|
Showing 1 - 7 of
7 matches in All Departments
Focused on the mathematical foundations of social media analysis,
Graph-Based Social Media Analysis provides a comprehensive
introduction to the use of graph analysis in the study of social
and digital media. It addresses an important scientific and
technological challenge, namely the confluence of graph analysis
and network theory with linear algebra, digital media, machine
learning, big data analysis, and signal processing. Supplying an
overview of graph-based social media analysis, the book provides
readers with a clear understanding of social media structure. It
uses graph theory, particularly the algebraic description and
analysis of graphs, in social media studies. The book emphasizes
the big data aspects of social and digital media. It presents
various approaches to storing vast amounts of data online and
retrieving that data in real-time. It demystifies complex social
media phenomena, such as information diffusion, marketing and
recommendation systems in social media, and evolving systems. It
also covers emerging trends, such as big data analysis and social
media evolution. Describing how to conduct proper analysis of the
social and digital media markets, the book provides insights into
processing, storing, and visualizing big social media data and
social graphs. It includes coverage of graphs in social and digital
media, graph and hyper-graph fundamentals, mathematical foundations
coming from linear algebra, algebraic graph analysis, graph
clustering, community detection, graph matching, web search based
on ranking, label propagation and diffusion in social media,
graph-based pattern recognition and machine learning, graph-based
pattern classification and dimensionality reduction, and much more.
This book is an ideal reference for scientists and engineers
working in social media and digital media production and
distribution. It is also suitable for use as a textbook in
undergraduate or graduate courses on digital media, social media,
or social networks.
Focused on the mathematical foundations of social media analysis,
Graph-Based Social Media Analysis provides a comprehensive
introduction to the use of graph analysis in the study of social
and digital media. It addresses an important scientific and
technological challenge, namely the confluence of graph analysis
and network theory with linear algebra, digital media, machine
learning, big data analysis, and signal processing. Supplying an
overview of graph-based social media analysis, the book provides
readers with a clear understanding of social media structure. It
uses graph theory, particularly the algebraic description and
analysis of graphs, in social media studies. The book emphasizes
the big data aspects of social and digital media. It presents
various approaches to storing vast amounts of data online and
retrieving that data in real-time. It demystifies complex social
media phenomena, such as information diffusion, marketing and
recommendation systems in social media, and evolving systems. It
also covers emerging trends, such as big data analysis and social
media evolution. Describing how to conduct proper analysis of the
social and digital media markets, the book provides insights into
processing, storing, and visualizing big social media data and
social graphs. It includes coverage of graphs in social and digital
media, graph and hyper-graph fundamentals, mathematical foundations
coming from linear algebra, algebraic graph analysis, graph
clustering, community detection, graph matching, web search based
on ranking, label propagation and diffusion in social media,
graph-based pattern recognition and machine learning, graph-based
pattern classification and dimensionality reduction, and much more.
This book is an ideal reference for scientists and engineers
working in social media and digital media production and
distribution. It is also suitable for use as a textbook in
undergraduate or graduate courses on digital media, social media,
or social networks.
Color Image Processing: Methods and Applications embraces two
decades of extraordinary growth in the technologies and
applications for color image processing. The book offers
comprehensive coverage of state-of-the-art systems, processing
techniques, and emerging applications of digital color imaging. To
elucidate the significant progress in specialized areas, the
editors invited renowned authorities to address specific research
challenges and recent trends in their area of expertise. The book
begins by focusing on color fundamentals, including color
management, gamut mapping, and color constancy. The remaining
chapters detail the latest techniques and approaches to
contemporary and traditional color image processing and analysis
for a broad spectrum of sophisticated applications, including:
Vector and semantic processing Secure imaging Object recognition
and feature detection Facial and retinal image analysis Digital
camera image processing Spectral and superresolution imaging Image
and video colorization Virtual restoration of artwork Video shot
segmentation and surveillance Color Image Processing: Methods and
Applications is a versatile resource that can be used as a graduate
textbook or as stand-alone reference for the design and the
implementation of various image and video processing tasks for
cutting-edge applications. This book is part of the Digital Imaging
and Computer Vision series.
