Intelligent Systems for Video Analysis and Access Over the Internet surveys new advances in efficient, automated multimedia content analysis, indexing, and access for the next-generation multimedia Web. It presents new algorithms for analyzing the perceptual and conceptual contents of multimedia streams, and introduces intelligent software agents for processing and decomposing them in real time. Finally, it demonstrates how to integrate automated analysis, clustering, and classification to organize, present, and customize multimedia content rapidly and efficiently.

Intelligent Systems for Video Analysis and Access over the Internet (Prentice Hall Imsc Press Multimedia Series.)

Computers & Technology

Kuo, C.C. Jay

Zhou, Wensheng

Computer Science

PRENTICE/HALL

Continuous media applications have exceptionally stringent QoS requirements, and QoS for multimedia will remain a challenge well into the future. This book shows how continuous media applications and QoS-enabled networks can interact, and offers a framework in which applications and the network can cooperatively optimize end-to-end QoS.

Quality of Service for Internet Multimedia

Shin, Jitae

Lee, Daniel C.

Web Development & Design

Semantic Video Object Segmentation for Content-Based Multimedia Applications (The Springer International Series in Engineering and Computer Science)

Semantic Video Object Segmentation for Content-Based Multimedia Applications provides a thorough review of state-of-the-art techniques as well as describing several novel ideas and algorithms for semantic object extraction from image sequences. Semantic object extraction is an essential element in content-based multimedia services, such as the newly developed MPEG4 and MPEG7 standards. An interactive system called SIVOG (Smart Interactive Video Object Generation) is presented, which converts user's semantic input into a form that can be conveniently integrated with low-level video processing. Thus, high-level semantic information and low-level video features are integrated seamlessly into a smart segmentation system. A region and temporal adaptive algorithm was further proposed to improve the efficiency of the SIVOG system so that it is feasible to achieve nearly real-time video object segmentation with robust and accurate performances. Also included is an examination of the shape coding problem and the object segmentation problem simultaneously. Semantic Video Object Segmentation for Content-Based Multimedia Applications will be of great interest to research scientists and graduate-level students working in the area of content-based multimedia representation and applications and its related fields.

Semantic Video Object Segmentation for Content-Based Multimedia Applications (The Springer International Series in Engineering and Computer Science, 639)

Ju Guo

Springer

With the fast growth ofmultimedia information, content-based video anal ysis, indexing and representation have attracted increasing attention in re cent years. Many applications have emerged in these areas such as video on-demand, distributed multimedia systems, digital video libraries, distance learning/education, entertainment, surveillance and geographical information systems. The need for content-based video indexing and retrieval was also rec ognized by ISOIMPEG, and a new international standard called "Multimedia Content Description Interface" (or in short, MPEG-7)was initialized in 1998 and finalized in September 2001. In this context, a systematic and thorough review ofexisting approaches as well as the state-of-the-art techniques in video content analysis, indexing and representation areas are investigated and studied in this book. In addition, we will specifically elaborate on a system which analyzes, indexes and abstracts movie contents based on the integration ofmultiple media modalities. Content ofeach part ofthis book is briefly previewed below. In the first part, we segment a video sequence into a set ofcascaded shots, where a shot consistsofone or more continuouslyrecorded image frames. Both raw and compressedvideo data will beinvestigated. Moreover, consideringthat there are always non-story units in real TV programs such as commercials, a novel commercial break detection/extraction scheme is developed which ex ploits both audio and visual cues to achieve robust results. Specifically, we first employ visual cues such as the video data statistics, the camera cut fre quency, and the existenceofdelimiting black frames between commercials and programs, to obtain coarse-level detection results.

Video Content Analysis Using Multimodal Information: For Movie Content Extraction, Indexing and Representation

Ying, Li

Reference

Radio Resource Management for Multimedia QoS Support in Wireless Networks

Due to the great success and enormous impact of IP networks, In ternet access (such as sending and receiving e-mails) and web brows ing have become the ruling paradigm for next generation wireless systems. On the other hand, great technological and commercial success of services and applications is being witnessed in mobile wire less communications with examples of cellular, pes voice telephony and wireless LANs. The service paradigm has thus shifted from the conventional voice service to seamlessly integrated high quality mul timedia transmission over broadband wireless mobile networks. The multimedia content may include data, voice, audio, image, video and so on. With availability of more powerful portable devices, such as PDA, portable computer and cellular phone, coupled with the easier access to the core network (using a mobile device), the number of mobile users and the demand for multimedia-based applications is increasing rapidly. As a result, there is an urgent need for a sys tem that supports heterogeneous multimedia services and provides seamless access to the desired resources via wireless connections. Therefore, the convergence of multimedia communication and wireless mobile networking technologies into the next generation wireless multimedia (WMM) networks with the vision of "anytime, anywhere, anyform" information system is the certain trend in the foreseeable future. However, successful combination of these two technologies presents many challenges such as available spectral bandwidth, energy efficiency, seamless end-to-end communication, robustness, security, etc.

