Apply neural network architectures to build state-of-the-art computer vision applications using the Python programming language
Key Features
Gain a fundamental understanding of advanced computer vision and neural network models in use today
Cover tasks such as low-level vision, image classification, and object detection
Develop deep learning models on cloud platforms and optimize them using TensorFlow Lite and the OpenVINO toolkit
Book Description
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
Who this book is for
This book is for computer vision professionals, image processing professionals, machine learning engineers and AI developers who have some knowledge of machine learning and deep learning and want to build expert-level computer vision applications. In addition to familiarity with TensorFlow, Python knowledge will be required to get started with this book.
Table of Contents
Computer Vision and Tensorflow Fundamentals
Content Recognition using Local Binary Pattern
Face Recognition and Tracking using Viola Jones Algorithm & OpenCV
Deep learning on images
Neural Network Architecture & Models
Visual Search using Transfer Learning
Object Detection using YOLO
Semantic Segmentation and Neural Style Transfer
Action Recognition using Multitask Deep Learning
Object Classification and Detection using RCNN
Deep Learning on Edge Devices with GPU/CPU Optimization
Cloud Computing Platform for Computer Vision