The function of a filter is to transform a signal into another one
more suit able for a given purpose. As such, filters find
applications in telecommunica tions, radar, sonar, remote sensing,
geophysical signal processing, image pro cessing, and computer
vision. Numerous authors have considered deterministic and
statistical approaches for the study of passive, active, digital,
multidimen sional, and adaptive filters. Most of the filters
considered were linear although the theory of nonlinear filters is
developing rapidly, as it is evident by the numerous research
papers and a few specialized monographs now available. Our research
interests in this area created opportunity for cooperation and co
authored publications during the past few years in many nonlinear
filter families described in this book. As a result of this
cooperation and a visit from John Pitas on a research leave at the
University of Toronto in September 1988, the idea for this book was
first conceived. The difficulty in writing such a mono graph was
that the area seemed fragmented and no general theory was available
to encompass the many different kinds of filters presented in the
literature. However, the similarities of some families of nonlinear
filters and the need for such a monograph providing a broad
overview of the whole area made the pro ject worthwhile. The result
is the book now in your hands, typeset at the Department of
Electrical Engineering of the University of Toronto during the
summer of 1989."
The function of a filter is to transform a signal into another one
more suit able for a given purpose. As such, filters find
applications in telecommunica tions, radar, sonar, remote sensing,
geophysical signal processing, image pro cessing, and computer
vision. Numerous authors have considered deterministic and
statistical approaches for the study of passive, active, digital,
multidimen sional, and adaptive filters. Most of the filters
considered were linear although the theory of nonlinear filters is
developing rapidly, as it is evident by the numerous research
papers and a few specialized monographs now available. Our research
interests in this area created opportunity for cooperation and co
authored publications during the past few years in many nonlinear
filter families described in this book. As a result of this
cooperation and a visit from John Pitas on a research leave at the
University of Toronto in September 1988, the idea for this book was
first conceived. The difficulty in writing such a mono graph was
that the area seemed fragmented and no general theory was available
to encompass the many different kinds of filters presented in the
literature. However, the similarities of some families of nonlinear
filters and the need for such a monograph providing a broad
overview of the whole area made the pro ject worthwhile. The result
is the book now in your hands, typeset at the Department of
Electrical Engineering of the University of Toronto during the
summer of 1989."
This book is the most up-to-date introduction to digital video and
television. It is very suitable to university/college/arts students
and video enthusiasts, by providing an accurate presentation,
without too many mathematical/technical details. It covers all
technologies related to video shooting/acquisition, editing,
compression, optical storage, broadcasting and display. To this
end, various video compression methods (MPEG-2, MPEG-4, HEVC) and
broadcasting systems (ATSC, DVB, DTMB, ISDB) are overviewed. Novel
trends in video streaming, webcasting and mobile video are
presented. An overview of the latest trends in production,
post-production and visual effects is presented for movie and TV
content creation. Human perception of video and quality enhancement
through video processing are detailed. Video analysis, description
and archiving for fast video search are overviewed. Finally, novel
trends in 3DTV and digital cinema are presented.
A unique collection of algorithms and lab experiments for practitioners and researchers of digital image processing technology With the field of digital image processing rapidly expanding, there is a growing need for a book that would go beyond theory and techniques to address the underlying algorithms. Digital Image Processing Algorithms and Applications fills the gap in the field, providing scientists and engineers with a complete library of algorithms for digital image processing, coding, and analysis. Digital image transform algorithms, edge detection algorithms, and image segmentation algorithms are carefully gleaned from the literature for compatibility and a track record of acceptance in the scientific community. The author guides readers through all facets of the technology, supplementing the discussion with detailed lab exercises in EIKONA, his own digital image processing software, as well as useful PDF transparencies. He covers in depth filtering and enhancement, transforms, compression, edge detection, region segmentation, and shape analysis, explaining at every step the relevant theory, algorithm structure, and its use for problem solving in various applications. The availability of the lab exercises and the source code (all algorithms are presented in C-code) over the Internet makes the book an invaluable self-study guide. It also lets interested readers develop digital image processing applications on ordinary desktop computers as well as on Unix machines.
|
You may like...
Barbie
Margot Robbie, Ryan Gosling
Blu-ray disc
R266
Discovery Miles 2 660
|