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  • Book Overview & Buying Deep Learning for Computer Vision
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Deep Learning for Computer Vision

Deep Learning for Computer Vision

By : Rajalingappaa Shanmugamani
3.2 (22)
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Deep Learning for Computer Vision

Deep Learning for Computer Vision

3.2 (22)
By: Rajalingappaa Shanmugamani
Buy this Book

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 (12 chapters)
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Preface
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Preface
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Who this book is for
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What this book covers
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1
Getting Started
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Getting Started
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Understanding deep learning
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Deep learning for computer vision
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Development environment setup
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Summary
2
Image Classification
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Image Classification
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Training the MNIST model in TensorFlow
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Training the MNIST model in Keras
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Other popular image testing datasets
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The bigger deep learning models
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Training a model for cats versus dogs
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Developing real-world applications
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Summary
3
Image Retrieval
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Image Retrieval
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Understanding visual features
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Model inference
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Content-based image retrieval
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Summary
4
Object Detection
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Object Detection
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Detecting objects in an image
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Exploring the datasets
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Localizing algorithms
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Detecting objects
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Object detection API
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The YOLO object detection algorithm
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Summary
5
Semantic Segmentation
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Semantic Segmentation
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Predicting pixels
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Datasets
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Algorithms for semantic segmentation
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Ultra-nerve segmentation
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Segmenting satellite images
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Segmenting instances
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Summary
6
Similarity Learning
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Similarity Learning
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Algorithms for similarity learning
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Human face analysis
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Summary
7
Image Captioning
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Image Captioning
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Understanding the problem and datasets
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Understanding natural language processing for image captioning
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Approaches for image captioning and related problems
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Implementing attention-based image captioning
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Summary
8
Generative Models
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Generative Models
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Applications of generative models
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Neural artistic style transfer
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Generative Adversarial Networks
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Visual dialogue model
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Summary
9
Video Classification
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Video Classification
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Understanding and classifying videos
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Extending image-based approaches to videos
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Summary
10
Deployment
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Deployment
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Performance of models
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Deployment in the cloud
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Deployment of models in devices
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Summary
11
Other Books You May Enjoy
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Other Books You May Enjoy
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Leave a review - let other readers know what you think

Algorithms for semantic segmentation

There are several deep learning-based algorithms that were proposed to solve image segmentation tasks. A sliding window approach can be applied at a pixel level for segmentation. A sliding window approach takes an image and breaks the image into smaller crops. Every crop of the image is classified for a label. This approach is expensive and inefficient because it doesn't reuse the shared features between the overlapping patches. In the following sections, we will discuss a few algorithms that can overcome this problem.

The Fully Convolutional Network

The Fully Convolutional Network (FCN) introduced the idea of an end-to-end convolutional network. Any standard CNN architecture can be used for FCN by removing the fully connected layers, and the implementation of the same was shown in Chapter 4, Object Detection. The fully connected layers are replaced by a convolution layer. The depth is higher in the final layers and the size is smaller. Hence, 1D convolution...

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Tech Concepts
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Programming languages
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Deep Learning for Computer Vision
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