Skip to content | Change text size

M O N A T A R

InfoTech Unit Avatar

ITO5221 Intelligent image and video analysis

Chief Examiner

This field records the Chief Examiner for unit approval purposes. It does not publish, and can only be edited by Faculty Office staff

To update the published Chief Examiner, you will need to update the Faculty Information/Contact Person field below.

NB: This view restricted to entries modified on or after 19990401000000

Unit Code, Name, Abbreviation

ITO5221 Intelligent image and video analysis (22 Jun 2020, 09:05am) [Intel Image Video Analysis (05 Feb 2021, 3:48pm)]

Reasons for Introduction

Reasons for Introduction (22 Jun 2020, 09:05am)

Created as part of the Master of Computer Science degree, and is one of six core units in the Artificial Intelligence specialisation.

Objectives

Objectives (10 Feb 2021, 10:56am)

On successful completion of this unit, you should be able to:

  1. describe image analysis and low-level vision.
  2. describe semantic image and video understanding techniques.
  3. describe CNN and their applications.
  4. implement and extend existing computer vision algorithms.
  5. evaluate and compare techniques suitable for adding vision capability to unimodal and multimodal intelligent systems.

Unit Content

ASCED Discipline Group Classification (05 Feb 2021, 3:49pm)

020119

Synopsis (22 Jun 2020, 09:06am)

This unit will discuss the fundamental and modern concepts in image and video analysis. The students will be introduced to the basics of image processing and low-level vision. The topics related to image formation, operations, features and segmentation will enable students with the understanding for developing vision enabled systems. Concepts related to convolutional neural networks (CNN) will be introduced and recent examples will be thoroughly analysed. Recent computer vision concepts will be discussed from a deep learning perspective. The unit will be balanced between the theoretical and the practical implementation aspects of computer vision.

Teaching Methods

Mode (22 Jun 2020, 09:06am)

Online

Assessment

Assessment Summary (05 Feb 2021, 3:50pm)

In-semester assessment: 100%

Workloads

Resource Requirements

Teaching Responsibility (Callista Entry) (22 Jun 2020, 09:07am)

FIT

Prerequisites

Prerequisite Units (22 Jun 2020, 09:07am)

ITO5047, ITO5136, ITO5163

Corequisites (22 Jun 2020, 09:08am)

Must be enrolled into the Master of Computer Science

Prohibitions (22 Jun 2020, 09:08am)

FIT5221

Proposed year of Introduction (for new units) (22 Jun 2020, 09:09am)

MO-TP6, 2020

Location of Offering (22 Jun 2020, 09:09am)

Monash Online

Faculty Information

Proposer

Emma Nash

Approvals

School:
Faculty Education Committee:
Faculty Board:
ADT:
Faculty Manager:
Dean's Advisory Council:
Other:

Version History

22 Jun 2020 Emma Nash
10 Feb 2021 Jeanette Niehus modified UnitObjectives/Objectives

This version: