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Computer Vision and Image Analysis

A deep dive into Computer Vision, Image Analysis and Semantic Segmentation using the Microsoft Cognitive Toolkit.

Self-Paced

Learning Style

Intermediate

Difficulty

16 Hours

Course Duration

Course Info

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A deep dive into Computer Vision, Image Analysis and Semantic Segmentation using the Microsoft Cognitive Toolkit.

Course Information

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.

Course Objective:

  • 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.

Audience:

  • Vision Science specialist
  • Mathematician
  •  Image Analysis

Prerequisite:

  • Working knowledge of Python
  • Skills equivalent to the following courses

Outline

More Information

More Information
Brand Microsoft
Subjects App Development
Lab Access No
Technology Microsoft
Learning Style Self-Paced Learning
Difficulty Intermediate
Course Duration 16 Hours
Language English

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