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Author: Wei Xiang (Ph.D.) Publisher: ISBN: Category : Application software Languages : en Pages : 128
Book Description
With the massive storage of multimedia data and increasing computational power of mobile devices, developing scalable computer vision applications has become the primary motivation for both research and industrial community. Among these applications, object detection and semantic segmentation are two of the most popular topics which, in addition, serve as the fundamental features for many computer vision systems under platforms like mobile, healthcare, autonomous driving, etc. Inspired by the current and foreseeable trend, this thesis focuses on developing both effective and efficient object detection and semantic segmentation models, with the large-scale,publicly available data sets sourced for various applications.In the last several years, object detection and semantic segmentation have received large attention in the literature, and have been significantly advanced with the emergence of deep learning methods. Particularly, by applying Convolutional Neural Networks (CNNs), researchers have leveraged unsupervised features in modeling which greatly simplified the tasks of classification and regression, compared to using merely hand-crafted features in those traditional approaches. In object detection, however, there still exist many open research problems like integrating contextual information to the existing models, the missing relationship between proposal scales and receptive field sizes for different CNNs, etc. In this thesis,we study extensively such relationship, and further demonstrate that our statistical results can be used as a guideline to design both heuristically and efficiently new detection models, with an improvement of detection accuracy particularly for small objects.In semantic segmentation, we investigate many of the state-of-the-art methods and figure out that current research have largely focused on using complicated backbones together with some popular meta-architectures and designs which, in turn,leads to the problem of overtting and incapability for real-time tasks. To overcome this issue, we propose Turbo Unified Network (ThunderNet), which builds on a minimum backbone followed by a pyramid pooling module and a customized, two-level lightweight decoder. Our experimental results show that ThunderNet remains one of the fastest models that are currently available, while achieving comparable accuracy to a majority of methods in the literature. We also test ThunderNet with a GPU-powered embedded platform{NVIDIA Jetson TX2, whose results indicate that ThunderNet performs sufficiently fast and accurate, thus meeting the demands for embedded system. Finally, this thesis also surveys on the joint calibration methods for RGB-D sensor. We summarize the related work and present our quantitative evaluation results thereafter.
Author: Wei Xiang (Ph.D.) Publisher: ISBN: Category : Application software Languages : en Pages : 128
Book Description
With the massive storage of multimedia data and increasing computational power of mobile devices, developing scalable computer vision applications has become the primary motivation for both research and industrial community. Among these applications, object detection and semantic segmentation are two of the most popular topics which, in addition, serve as the fundamental features for many computer vision systems under platforms like mobile, healthcare, autonomous driving, etc. Inspired by the current and foreseeable trend, this thesis focuses on developing both effective and efficient object detection and semantic segmentation models, with the large-scale,publicly available data sets sourced for various applications.In the last several years, object detection and semantic segmentation have received large attention in the literature, and have been significantly advanced with the emergence of deep learning methods. Particularly, by applying Convolutional Neural Networks (CNNs), researchers have leveraged unsupervised features in modeling which greatly simplified the tasks of classification and regression, compared to using merely hand-crafted features in those traditional approaches. In object detection, however, there still exist many open research problems like integrating contextual information to the existing models, the missing relationship between proposal scales and receptive field sizes for different CNNs, etc. In this thesis,we study extensively such relationship, and further demonstrate that our statistical results can be used as a guideline to design both heuristically and efficiently new detection models, with an improvement of detection accuracy particularly for small objects.In semantic segmentation, we investigate many of the state-of-the-art methods and figure out that current research have largely focused on using complicated backbones together with some popular meta-architectures and designs which, in turn,leads to the problem of overtting and incapability for real-time tasks. To overcome this issue, we propose Turbo Unified Network (ThunderNet), which builds on a minimum backbone followed by a pyramid pooling module and a customized, two-level lightweight decoder. Our experimental results show that ThunderNet remains one of the fastest models that are currently available, while achieving comparable accuracy to a majority of methods in the literature. We also test ThunderNet with a GPU-powered embedded platform{NVIDIA Jetson TX2, whose results indicate that ThunderNet performs sufficiently fast and accurate, thus meeting the demands for embedded system. Finally, this thesis also surveys on the joint calibration methods for RGB-D sensor. We summarize the related work and present our quantitative evaluation results thereafter.
Author: Le Lu Publisher: Springer ISBN: 331942999X Category : Computers Languages : en Pages : 327
Book Description
This book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks, with the theory supported by practical examples. Features: highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in medical image computing; discusses the insightful research experience of Dr. Ronald M. Summers; presents a comprehensive review of the latest research and literature; describes a range of different methods that make use of deep learning for object or landmark detection tasks in 2D and 3D medical imaging; examines a varied selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to interleaved text and image deep mining on a large-scale radiology image database.
Author: Valliappa Lakshmanan Publisher: "O'Reilly Media, Inc." ISBN: 1098102339 Category : Computers Languages : en Pages : 481
Book Description
This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability. Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras. You'll learn how to: Design ML architecture for computer vision tasks Select a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your task Create an end-to-end ML pipeline to train, evaluate, deploy, and explain your model Preprocess images for data augmentation and to support learnability Incorporate explainability and responsible AI best practices Deploy image models as web services or on edge devices Monitor and manage ML models
Author: Le Lu Publisher: Springer Nature ISBN: 3030139697 Category : Computers Languages : en Pages : 461
Book Description
This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases. It particularly focuses on the application of convolutional neural networks, and on recurrent neural networks like LSTM, using numerous practical examples to complement the theory. The book’s chief features are as follows: It highlights how deep neural networks can be used to address new questions and protocols, and to tackle current challenges in medical image computing; presents a comprehensive review of the latest research and literature; and describes a range of different methods that employ deep learning for object or landmark detection tasks in 2D and 3D medical imaging. In addition, the book examines a broad selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to text and image deep embedding for a large-scale chest x-ray image database; and discusses how deep learning relational graphs can be used to organize a sizable collection of radiology findings from real clinical practice, allowing semantic similarity-based retrieval. The intended reader of this edited book is a professional engineer, scientist or a graduate student who is able to comprehend general concepts of image processing, computer vision and medical image analysis. They can apply computer science and mathematical principles into problem solving practices. It may be necessary to have a certain level of familiarity with a number of more advanced subjects: image formation and enhancement, image understanding, visual recognition in medical applications, statistical learning, deep neural networks, structured prediction and image segmentation.
