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

Applications of generative models


Let's start this chapter with the possible applications of generative models. The applications are enormous. We will see a few of these applications to understand the motivation and possibilities.

Artistic style transfer

Artistic style transfer is the process of transferring the style of art to any image. For example, an image can be created with the artistic style of an image and content of another image. An example of one image combined with several different styles is shown here illustrated by Gatys et al.  (https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Gatys_Image_Style_Transfer_CVPR_2016_paper.pdf). The image A is the photo on which the style is applied, and the results are shown in other images:

Reproduced from Gatys et al.

This application has caught the public's attention, and there are several mobile apps in the market providing this facility. 

Predicting the next frame in a video 

Predicting future frames from synthetic video sets...