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|
Web development Instructor
Brian Hernandez has been in the development field for over a decade. Brain works extensively with Full Stack Web Development, MEAN Stack, MEMR (Mango, Express, MySQL, React) Stack and other Modern Web Frameworks.
Brian is a consultant, and his company is currently catering to clients to want to improve their online presence or build one from scratch. He has worked with high profile companies, helping them move them to digital, both for in-housework, and for having a digital presence for their external stakeholders.
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