the module|
|Entrepreneurship & Innovation Management MSc||2||Optional||A weighted aggregate mark of 50% is required to pass the module|
|Artificial Intelligence MSc||2||Compulsory||A weighted aggregate mark of 50% is required to pass the module|
|Data Science MSc||2||Optional||A weighted aggregate mark of 50% is required to pass the module|
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2021/2 academic year.