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Computer Vision and TensorFlow Fundamentals

Computer vision is rapidly expanding in many different applications as traditional techniques, such as image thresholding, filtering, and edge detection, have been augmented by deep learning methods. TensorFlow is a widely used, powerful machine learning tool created by Google. It has user configurable APIs available to train and build complex neural network model in your local PC or in the cloud and optimize and deploy at scale in edge devices.

In this chapter, you will gain an understanding of advanced computer vision concepts using TensorFlow. This chapter discusses the foundational concepts of computer vision and TensorFlow to prepare you for the later, more advanced chapters of this book. We will look at how to perform image hashing and filtering. Then, we will learn about various methods of feature extraction and image retrieval...

Key benefits

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

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.

Who is this book 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.

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

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

$65.99

Mastering Computer Vision with TensorFlow 2.x

$48.99

Advanced Deep Learning with TensorFlow 2 and Keras

$43.99

Total $ 158.97

Kindle Customer Dec 29, 2020

Good variety of examples to work through. The examples and concepts go beyond the typical introductory book. The code is generic enough to run locally or on AWS SageMaker. The author provides great support when problems were encountered. I'm glad I purchased this book.Would love to see another advanced book that covers TensorFlow model zoo, model/research, object detection API, TensorRT and deploying models to IoT edge devices (e.g. NVIDIA Jetsons)

Amazon Verified review

maninblack Jul 22, 2020

This book is a good starting point for computer science professionals who are new to the field of Computer Vision. I personally have not worked with CV a lot, but have the general prerequisite knowledge of machine learning concepts, and found this book to be a good starting point. This book had a good balance between theoretical concepts and working code snippets that can be tried out. The GitHub repo linked in the book was very helpful to replicate the code snippets as well, and I have not come across many books with this kind of functionality. All the code snippets I tried out worked well without any problems. From a practical implementation point of view, the section on deploying the model on a phone was quite interesting and fun to explore. Each concept mentioned in the book was very well illustrated and supported with thorough mathematical explanations as well.On the downside, I felt the book was focused mostly on image applications and could have also included more applications with videos as input in addition to images. I would have also liked a little more intuition when explaining certain concepts, in addition to the mathematical formulations already mentioned in the book. That really helps readers grasp the concept well.Overall, this was a well-written and instructive book for people wanting to gain knowledge in the CV domain!

MIke R Jul 04, 2020

There are many books out there / but this book stands out - very clear explanation of codes and contents, lots of detailed explanations for object detection, classification, visual search, matching and training in cloud. All the codes work and and their screen video has been really helpful. I find deployment of model in phone and Raspberry PI example has many practical usage. +4minus point: some sections like action recognition and semantic segmentation could be expanded more with training a custom model. -1bonus point: the author has been very helpful to connect and answer questions through LinkedIn +1

c Jun 24, 2022

Says it teaches image in painting but the linked notebook is just a GAN to generate MNIST digits

Mastering Computer Vision with TensorFlow 2.x: Build advanced computer vision applications using machine learning and deep learning techniques

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Mastering Computer Vision with TensorFlow 2.x

Computer Vision and TensorFlow Fundamentals

Technical requirements

Detecting edges using image hashing and filtering

Extracting features from an image

Object detection using Contours and the HOG detector

Contour detection

An overview of TensorFlow, its ecosystem, and installation

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