PY3TDV-Three-Dimensional Vision

Module Provider: Psychology
Number of credits: 10 [5 ECTS credits]
Level:6
Terms in which taught: Autumn term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2020/1

Module Convenor: Prof Andrew Glennerster

Email: a.glennerster@reading.ac.uk

Type of module:

Summary module description:
Three-dimensional vision

Aims:

This module will enable students to explore the following; how the brain combines information from images to represent a three-dimensional scene. This can either be a combination of left and right eyes’ images (binocular stereopsis) or images over time (structure from motion). This option aims to give students the experience of critical evaluations of recent research approaches on the physiology, psychophysics and computational models of 3D vision in the brain.ÌýWe will consider the general problem of finding correspondence between features seen in the two eyes or across time which is necessary to see depth and how neurons in visual cortex contribute to this process. We will look into the modern approaches to 3D representation using deep neural nets that do not involve a 3D reconstruction.

Assessable learning outcomes:

By the end of the module, the student will be able to:



1. Critically analyse psychophysical and neurophysiological experiments about the models of 3D vision.



2. Appraise key research approaches and theoretical perspectives on 3D vision.

Additional outcomes:

Collaboration with others in formulating research questions and designing research projects within this topic area; Analysis and synthesis of complex literature relating to 3D vision; Ability to critically evaluate research and theory independently and as part of a small discussion group.

Outline content:

The module comprises seven 2-hour seminars.



Look around you. It seems that your perception of the 3D scene you can see is formed effortlessly but we do not know how it is done. The range of current hypotheses is surprisingly wide. People often think of 3D vision as binocular stereo (eg 3D movies) but information from moving images (eg ordinary movies) is just as important. In this module, we study the information that is available in images to support 3D vision. We wi ll focus particularly on the relevant literature from psychophysics and neurophysiology and relate these to recent developments in machine learning for 3D vision.

Brief description of teaching and learning methods:

Seminars involve lectures, interactive discussions, practical activities and student presentations. During the course of the module, students will prepare a collaborative presentation on a relevant topic.

Contact hours:

Ìý	Autumn	Spring	Summer
Lectures	14		1
Guided independent study:	85
Ìý	Ìý	Ìý	Ìý
Total hours by term		0
Ìý	Ìý	Ìý	Ìý
Total hours for module	100

Summative Assessment Methods:

Method	Percentage
Written exam	75
Written assignment including essay	25

Summative assessment- Examinations:
1.5 hours

Summative assessment- Coursework and in-class tests:

This module is assessed through coursework (25%) and a final exam (75%).Ìý



The 1.5-hour Summer Exam will require students to answer 1 essay question on topics covered in the module.



Coursework will comprise a 2000-word essay.Ìý

Formative assessment methods:

Students will be provided with feedback on their collaborative presentation activities. This feedback will help students prepare for the final exam.

Penalties for late submission:

The Module Convenor will apply the following penalties for work submitted late:

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day[1] (or part thereof) following the deadline up to a total of five working days;
• where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

The University policy statement on penalties for late submission can be found at:
You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Assessment requirements for a pass:
A minimum of 40% overall

Reassessment arrangements:
Re-assessment is by re-examination in the August/September period

Additional Costs (specified where applicable):

Last updated: 4 April 2020