Business & Money

KUMAR, SUNIL

Huan Chen

Lei Huang

Job Hunting & Careers

Cooperative Communications and Networking: Technologies and System Design

Cooperative and relay communications have recently become the most widely explored topics in communications, whereby users cooperate in transmitting their messages to the destination, instead of conventional networks which operate independently and compete among each other for channel resources. As the field has progressed, cooperative communications have become a design concept rather than a specific transmission technology. This concept has revolutionized the design of wireless networks, allowing increased coverage, throughput, and transmission reliability even as conventional transmission techniques gradually reach their limits. Cooperative and relay technologies have also made their way toward next generation wireless standards, such as IEEE802.16 (WiMAX) or LTE, and have been incorporated into many modern wireless applications, such as cognitive radio and secret communications. 
Cooperative Communications and Networking: Technologies and System Design provides a systematic introduction to the fundamental concepts of cooperative communications and relays technology to enable engineers, researchers or graduate students to conduct advanced research and development in this area.
Cooperative Communications and Networking: Technologies and System Design provides researchers, graduate students, and practical engineers with sufficient knowledge of both the background of cooperative communications and networking, and potential research directions.

Hong, Y.-W. Peter

Huang, Wan-Jen

Semantic Video Object Segmentation for Content-Based Multimedia Applications provides a thorough review of state-of-the-art techniques as well as describing several novel ideas and algorithms for semantic object extraction from image sequences. Semantic object extraction is an essential element in content-based multimedia services, such as the newly developed MPEG4 and MPEG7 standards. An interactive system called SIVOG (Smart Interactive Video Object Generation) is presented, which converts user's semantic input into a form that can be conveniently integrated with low-level video processing. Thus, high-level semantic information and low-level video features are integrated seamlessly into a smart segmentation system. A region and temporal adaptive algorithm was further proposed to improve the efficiency of the SIVOG system so that it is feasible to achieve nearly real-time video object segmentation with robust and accurate performances. Also included is an examination of the shape coding problem and the object segmentation problem simultaneously.  Semantic Video Object Segmentation for Content-Based Multimedia Applications will be of great interest to research scientists and graduate-level students working in the area of content-based multimedia representation and applications and its related fields.

Ju Guo, Ju

Multi-Carrier Techniques for Broadband Wireless Communications provides an accessible introduction to OFDM-based systems from a signal processing perspective. The first part presents a concise treatment of some fundamental concepts related to wireless communications and multicarrier systems, while the second offers a comprehensive survey of recent developments on a variety of critical design issues. These include synchronization techniques, channel estimation methods, adaptive resource allocation and practical schemes for reducing the peak-to-average power ratio of the transmitted waveform.

MULTI-CARRIER TECHNIQUES FOR BROADBAND WIRELESS COMMUNICATIONS: A SIGNAL PROCESSING PERSPECTIVE (Communications and Signal Processing)

Morelli, Michele

Pun, Man-On

IMPERIAL COLLEGE PRESS

From the Back Cover This book introduces the point cloud; its applications in industry, and the most frequently used datasets. It mainly focuses on three computer vision tasks -- point cloud classification, segmentation, and registration -- which are fundamental to any point cloud-based system. An overview of traditional point cloud processing methods helps readers build background knowledge quickly, while the deep learning on point clouds methods include comprehensive analysis of the breakthroughs from the past few years. Brand-new explainable machine learning methods for point cloud learning, which are lightweight and easy to train, are then thoroughly introduced. Quantitative and qualitative performance evaluations are provided. The comparison and analysis between the three types of methods are given to help readers have a deeper understanding.With the rich deep learning literature in 2D vision, a natural inclination for 3D vision researchers is to develop deep learning methods for point cloud processing. Deep learning on point clouds has gained popularity since 2017, and the number of conference papers in this area continue to increase. Unlike 2D images, point clouds do not have a specific order, which makes point cloud processing by deep learning quite challenging. In addition, due to the geometric nature of point clouds, traditional methods are still widely used in industry. Therefore, this book aims to make readers familiar with this area by providing comprehensive overview of the traditional methods and the state-of-the-art deep learning methods.A major portion of this book focuses on explainable machine learning as a different approach to deep learning. The explainable machine learning methods offer a series of advantages over traditional methods and deep learning methods. This is a main highlight and novelty of the book. By tackling three research tasks -- 3D object recognition, segmentation, and registration using our methodology -- readers will have a sense of how to solve problems in a different way and can apply the frameworks to other 3D computer vision tasks, thus give them inspiration for their own future research. Numerous experiments, analysis and comparisons on three 3D computer vision tasks (object recognition, segmentation, detection and registration) are provided so that readers can learn how to solve difficult Computer Vision problems. Product Description This book introduces the point cloud; its applications in industry, and the most frequently used datasets. It mainly focuses on three computer vision tasks -- point cloud classification, segmentation, and registration -- which are fundamental to any point cloud-based system. An overview of traditional point cloud processing methods helps readers build background knowledge quickly, while the deep learning on point clouds methods include comprehensive analysis of the breakthroughs from the past few years. Brand-new explainable machine learning methods for point cloud learning, which are lightweight and easy to train, are then thoroughly introduced. Quantitative and qualitative performance evaluations are provided. The comparison and analysis between the three types of methods are given to help readers have a deeper understanding.With the rich deep learning literature in 2D vision, a natural inclination for 3D vision researchers is to develop deep learning methods for point cloud processing. Deep learning on point clouds has gained popularity since 2017, and the number of conference papers in this area continue to increase. Unlike 2D images, point clouds do not have a specific order, which makes point cloud processing by deep learning quite challenging. In addition, due to the geometric nature of point clouds, traditional methods are still widely used in industry. Therefore, this book aims to make readers familiar with this area by providing comprehensive overview of the traditional methods and the state-of-the-art deep learning methods.A m

3D Point Cloud Analysis: Traditional, Deep Learning, and Explainable Machine Learning Methods

Zhang, Min

Liu, Shan

Kadam, Pranav

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