Mastering Computer Vision with TensorFlow 2.x: Build advanced computer vision applications using machine learning and deep learning techniques
by: Krishnendu Kar
Print Length 页数: 430 pages
Publisher finelybook 出版社: Packt Publishing (15 May 2020)
Language 语言: English
ISBN-10: 1838827064
ISBN-13: 9781838827069
Book Description
Apply neural network architectures to build state-of-the-art computer vision applications using the Python programming language
Computer vision allows machines to gain human-level understanding to visualize,process,and analyze images and videos. This book focuses on using TensorFlow to help you learn advanced computer vision tasks such as image acquisition,processing,and analysis. You’ll start with the key principles of computer vision and deep learning to build a solid foundation,before covering neural network architectures and understanding how they work rather than using them as a black box. Next,you’ll explore architectures such as VGG,ResNet,Inception,R-CNN,SSD,YOLO,and MobileNet. As you advance,you’ll learn to use visual search methods using transfer learning. You’ll also cover advanced computer vision concepts such as semantic segmentation,image inpainting with GAN’s,object tracking,video segmentation,and action recognition. Later,the book focuses on how machine learning and deep learning concepts can be used to perform tasks such as edge detection and face recognition. You’ll then discover how to develop powerful neural network models on your PC and on various cloud platforms. Finally,you’ll learn to perform model optimization methods to deploy models on edge devices for real-time inference. By the end of this book,you’ll have a solid understanding of computer vision and be able to confidently develop models to automate tasks.
What you will learn
Explore methods of feature extraction and image retrieval and visualize different layers of the neural network model
Use TensorFlow for various visual search methods for real-world scenarios
Build neural networks or adjust parameters to optimize the performance of models
Understand TensorFlow DeepLab to perform semantic segmentation on images and DCGAN for image inpainting
Evaluate your model and optimize and integrate it into your application to operate at scale
Get up to speed with techniques for performing manual and automated image annotation
Table of Contents
Preface
Section 1: Introduction to Computer Vision and Neural Networks
Chapter 1: Computer Vision and TensorFlow Fundamentals
Chapter 2: Content Recognition Using Local Binary Patterns
Chapter 3: Facial Detection Using OpenCV and CNN
Chapter 4: Deep Learning on lmages
Section 2: Advanced Concepts of Computer Vision with TensorFlow
Chapter 5: Neural Network Architecture and Models
Chapter 6: Visual Search Using Transfer Learning
Chapter 7: Object Detection Using YOLO
Chapter 8: Semantic Segmentation and Neural Style Transfer
Section 3: Advanced Implementation of Computer Vision with
TensorFlow
Chapter 9: Action Recognition Using Multitask Deep Learning
Chapter 10: Object Detection Using R-CNN,SSD,and R-FCN
Section 4: TensorFlow Implementation at the Edge and on the
Cloud
Chapter 11: Deep Learning on Edge Devices with CPU/GPU
Optimization
Chapter 12: Cloud Computing Platform for Computer Vision
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