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.
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.
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.