Author: D.J. Hemanth Publisher: IOS Press ISBN: 1614998221 Category : Computers Languages : en Pages : 284
Book Description
Deep learning and image processing are two areas of great interest to academics and industry professionals alike. The areas of application of these two disciplines range widely, encompassing fields such as medicine, robotics, and security and surveillance. The aim of this book, ‘Deep Learning for Image Processing Applications’, is to offer concepts from these two areas in the same platform, and the book brings together the shared ideas of professionals from academia and research about problems and solutions relating to the multifaceted aspects of the two disciplines. The first chapter provides an introduction to deep learning, and serves as the basis for much of what follows in the subsequent chapters, which cover subjects including: the application of deep neural networks for image classification; hand gesture recognition in robotics; deep learning techniques for image retrieval; disease detection using deep learning techniques; and the comparative analysis of deep data and big data. The book will be of interest to all those whose work involves the use of deep learning and image processing techniques.
Author: Fouad Sabry Publisher: One Billion Knowledgeable ISBN: Category : Computers Languages : en Pages : 159
Book Description
What is Object Detection The field of computer technology known as object detection is closely associated with computer vision and image processing. Its primary objective is to identify instances of semantic objects belonging to a specific class inside digital images and videos. In the field of object detection, face detection and pedestrian detection are two areas that have received extensive attention. Object detection is useful in a wide variety of computer vision applications, including image retrieval and video surveillance, among others. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Object detection Chapter 2: Computer vision Chapter 3: Image segmentation Chapter 4: Template matching Chapter 5: Optical braille recognition Chapter 6: Deep learning Chapter 7: Convolutional neural network Chapter 8: DeepDream Chapter 9: Saliency map Chapter 10: Small object detection (II) Answering the public top questions about object detection. (III) Real world examples for the usage of object detection in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Object Detection.
Author: Iffat Zafar Publisher: Packt Publishing Ltd ISBN: 1789132827 Category : Computers Languages : en Pages : 264
Book Description
Learn how to apply TensorFlow to a wide range of deep learning and Machine Learning problems with this practical guide on training CNNs for image classification, image recognition, object detection and many computer vision challenges. Key Features Learn the fundamentals of Convolutional Neural Networks Harness Python and Tensorflow to train CNNs Build scalable deep learning models that can process millions of items Book Description Convolutional Neural Networks (CNN) are one of the most popular architectures used in computer vision apps. This book is an introduction to CNNs through solving real-world problems in deep learning while teaching you their implementation in popular Python library - TensorFlow. By the end of the book, you will be training CNNs in no time! We start with an overview of popular machine learning and deep learning models, and then get you set up with a TensorFlow development environment. This environment is the basis for implementing and training deep learning models in later chapters. Then, you will use Convolutional Neural Networks to work on problems such as image classification, object detection, and semantic segmentation. After that, you will use transfer learning to see how these models can solve other deep learning problems. You will also get a taste of implementing generative models such as autoencoders and generative adversarial networks. Later on, you will see useful tips on machine learning best practices and troubleshooting. Finally, you will learn how to apply your models on large datasets of millions of images. What you will learn Train machine learning models with TensorFlow Create systems that can evolve and scale during their life cycle Use CNNs in image recognition and classification Use TensorFlow for building deep learning models Train popular deep learning models Fine-tune a neural network to improve the quality of results with transfer learning Build TensorFlow models that can scale to large datasets and systems Who this book is for This book is for Software Engineers, Data Scientists, or Machine Learning practitioners who want to use CNNs for solving real-world problems. Knowledge of basic machine learning concepts, linear algebra and Python will help.
Author: Ben Othman Soufiene Publisher: CRC Press ISBN: 1003805671 Category : Technology & Engineering Languages : en Pages : 270
Book Description
Machine Learning and Deep Learning Techniques for Medical Image Recognition comprehensively reviews deep learning-based algorithms in medical image analysis problems including medical image processing. It includes a detailed review of deep learning approaches for semantic object detection and segmentation in medical image computing and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks with the theory and varied selection of techniques for semantic segmentation using deep learning principles in medical imaging supported by practical examples. Features: Offers important key aspects in the development and implementation of machine learning and deep learning approaches toward developing prediction tools and models and improving medical diagnosis Teaches how machine learning and deep learning algorithms are applied to a broad range of application areas, including chest X-ray, breast computer-aided detection, lung and chest, microscopy, and pathology Covers common research problems in medical image analysis and their challenges Focuses on aspects of deep learning and machine learning for combating COVID-19 Includes pertinent case studies This book is aimed at researchers and graduate students in computer engineering, artificial intelligence and machine learning, and biomedical imaging.
Author: Wei Xiang (Ph.D.)
Author: Wei Xiang (Ph.D.)
Author: Le Lu
Author: Valliappa Lakshmanan
Author: Le Lu
Author: D.J. Hemanth
Author: Jason Brownlee
Author: Fouad Sabry
Author: Iffat Zafar
Author: Pedro Henrique Oliveira Pinheiro
Author: Ben Othman Soufiene