IMAGE PROCESSING

CSE3VIS

2020

Credit points: 15

Subject outline

This subject covers both fundamentals of image processing as well as computing techniques with applications in many cutting-edge domains such as image recognition, object detection and segmentation, image registration and retrieval. Design issues on image recognition will be addressed, which contain eigenface technology, image feature extraction, similarity measurement, and performance evaluation. Practice on image recognition will be offered in Labs.

School: Engineering and Mathematical Sciences (Pre 2022)

Credit points: 15

Subject Co-ordinator: Lydia Cui

Available to Study Abroad/Exchange Students: Yes

Subject year level: Year Level 3 - UG

Exchange Students: No

Available as Elective: No

Learning Activities: N/A

Capstone subject: No

Subject particulars

Subject rules

Prerequisites: CSE2AIF

Co-requisites: N/A

Incompatible subjects: N/A

Equivalent subjects: N/A

Quota Management Strategy: N/A

Quota-conditions or rules: N/A

Special conditions: N/A

Minimum credit point requirement: N/A

Assumed knowledge: N/A

Learning resources

Pattern Recognition and Machine Learning

Resource Type: Book

Resource Requirement: Recommended

Author: Christopher M. Bishop

Year: 2006

Edition/Volume: N/A

Publisher: Springer

ISBN: N/A

Chapter/article title: N/A

Chapter/issue: N/A

URL: N/A

Other description: N/A

Source location: N/A

Digital Image Processing

Resource Type: Book

Resource Requirement: Recommended

Author: Rafael C. Gonzalez, Richard E. Woods.

Year: 2017

Edition/Volume: N/A

Publisher: Pearson

ISBN: N/A

Chapter/article title: N/A

Chapter/issue: N/A

URL: N/A

Other description: N/A

Source location: N/A

Career Ready

Career-focused: No

Work-based learning: No

Self sourced or Uni sourced: N/A

Entire subject or partial subject: N/A

Total hours/days required: N/A

Location of WBL activity (region): N/A

WBL addtional requirements: N/A

Graduate capabilities & intended learning outcomes

Graduate Capabilities

Intended Learning Outcomes

01. Demonstrate understanding of image processing fundamentals by being able to explain data sampling and quantization in acquisition, image enhancement and denoising, and image representation.

02. Define learned image processing and pattern recognition techniques for image recognition, object detection and segmentation, image registration and retrieval.

03. Implement an image recognition system with learned knowledge and techniques.

04. Evaluate the performance and robustness of image processing techniques for real world applications.

Melbourne (Bundoora), 2020, Semester 1, Day

Overview

Online enrolment: Yes

Maximum enrolment size: N/A

Enrolment information: N/A

Subject Instance Co-ordinator: Lydia Cui

Class requirements

Laboratory ClassWeek: 11 - 22
One 2.00 hours laboratory class per week on weekdays during the day from week 11 to week 22 and delivered via face-to-face.

LectureWeek: 10 - 22
One 2.00 hours lecture per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.

Assessments

Assessment element	Comments	Category	Contribution	Hurdle	%	ILO*
One 3-hour examination (equivalent to 3,000 words)Hurdle requirement: To pass the subject, a pass in the examination is mandatory.	N/A	N/A	N/A	Yes	70	SILO1, SILO2
Design report (equivalent to 1200 words)	N/A	N/A	N/A	No	30	SILO3, SILO4