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Deep Learning for Computer Vision

Deep Learning for Computer Vision

By : Shanmugamani
3.2 (22)
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Deep Learning for Computer Vision

Deep Learning for Computer Vision

3.2 (22)
By: Shanmugamani

Overview of this book

Deep learning has shown its power in several application areas of Artificial Intelligence, especially in Computer Vision. Computer Vision is the science of understanding and manipulating images, and finds enormous applications in the areas of robotics, automation, and so on. This book will also show you, with practical examples, how to develop Computer Vision applications by leveraging the power of deep learning. In this book, you will learn different techniques related to object classification, object detection, image segmentation, captioning, image generation, face analysis, and more. You will also explore their applications using popular Python libraries such as TensorFlow and Keras. This book will help you master state-of-the-art, deep learning algorithms and their implementation.
Table of Contents (12 chapters)
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Semantic Segmentation

In this chapter, we will learn about various semantic segmentation techniques and train models for the same. Segmentation is a pixel-wise classification task. The ideas to solve segmentation problem is an extension to object detection problems. Segmentation is highly useful in applications such medical and satellite image understanding. 

The following topics will be covered in the chapter:

  • Learning the difference between semantic segmentation and instance segmentation
  • Segmentation datasets and metrics 
  • Algorithms for semantic segmentation
  • Application of segmentation to medical and satellite images
  • Algorithms for instance segmentation

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