Computer Vision and Image Analysis
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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.
- 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.
- Vision Science specialist
- Image Analysis
- Working knowledge of Python
- Skills equivalent to the following courses
|Learning Style||Self-Paced Learning|
|Course Duration||16 Hours|