ECE5176 · Computer vision

Overview

This unit aims to develop an understanding of methods for extracting useful information (eg 3-D structure; object size, motion, shape, location and identity, etc) from images. It will allow you to understand how to construct computer vision systems for robotics, surveillance, medical imaging, and related application areas.

Areas of study: E6017 Master of Advanced Engineering - Specialisation: Robotics engineering E6017 Master of Advanced Engineering - Specialisation: Telecommunications engineering

Offerings

Assessment

The Handbook lists an examination for this unit.

Continuous assessment: 40% Final assessment: 60% This unit contains threshold hurdle requirements that you must achieve to be able to pass the unit. You are required to achieve at least 45% in the total continuous assessment component and at least 45% in the final assessment component. The consequence of not achieving a hurdle requirement is a fail grade (NH) and a maximum mark of 45 for the unit. The assessments of this unit are designed to demonstrate the achievement of the advanced learning outcomes and standards expected of Master’s level coursework.

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

Requisites

Learning outcomes

1. Appreciate different camera models.
2. Discuss the elements of the human visual system and perception.
3. Apply geometry and photometry to image analysis.
4. Generate implementations for low-level vision processes such as linear filtering, edge detection, texture, multi-view geometry, stereopsis, structure from motion and optical flow and mid-level vision processes, such as segmentation and clustering, model fitting and tracking.
5. Design and implement high-level vision processes such as model-based vision, surfaces and outlines, graphs, range data, templates and classifiers and learning methods.
6. Synthesise and design code to complete computer vision programming exercises in programming languages such as C and MatLab.

Workload

The minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of 3-6 hours of scheduled learning activities and 6-9 hours of independent study per week. Scheduled activities may include a combination of teacher-directed learning, peer-directed learning and online engagement. Independent study may include associated readings, assessment and preparation for scheduled activities.

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