Advanced Computer Vision Training

Computer Vision is a field of Artificial Intelligence and Computer Science that aims at giving computers a visual understanding of the world. Computer Vision has been used in face recognition, autonomous cars, image search, optical character recognition, robotics vision, machine vision, and many applications. 

Computer Vision is undergoing rapid advances in recent years. In this course, you will learn state of the art computer vision techniques such Region-based CNN and YOLO techniques. You wil also learn about convolutional neural network and transfer learning. The topics includes:

• ConvNET
• Transfer Learning
• R-CNN Based Object Detection
• YOLO Based Object Detection

Module 1 Overview of Computer Vision

• Computer Vision Tasks
• Computer Vision Applications
• Computer Vision Approaches

Module 2 ConvNet

• What is ConvNET?
• ConvNET Architecture
• Image Classification with ConvNET
• ConvNET with Small Datasets
• Overfitting - Dropout & Data Augmentation
• Visualize ConvNET Layer

Module 3 Transfer Learning

• What is Transfer Learning
• Pre-Trained Models
• Data Augmentation
• Fine Tune Pre-Trained Models
• Visualize VGG16 layers

Module 4 R-CNN

• R-CNN
• Fast R-CNN
• Faster R-CNN
• Mask R-CNN
• Mask R-CNN Demo

Module 5 YOLO

• What is YOLO
• YOLO Algorithm
• Anchor Boxes
• IOU
• Non Max Suppression
• YOLO v3 Demo

Wee Chee Hong is a veteran consultant in the field of Engineering Design. He has over 20 years experience in electronics/ IT industry with several MNCs including Compaq, Adaptec, Agilent, etc., with specialization in Automation System Design (drilling/ milling machine, 3D machine automation, machine data collection, test & measurement electronics design), Motion control design & applications. With wide technical skill set in development language/ tools .Net, Java, PowerBuilder to C++/ C/C#, he has also strong knowledge in development of firmware for microcontroller such as PIC, ARM and others.

Chee Hong graduated with a B.Eng degree in Electrical Electronics Engineering and holds a Master Degree of Technology from NUS.

Dr.Sai Manoj Prakhya currently is Principal Engineer for Autonomous Vehicle Technology company. He worked as research scientist in A*Star. He got his PhD in 3D Perception for Mobile Robotics from NUS in 2017. His expertise are Self-Driving Cars, 3D Mapping and Localization, Sensor Fusion and Object Tracking, Mobile Robotics, 3D Perception, PCL (Point Cloud Library), ROS (Robot Operating System), Deep Learning, PIC Microcontroller, C, C++, Python, MATLAB.