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

Extending image-based approaches to videos


Images can be used for pose estimation, style transfer, image generation, segmentation, captioning, and so on. Similarly, these applications find a place in videos too. Using the temporal information may improve the predictions from images and vice versa. In this section, we will see how to extend these applications to videos.

Regressing the human pose

Human pose estimation is an important application of video data and can improve other tasks such as action recognition. First, let's see a description of the datasets available for pose estimation:

Pfister et al. (https://www.cv-foundation.org/openaccess/content_iccv_2015...