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.

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Table of Contents (17 chapters)
Title Page
Title Page
Copyright and Credits
Copyright and Credits
Packt Upsell
Packt Upsell
Foreword
Foreword
Contributors
Contributors
Preface
Preface
Free Chapter
1
Getting Started
Getting Started
Understanding deep learning
Deep learning for computer vision
Development environment setup
Summary
2
Image Classification
Image Classification
Training the MNIST model in TensorFlow
Training the MNIST model in Keras
Other popular image testing datasets 
The bigger deep learning models
Training a model for cats versus dogs
Developing real-world applications
Summary
3
Image Retrieval
Image Retrieval
Understanding visual features
Model inference
Content-based image retrieval
Summary
4
Object Detection
Object Detection
Detecting objects in an image
Exploring the datasets
Localizing algorithms 
Detecting objects
Object detection API
The YOLO object detection algorithm 
Summary
5
Semantic Segmentation
Semantic Segmentation
Predicting pixels
Datasets
Algorithms for semantic segmentation
Ultra-nerve segmentation
Segmenting satellite images
Segmenting instances
Summary
6
Similarity Learning
Similarity Learning
Algorithms for similarity learning
Human face analysis
Summary
7
Image Captioning
Image Captioning
Understanding the problem and datasets
Understanding natural language processing for image captioning
Approaches for image captioning and related problems
Implementing attention-based image captioning
Summary
8
Generative Models
Generative Models
Applications of generative models
Neural artistic style transfer
Generative Adversarial Networks
Visual dialogue model
Summary
9
Video Classification
Video Classification
Understanding and classifying videos 
Extending image-based approaches to videos
Summary
10
Deployment
Deployment
Performance of models
Deployment in the cloud
Deployment of models in devices
Summary
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Segmenting satellite images

In this section, we will use a dataset provided by the International Society for Photogrammetry and Remote Sensing (ISPRS). The dataset contains satellite images of Potsdam, Germany with 5 cm resolution. These images come with an additional data of infrared and height contours of the images. There are six labels associated with the images, which are:

  • Building
  • Vegetation
  • Trees
  • Cabs
  • Clutter
  • Impervious

A total of 38 images are provided with 6,000 x 6,000 patches. Please go to the page, http://www2.isprs.org/commissions/comm3/wg4/data-request-form2.html and fill in the form. After that, select the following options on the form:

Post the form, an email will be sent to you, from which the data can be downloaded.

Modeling FCN for segmentation

Import the libraries and get the shape of the input. The number of labels is defined as 6:

from .resnet50 import ResNet50
nb_labels = 6

img_height, img_width, _ = input_shape
input_tensor = tf.keras.layers.Input(shape=input_shape)
weights...
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