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Image Segmentation

Many of the millions of digital images we're generating need interpretation, but there aren't enough human eyes for the task. This course will teach you how to use Python libraries and deep learning models to automate image segmentation.

David Clinton - Pluralsight course - Image Segmentation
by David Clinton

What you'll learn

You want your application to consume digital images and convert them to usable data, but it's far too time-consuming to do that manually.

In this course, Image Segmentation, you’ll learn to use Python libraries and deep learning models to automate your image interpretation through segmentation. First, you’ll explore using the OpenCV and Pillow libraries. Next, you’ll discover how to fine tune those libraries, including through the use of the watershed algorithm. Finally, you’ll learn how to use the U-Net and Mask R-CNN deep learning models. When you’re finished with this course, you’ll have the skills and knowledge of image segmentation needed to incorporate image interpretation into your application workflow.

Table of contents

About the author

David Clinton - Pluralsight course - Image Segmentation
David Clinton

David taught high school for twenty years, worked as a Linux system administrator for five years, and has been writing since he could hold a crayon between his fingers. His childhood bedroom wall has since been repainted.

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