Computer Vision and Image Analysis

Computer Vision and Image Analysis

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

About this course

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This course is part of the Microsoft Professional Program in Artificial Intelligence.

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.

edX offers financial assistance for learners who want to earn Verified Certificates but who may not be able to pay the fee. To apply for financial assistance, enroll in the course, then follow this link to complete an application for assistance.

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.

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