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

Ultra-nerve segmentation


The Kaggler is an organization that conducts competitions on predictive modelling and analytics. The Kagglers were once challenged to segment nerve structures from ultrasound images of the neck. The data regarding the same can be downloaded from https://www.kaggle.com/c/ultrasound-nerve-segmentation. The UNET model proposed by Ronneberger et al. (https://arxiv.org/pdf/1505.04597.pdf) resembles an autoencoder but with convolutions instead of a fully connected layer. There is an encoding part with the convolution of decreasing dimensions and a decoder part with increasing dimensions as shown here:

Figure illustrating the architecture of the UNET model [Reproduced with permission from Ronneberger et al.]

The convolutions of the similar sized encoder and decoder part are learning by skip connections. The output of the model is a mask that ranges between 0 and 1. Let's start by importing the functions, with the help of the following code:

import os
from skimage.transform...