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.
Author Name: David Clinton
Author Description:
David Clinton is an AWS Solutions Architect and a Linux server administrator. He’s written books on cloud and Linux administration, IT security, and data analytics.

Table of Contents

• Course Overview
  1min
• Image Segmentation Using OpenCV and PIllow
  12mins
• Image Segmentation Using Deep Learning Models
  18mins