Book Image

Deep Learning for Computer Vision

By : Rajalingappaa Shanmugamani
Book Image

Deep Learning for Computer Vision

By: Rajalingappaa 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 (17 chapters)
Title Page
Copyright and Credits
Packt Upsell
Foreword
Contributors
Preface

Summary


In this chapter, we have learned about the various segmentation algorithms. We also saw the datasets and metrics that are used for benchmarking. We applied the techniques learned to segment satellite and medical images. In the end, we touched upon the Mask R-CNN algorithm for instance segmentation.

In the next chapter, we will learn about similarity learning. Similarity learning models learn a comparison mechanism between two images. It is useful for several applications such as face recognition. We will learn several model architectures that can be used for similarity learning.