Duration
4 weeks (3-4 hours per week)
About this course
Computer Vision is the art of distilling actionable information from images.
In this hands-on course, we’ll learn about Image Analysis techniques using OpenCV and the Microsoft Cognitive Toolkit to segment images into meaningful parts. We’ll explore the evolution of Image Analysis, from classical to Deep-Learning techniques.
We’ll use Transfer Learning and Microsoft ResNet to train a model to perform Semantic Segmentation.
Length: 4 weeks
Effort: 3-4 hours per week
Level: Intermediate
What you'll learn
- Apply classical Image Analysis techniques, such as Edge Detection, Watershed and Distance Transformation as well as K-means Clustering to segment a basic dataset.
- Implement classical Image Analysis algorithms using the OpenCV library.
- Compare classical and Deep-Learning object classification techniques.
- Apply Microsoft ResNet, a deep Convolutional Neural Network (CNN) to object classification using the Microsoft Cognitive Toolkit.
- Apply Transfer Learning to augment ResNet18 for a Fully Convolutional Network (FCN) for Semantic Segmentation.
This is a version-specific reference outline. Confirm the current syllabus and delivery arrangements with EIEIO.