Course Code: AIB553
Synopsis
Computer vision is a scientific field that enables computers to “see” –-- understand the content of images and videos and use that information to solve real-world problems without human assistance. This course AIB553 Computer Vision & Applications aims to teach the fundamental concepts and various applications of computer vision. The topics covered include image representation, feature detection and matching, camera model, Convolutional Neural Network (CNN), image classification, face recognition, semantic segmentation and Generative Adversarial Network (GAN).
Level: 5
Credit Units: 5
Presentation Pattern: EVERY REGULAR SEMESTER
Topics
- Introduction to computer vision
- Image formation
- Camera model
- Feature detection and matching
- Foundations of Convolutional Neural Networks (CNN)
- Deep Convolutional Models
- Image classification
- Object detection and face recognition
- Object tracking
- Image segmentation
- Generative Adversarial Network (GAN)
- Computer vision for business
Learning Outcome
- Appraise the image processing fundamentals
- Construct robust image matching and stitching
- Evaluate camera and projection models
- Critique the fundamental theory and techniques of CNN
- Formulate a CNN model to solve image classification problem
- Propose various computer vision applications for business
Who Should Attend
Managers and executives engaged in leveraging advanced Artificial intelligence (AI) and Machine learning (ML) methods for data-driven decision-making to address business challenges.
Relevance of Course to employment/upskilling/reskilling
The course addresses crucial competencies by providing students with comprehensive knowledge of computer vision fundamentals and their applications. It equips learners to appraise image processing fundamentals, create robust image matching and stitching solutions, evaluate camera and projection models, and critically analyse Convolutional Neural Network (CNN) theory and techniques. These skills are highly valuable for employment development and job upgrading, as they enable individuals to formulate CNN models for image classification and propose various computer vision applications for businesses. Given the growing importance of computer vision in industries like healthcare, automotive, and retail, this course aligns perfectly with industry needs, preparing graduates to excel in the competitive job market.
Admissions pre-requisite
- An undergraduate degree or an equivalent qualification from a recognised institution
- Specific courses may have additional requirements or pre-requisite (e.g. counselling courses). For more information, you may contact the Head of Programme regarding additional course requirements or pre-requisite
Subject to your eligibility and the approval of the Head of Programme, credits earned (up to a cap of 30 credit units) from the completion of Graduate CET Modular courses from the suite of Graduate Programmes may be recognised when admitted to the relevant Graduate Programmes.
Schedule
| Time | Agenda |
|---|---|
| Week 1 | |
| 19:00 | Introduction & Course Overview |
| 19:30 | Introduction to Computer Vision |
| 20:30 | Break |
| 20:45 | Image formation , Conclusion & Q&A |
| Week 2 | |
| 19:00 | Camera model |
| 19:45 | Break |
| 19:55 | Feature detection and matching |
| 20:40 | Break |
| 20:50 | Feature detection and matching, Conclusion & Q&A |
| Week 3 | |
| 19:00 | Foundations of Convolutional Neural Networks (CNN) |
| 19:45 | Break |
| 19:55 | Deep Convolutional Models |
| 20:40 | Break |
| 20:50 | Deep Convolutional Models, Conclusion & Q&A |
| Week 4 | |
| 19:00 | Image classification |
| 19:45 | Break |
| 19:55 | Image classification, Object detection and face recognition |
| 20:40 | Break |
| 20:50 | Object detection and face recognition, Conclusion & Q&A |
| Week 5 | |
| 19:00 | Object tracking |
| 19:45 | Break |
| 19:55 | Object tracking, Image segmentation |
| 20:40 | Break |
| 20:50 | Image segmentation, Conclusion & Q&A |
| Week 6 | |
| 19:00 | Generative Adversarial Network (GAN) |
| 19:45 | Break |
| 19:55 | Generative Adversarial Network (GAN) |
| 20:40 | Break |
| 20:50 | Computer vision for business, Conclusion & Q&A |
Assessments
The overall course grade is determined by
- Others, Assignments
- Quiz, Case study
Trainer info
Dr. Jing Zhang is a Professor, Doctoral Supervisor, and Provincial Outstanding Talent at the School of Biomedical Engineering, Sichuan University, where he also serves as the Head of the Department of Medical Information Engineering.
