# COMP37212 Computer Vision syllabus 2019-2020

COMP37212 Computer Vision

Level 3
Credits: 10
Enrolled students: 79

Requisites

• Pre-Requisite (Compulsory): COMP27112
• Pre-Requisite (Compulsory): COMP11120

• Students who are not from the School of Computer Science must have permission from both Computer Science and their home School to enrol.

Pre-requisites

To enrol students are required to have taken COMP11120 (waived for CM students) plus COMP27112.

Assessment methods

• 70% Written exam
• 30% Coursework
Timetable
SemesterEventLocationDayTimeGroup
Sem 2 Lecture 1.5 Mon 10:00 - 11:00 -
Sem 2 Lecture 1.4 Fri 12:00 - 13:00 -
Sem 2 Lab 1.8 Thu 12:00 - 13:00 -
Themes to which this unit belongs
• Visual Computing

## Aims

To provide a broad introduction to Computer Vision and Image Interpretation. To introduce the basic concepts and algorithmic tools of Computer Vision To explore the importance of modelling and representation in interpretation of images. To provide an understanding of the range of processing components involved in image interpretation systems.

## Syllabus

The tools and algorithms of computer vision are introduced in the context of two major capabilities required of visual systems: recognition - finding and identifying expected things in images and 3D interpretation - understanding a dynamic 3D scene from 2D images or sequences of images. These capabilities are explored using applications of varying levels of complexity: recognising man-made objects, interpreting medical images, face recognition, robotics, scene reconstruction and surveillance.

Students taking the module will require some basic familiarity with matrix and vector algebra, such as that covered in MT1662 or MT1672. Tutorial material will be provided where the mathematics goes beyond the scope of those modules. A general familiarity with basic concepts of calculus (integration, partial differentiation) will also be useful.

Introduction:

The role of Computer Vision, applications, successes, research issues; its relationship to natural vision, basic image properties.

Image Interpretation: Finding Things in Images

Exemplars

• Face Recognition, Medical Image Analysis, Recognising man-made objects.
• Representing knowledge of expected image contents.
• Matching models to image data.
• Simple implicit models: Thresholding, Edge finding, Organising mechanisms. Mathematical Morphology.
• Hough Transform, Template Matching (correlation), Active Contour Models (Snakes).
• Multi-resolution approaches, Feature-based models, Edges, Corners, Object Recognition.
• Flexible Templates, Search, Constraining Model Fit, Statistical Models, Describing natural variability, Active Shape Models, Recognition, Classification.

Beyond 2D: 3D and Motion

Exemplars

• Robot Vision, Scene Reconstruction, Surveillance.
• Stereopsis: Recovering depth, The correspondence problem, Stereo constraints.
• Motion: Tracking, Image differencing, Feature matching Models, Optic flow.

In addition to the material in lecture notes and textbooks, Self-test questions and solutions will be provided.  For some topics, practical exercises, with associated MATALB scripts and images will be available for use by students unsupervised.  These additional materials may be downloaded from the course web site.

Lectures

22

## Feedback methods

Written feedback is provided on 6 pieces of coursework throughout the course, corresponding to the major topics covered.

## Study hours

• Lectures (23 hours)

## Learning outcomes

On successful completion of this unit, a student will be able to:

• describe image segmentation as a clustering problem and be able to compare different clustering algorithms for segmenting images
• describe interest points and Local Feature in images, compare the strengths and weaknesses of different Local Features and apply them to solve object recognition, image retrieval and stereo-based scene reconstruction problems.
• describe and compare model-based object recognition algorithms and analyse the strengths and weaknesses of model-based vs image-based object recognition computer vision systems.
• describe the basic steps of stereopsis, analyse the differences between sparse and dense stereo vision matching algorithms and apply them to solve stereo-based scene reconstruction problems.
• describe the basic steps of rigid and non-rigid image registration algorithms and analyse their use to biomedical image applications.