David Doermann – författare

The technical ability to generate volumes of digital multimedia data is becoming increasingly "mainstream" in today's electronic world. Online services create volumes of primarily textual information, such as news reports, product reviews, and e-mail chronicles. Advances in digital video technology have given organizations the capability to amass visual records and produce collections of surveillance monitoring data streams. With this ability to generate and archive volumes of data comes the potential of deriving or recalling information and knowledge from these data histories. To effectively utilize the growing number of multimedia data repositories, there is a convergence in technologies from large-scale data management, semantic-oriented media (text, image, and video) understanding, and multi-source trend analysis. This convergence is not straightforward and introduces a significant challenge in construction solutions that offer scalable deployment with semantically rich quality.The Microelectronics and Computer Technology Corporation (MCC) and its member companies carried out a study in 1997 to investigate the state of the art in technologies for annotating and manipulating large-scale networks of multimedia information objects with content-based concepts. This book documents the study's technology assessment and identifies shortcomings where further research and integration of technologies are needed to meet anticipated application requirements. The major points highlighted in this book can be used as cornerstones for defining advanced research and development directions, and opportunities to exploit the content available in networks of large-scale multi-media sources. Based on the results of the study, MCC initiated the Content-Based Access to Multimedia (CBAM) Information project to investigate semantically-oriented access to large-scale image and video repositories. The project focuses on concept extraction, annotation, and collection principles applied in and across large-scale image and video repositories.It demonstrates proof-of-concept environments where multimedia objects acquire semantic content annotations and become elements exploited in distributed information-gathering applications.

The Handbook of Document Image Processing and Recognition is a comprehensive resource on the latest methods and techniques in document image processing and recognition. Each chapter provides a clear overview of the topic followed by the state of the art of techniques used – including elements of comparison between them – along with supporting references to archival publications, for those interested in delving deeper into topics addressed. Rather than favor a particular approach, the text enables the reader to make an informed decision for their specific problems.

Deep learning has achieved impressive results in image classification, computer vision, and natural language processing. To achieve better performance, deeper and wider networks have been designed, which increase the demand for computational resources. The number of floatingpoint operations (FLOPs) has increased dramatically with larger networks, and this has become an obstacle for convolutional neural networks (CNNs) being developed for mobile and embedded devices. In this context, Binary Neural Networks: Algorithms, Architectures, and Applications will focus on CNN compression and acceleration, which are important for the research community. We will describe numerous methods, including parameter quantization, network pruning, low-rank decomposition, and knowledge distillation. More recently, to reduce the burden of handcrafted architecture design, neural architecture search (NAS) has been used to automatically build neural networks by searching over a vast architecture space. Our book will also introduce NAS and binary NAS and its superiority and state-of-the-art performance in various applications, such as image classification and object detection. We also describe extensive applications of compressed deep models on image classification, speech recognition, object detection, and tracking. These topics can help researchers better understand the usefulness and the potential of network compression on practical applications. Moreover, interested readers should have basic knowledge of machine learning and deep learning to better understand the methods described in this book.Key FeaturesReviews recent advances in CNN compression and accelerationElaborates recent advances on binary neural network (BNN) technologiesIntroduces applications of BNN in image classification, speech recognition, object detection, and more

Deep learning has achieved impressive results in image classification, computer vision, and natural language processing. To achieve better performance, deeper and wider networks have been designed, which increase the demand for computational resources. The number of floatingpoint operations (FLOPs) has increased dramatically with larger networks, and this has become an obstacle for convolutional neural networks (CNNs) being developed for mobile and embedded devices. In this context, Binary Neural Networks: Algorithms, Architectures, and Applications will focus on CNN compression and acceleration, which are important for the research community. We will describe numerous methods, including parameter quantization, network pruning, low-rank decomposition, and knowledge distillation. More recently, to reduce the burden of handcrafted architecture design, neural architecture search (NAS) has been used to automatically build neural networks by searching over a vast architecture space. Our book will also introduce NAS and binary NAS and its superiority and state-of-the-art performance in various applications, such as image classification and object detection. We also describe extensive applications of compressed deep models on image classification, speech recognition, object detection, and tracking. These topics can help researchers better understand the usefulness and the potential of network compression on practical applications. Moreover, interested readers should have basic knowledge of machine learning and deep learning to better understand the methods described in this book.Key FeaturesReviews recent advances in CNN compression and accelerationElaborates recent advances on binary neural network (BNN) technologiesIntroduces applications of BNN in image classification, speech recognition, object detection, and more

