IMAGE PROCESSING AND DEEP LEARNING - 2021/2

Module Overview

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.

Module provider

Computer Science and Electronic Eng

Module Leader

SONG Yi-Zhe (Elec Elec En)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): 100

Module Availability

Semester 2

Prerequisites / Co-requisites

None.

Module content

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 pattern

Alternative Assessment

Not applicable: students failing a unit of assessment resit the assessment in its original format.

Assessment Strategy

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

 

 

 

 

Module aims

• 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.

Learning outcomes

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.

• 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

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: EEEM063

Other information

This module has a capped number and may not be available to ERASMUS and other international exchange students. Please check with the International Engagement Office email: ieo.incoming@surrey.ac.uk

Programmes this module appears in

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.