The concept of edge detection was covered in Chapter 1, Computer Vision and TensorFlow Fundamentals. In this chapter, you will learn how edge detection is used to create convolution operations over volume and how different convolution parameters such as filter size, dimensions, and operation type (convolution versus pooling) affect the convolution volume (width versus depth). This chapter will give you a very detailed overview of how a neural network sees an image and how it uses that visualization to classify images. You will start by building your first neural network and then visualize an image as it goes through its different layers. You will then compare the network model's accuracy and visualization to an advanced network such as VGG 16 or Inception.
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Book Overview & Buying
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Table Of Contents
Mastering Computer Vision with TensorFlow 2.x
By :
Mastering Computer Vision with TensorFlow 2.x
By:
Overview of this book
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.
Table of Contents (18 chapters)
Preface
Section 1: Introduction to Computer Vision and Neural Networks
Free Chapter
Computer Vision and TensorFlow Fundamentals
Content Recognition Using Local Binary Patterns
Facial Detection Using OpenCV and CNN
Deep Learning on Images
Section 2: Advanced Concepts of Computer Vision with TensorFlow
Neural Network Architecture and Models
Visual Search Using Transfer Learning
Object Detection Using YOLO
Semantic Segmentation and Neural Style Transfer
Section 3: Advanced Implementation of Computer Vision with TensorFlow
Action Recognition Using Multitask Deep Learning
Object Detection Using R-CNN, SSD, and R-FCN
Section 4: TensorFlow Implementation at the Edge and on the Cloud
Deep Learning on Edge Devices with CPU/GPU Optimization
Cloud Computing Platform for Computer Vision
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