Human pose estimation is another area of the remarkable success of the deep neural networks and has had rapid growth in recent years. In the last few chapters, we learned that deep neural networks use a combination of linear (convolution) and nonlinear (ReLU) operations to predict the output for a given set of input images. In the case of pose estimation, the deep neural network predicts the joint locations, when given a set of input images. The labeled dataset in an image consists of a bounding box determining N persons in the image and K joints per person. As the pose changes, the orientation of the joints change, so different positions are characterized by looking into the relative position of the joints. In the following sections, we'll describe the different pose estimation methods we can use.
Mastering Computer Vision with TensorFlow 2.x
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Mastering Computer Vision with TensorFlow 2.x
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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.
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Mastering Computer Vision with TensorFlow 2.x
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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