This text aims to be a reference for the practitioners and academicians in the fields of multimedia search engines. Half a terabyte or 9000 hours of motion pictures are produced around the world every year. Furthermore, 3000 television stations broadcasting for twenty-four hours a day produce eight million hours per year, amounting to 24,000 terabytes of data. Although some of the data is labelled at the time of production, an enormous portion remains unindexed. For practical access to such huge amounts of data, there is a great need to develop efficient tools for browsing and retrieving content of interest, so that producers and end users can quickly locate specific video sequences in this ocean of audio-visual data. This text describes the main techniques being developed by the major players in industry and academic research to address this problem.

Video Mining is an essential reference for the practitioners and academicians in the fields of multimedia search engines. Half a terabyte or 9,000 hours of motion pictures are produced around the world every year. Furthermore, 3,000 television stations broadcasting for twenty-four hours a day produce eight million hours per year, amounting to 24,000 terabytes of data. Although some of the data is labeled at the time of production, an enormous portion remains unindexed. For practical access to such huge amounts of data, there is a great need to develop efficient tools for browsing and retrieving content of interest, so that producers and end users can quickly locate specific video sequences in this ocean of audio-visual data. Video Mining is important because it describes the main techniques being developed by the major players in industry and academic research to address this problem. It is the first time research from these leaders in the field developing the next-generation multimedia search engines is being described in great detail and gathered into a single volume.Video Mining will give valuable insights to all researchers and non-specialists who want to understand the principles applied by the multimedia search engines that are about to be deployed on the Internet, in studios' multimedia asset management systems, and in video-on-demand systems.

In the past five years, the field of electrostatic discharge (ESD) control has under­ gone some notable changes. Industry standards have multiplied, though not all of these, in our view, are realistic and meaningful. Increasing importance has been ascribed to the Charged Device Model (CDM) versus the Human Body Model (HBM) as a cause of device damage and, presumably, premature (latent) failure. Packaging materials have significantly evolved. Air ionization techniques have improved, and usage has grown. Finally, and importantly, the government has ceased imposing MIL-STD-1686 on all new contracts, leaving companies on their own to formulate an ESD-control policy and write implementing documents. All these changes are dealt with in five new chapters and ten new reprinted papers added to this revised edition of ESD from A to Z. Also, the original chapters have been augmented with new material such as more troubleshooting examples in Chapter 8 and a 20-question multiple-choice test for certifying operators in Chapter 9. More than ever, the book seeks to provide advice, guidance, and practical ex­ amples, not just a jumble of facts and generalizations. For instance, the added tailored versions of the model specifications for ESD-safe handling and packaging are actually in use at medium-sized corporations and could serve as patterns for many readers.

In the fall of 2006, the University of Maryland, along with various government and industrial sponsors, invited leading researchers from all over the world to a two-day Summit on Arabic and Chinese Handwriting Recognition (SACH 2006).

Deep learning has achieved impressive results in image classification, computer vision and natural language processing. To achieve better performance, deeper and wider networks have been designed, which increase the demand for computational resources. The number of floating-point operations (FLOPs) has increased dramatically with larger networks, and this has become an obstacle for convolutional neural networks (CNNs) being developed for mobile and embedded devices. In this context, our book will focus on CNN compression and acceleration, which are important for the research community. We will describe numerous methods, including parameter quantization, network pruning, low-rank decomposition and knowledge distillation. More recently, to reduce the burden of handcrafted architecture design, neural architecture search (NAS) has been used to automatically build neural networks by searching over a vast architecture space. Our book will also introduce NAS due to its superiority and state-of-the-art performance in various applications, such as image classification and object detection. We also describe extensive applications of compressed deep models on image classification, speech recognition, object detection and tracking. These topics can help researchers better understand the usefulness and the potential of network compression on practical applications. Moreover, interested readers should have basic knowledge about machine learning and deep learning to better understand the methods described in this book.