CS933-15 Image and Video Analysis

Academic year
21/22
Department
Computer Science
Level
Taught Postgraduate Level
Module leader
Abhir Bhalerao
Credit value
15
Module duration
10 weeks
Assessment
Multiple
Study location
University of Warwick main campus, Coventry
Introductory description

The course will enable computer science undergraduates to apply their mathematical knowledge and understanding of algorithms to problems in image and video processing: from preprocessing, to quantitation, video compression and video interpretation. The methods have numerous applications e.g. in medicine, biology, robotics (computer vision), surveillance, security, biometrics, database searching, TV and entertainment.

Module aims

The module aims to teach the fundamentals of digital image processing, image and video analysis. In particular, it will present the mathematics and algorithms that underlie image analysis techniques such as filtering, denoising, edge detection, feature detection, tracking and 3D reconstruction. It will also present how these tools are used in algorithms image and video segmentation, motion estimation, stereo reconstruction, video denoising and
video analysis, object detection and recognition, and standards for video compression and communication.

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

Learning outcomes

By the end of the module, students should be able to:

Indicative reading list

Please see Talis Aspire link for most up to date list.

View reading list on Talis Aspire

Subject specific skills
Transferable skills

Study time

Type Required
Lectures 30 sessions of 1 hour (20%)
Supervised practical classes 9 sessions of 2 hours (12%)
Private study 102 hours (68%)
Total 150 hours
Private study description

30 hours lectures
9 x 2hr labs = 18 hours
35 hours coursework
67 hours private study

Costs

No further costs have been identified for this module.

You do not need to pass all assessment components to pass the module.

Students can register for this module without taking any assessment.

Assessment group D1
Weighting Study time
Unsupervised practical assignments 30%

Coursework assignment. This assignment is worth more than 3 CATS and is not, therefore, eligible for self-certification.

In-person Examination 70%

CS933 examination


  • Answerbook Pink (12 page)
  • Students may use a calculator
Assessment group R1
Weighting Study time
In-person Examination - Resit 100%

CS933 resit examination


  • Answerbook Pink (12 page)
  • Students may use a calculator
Feedback on assessment

Individual written feedback on Assessed Coursework.
1-to-1 feedback on Assessed Coursework.

Past exam papers for CS933

Pre-requisites

Students must have studied the material in CS118, CS131 or have equivalent knowledge and experience.

Courses

This module is Optional for:

  • Year 1 of TCSA-G5PD Postgraduate Taught Computer Science
  • Year 1 of TCSA-G5PA Postgraduate Taught Data Analytics