He completed his undergraduate studies in Automation at Tsinghua University and earned both his Master’s and Ph.D. degrees in Electrical and Computer Engineering from the National University of Singapore. Dr. Zhang has previously held positions as a Senior Research Fellow at the Institute for Infocomm Research in Singapore and as Assistant Director at the Media Innovation Institute of Nanyang Technological University. Since 2011, he has been a faculty member at Sichuan University.
Dr. Zhang has published over 80 papers in international journals and conferences, including Nature, IEEE Transactions on Medical Imaging, Medical Image Analysis, NIPS. He holds two international patents, and more than 20 Chinese patents. His contributions have been recognized with awards such as the Sichuan Province Thousand Talents Plan Innovative and Entrepreneurial Team and the Chengdu Rongpiao Plan Innovative and Entrepreneurial Team. He has led numerous research projects in Singapore and China, including a National Natural Science Foundation general project, a project of the Ministry of Industry and Information Technology's AI Medical Device Project Selection and Leadership Program, an 863 program subproject, a Ministry of Education Overseas Returned Scholars Project, three Sichuan Province Science and Technology Department projects, one Sichuan University Talent Introduction Research Start-up Project, and several industry-sponsored projects.
Course Completion requirements
- Participants are required to achieve at least 75% attendance and pass any prescribed examinations/assessments or submit any course/project work (if any) under the course requirement.
- Participants are required to complete all surveys and feedbacks related to the course
- The course fees are reviewed annually and may be revised. The University reserves the right to adjust the course fees without prior notice.
- Singapore University of Social Sciences reserves the right to amend and/or revise the above schedule without prior notice
Course Fees, payment and refund policy
| International Participants | Singapore Citizens (below 40yrs), Permanent Residents | Singapore Citizens (40yrs and above) SkillsFuture Mid - Career Enhanced Subsidy1 | Enhanced Training Support for SMEs2 (Singaporean and PRs) | |
|---|---|---|---|---|
| Course Fees (A) | $3,168.00 | $2,640.00 | $2,640.00 | $2,640.00 |
| SSG Grant (70%) (B) | $1,848.00 | $1,848.00 | $1,848.00 | |
| Nett Course fees (A) - (B) = (C) | $3,168.00 | $792.00 | $792.00 | $792.00 |
| 9% GST on Nett course fees (D) | $285.12 | $71.28 | $71.28 | $71.28 |
| Total nett course fees payable including GST (C) + (D) | $3,453,12 | $863.28 | $863.28 | $863.28 |
| Less additional funding if eligible under various schemes (F) | - | - | $528.00 | $528.00 |
| Total nett course fees payable including GST, after additional funding form the various schemes (E) - (f) = (H) | $3,453.12 | $863.28 | $335.28 | $335.28 |
1 Mid-Career Enhanced Subsidy: Singaporeans aged 40 and above may enjoy subsidies up to 90% of the course fees.
2 Enhanced Training Support for SMEs: SME-sponsored employees (Singapore citizens and PRs) aged 21 and above may enjoy subsidies up to 90% of the course fees.
For the various payment mode, please refer here.
For the refund policy, please refer here.
A written request for a refund must be submitted and is subject to approval.
If written notice of withdrawal is given within the cooling off period1 and before the course start date, a full refund of the fees paid less an administrative charge of $110.00 (exclusive of GST) will be given. No refund will be given for withdrawal thereafter.
1 The cooling off period is defined as 7 working days after payment of course fee.
For clarification, please contact the SUSS Academy via the following:
Telephone: +65 6248 0263
Email: [email protected]