Dates:
Fundamentals of Computer Vision:
20 Nov, 21 Nov, 27 Nov, 30 Nov, 4 Dec, 5 Dec 2023 | 9am-5.30pm | Classroom Learning
Duration: 6 Days
Course Overview
This course provides a comprehensive introduction to Computer Vision, designed for anyone new to computer vision and looking to improve their knowledge and skills in this field. By the end of the course, participants will have a better understanding of Computer Vision concepts, be able to manage Computer Vision projects, interpret Computer Vision results, and understand the limitations of Computer Vision. Throughout the course, participants will use Python to explore and apply the concepts learned in the lectures. They will gain practical experience in image processing, object detection, segmentation, and other computer vision tasks using Python libraries and frameworks.
Course Objectives
This Professional Certificate will equip learners with the following competencies:
- A conceptual understanding of Computer Vision
- The ability to manage Computer Vision projects
- The ability to interpret Computer Vision results
- An understanding of the limitations of Computer Vision
Job Role Readiness
- Computer Vision Engineers
- Image Processing Engineers
- Computer Vision Users
Programme Structure
Who Should Attend
- Engineers who want to expand their skills in Computer Vision
- Software developers interested in Computer Vision applications
- Data scientist/analysts who want to explore Computer Vision techniques
- Researchers who want to apply Computer Vision in their work
- Professionals who want to build a career in Computer Vision
- Anyone who wants to apply Computer Vision skills in their current job.
Prerequisites
- Basic programming in Python
- Mathematics knowledge equivalent to ‘Additional Mathematics’ at O-level
Course Conveners
(Click their photos to view their short biographies)
Dr Natarajan Prabhu
Dr Natarajan Prabhu
Dr Natarajan Prabhu is currently a lecturer in the School of Computing at the National University of Singapore. He has 10+ years of experience in teaching for master’s degree programs, undergraduate modules, and continuing education courses. Before joining NUS, he was teaching at DigiPen Institute of Technology, where he taught AI for Games, Digital Image Processing, Machine Learning, Deep Learning, Data Structures, etc. In DigiPen, he developed a master’ degree program for Computer Vision that primarily prepares graduate students to work in the CV industry. After joining NUS as a lecturer, he is currently working on developing and teaching an AI module for non-CS students in Blended learning.
He graduated with a Ph.D. degree from NUS in 2013, a master’s degree, and a bachelor’s degree from Anna University in 2008 and 2006, respectively. His Ph.D. thesis was about automatically controlling and coordinating multiple active cameras in surveillance networks. During this time he has gained rich experience in building multi-camera surveillance systems. He has received “Best PhD Forum Paper” award from International Conference on Distributed Smart Cameras (Hong Kong, 2012) and “Research Achievement Award” from School of Computing, NUS (2012).
Assoc Prof Terence Sim
Assoc Prof Terence Sim
Explain. Demonstrate. Experiment. Inspire.
The above sums up Dr Terence Sim’s teaching and research philosophy. Over the years, Dr Sim has had the pleasure of teaching many courses – Introductory Programming, Computer Vision and Pattern Recognition, Digital Visual Effects, Theoretical Foundations of Multimedia, Analysis of Multimedia – and interacting with many talented students. He is currently teaching a freshmen module in Discrete Structures, and a graduate module in Biometrics Authentication.
For research, Dr Sim explores several areas related to Visual Computing: Facial image analysis, Multimodal biometrics, Facial rendering, Computational photography, Continuous authentication, Music transcription, to name a few. He combines machine learning with physics-based modeling and graphics rendering to tackle the challenges in research. Dr Sim also provides consultancy in biometrics, which can be in the form of training, feasibility study, or technical assessment.
Dr Sim has published over 100 papers in top international journals and conferences. He is active both as Reviewer and as Senior Program Committee Member in numerous conferences. He is also an IEEE Member. He served as President of the Pattern Recognition and Machine Intelligence Association in Singapore from 2014 to 2016, and was also Vice President from 2010 to 2014. Dr Sim also strongly believes in International Standards and served as Chairman of Workgroup 6: Cross-Jurisdiction and Societal Issues of the Biometrics Technical Committee in Singapore from 2006 to 2014.
Dr Sim considers it a blessing and privilege to have attended, and graduated from three top universities in the world: He obtained his Bachelor of Science in Computer Science and Engineering in 1990 from the Massachusetts Institute of Technology (MIT), his Master of Science in Computer Science in 1991 from Stanford University, and his Doctor of Philosophy in Electrical Engineering in 2002 from Carnegie Mellon University.
Course Fees
Total Nett Programme Fee Payable, Including GST, after additional funding from the various funding schemes
Participants must fulfill at least 75% attendance and pass all assessment components to be eligible for SSG funding.
To enquire, email soc-ace@nus.edu.sg
To register, click Register
Course Code
Fundamentals of Computer Vision Course: TGS- 2021010306 (Classroom Learning)
