IMAGE PROCESSING

CSE4VIS

Not currently offered

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 measure, 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 4 - UG/Hons/1st Yr PG

Available as Elective: No

Learning Activities: N/A

Capstone subject: No

Subject particulars

Subject rules

Prerequisites: CSE2AIF or admission into one of the following courses: SMIT, SMICT, SMITCN, SMCSC, SGIT or SGCS

Co-requisites: N/A

Incompatible subjects: CSE3VIS AND Students in the following courses are not permitted to enrol: SBCS, SBIT, SBITP and SBBIY

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

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

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

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. Demonstrate knowledge of major components and issues in image processing applications by being able to describe learned image processing and pattern recognition techniques for image recognition, object detection and segmentation, image registration and retrieval.
03. Implement and evaluate an image recognition system with learned knowledge and techniques.
04. Research and explain the significance of robust image recognition techniques for real applications.
Subject not currently offered - Subject options not available.