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

Localizing algorithms 


Localization algorithms are an extension of the materials learned in Chapter 2, Image Classification and Chapter 3, Image Retrieval. In image classification, an image is passed through several layers of a CNN (convolutional neural network). The final layer of CNN outputs the probabilistic value, belonging to each of the labels. This can be extended to localize the objects. We will see these ideas in the following sections.

Localizing objects using sliding windows

An intuitive way of localization is to predict several cropped portions of an image with an object. The cropping of the images can be done by moving a window across the image and predicting for every window. The method of moving a smaller window than the image and cropping the image according to window size is called a sliding window. A prediction can be made for every cropped window of the image which is called sliding window object detection. 

The prediction can be done by the deep learning model trained for...