FIT5221 · Intelligent image and video analysis

Overview

This unit will discuss the fundamental and modern concepts in image and video analysis. You 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 you 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 balance between the theoretical and the practical implementation aspects of computer vision.

Offerings

Assessment

The Handbook lists an examination for this unit.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

1. describe image analysis and low-level vision
2. describe semantic image and video understanding techniques
3. describe convolutional neural networks 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

Workload

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled teaching activities.

Student experience, not official rules. Nothing here changes what the Handbook says.