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Riding on the success of 3D cinema blockbusters and advances in
stereoscopic display technology, 3D video applications have
gathered momentum in recent years. 3D-TV System with
Depth-Image-Based Rendering: Architectures, Techniques and
Challenges surveys depth-image-based 3D-TV systems, which are
expected to be put into applications in the near future.
Depth-image-based rendering (DIBR) significantly enhances the 3D
visual experience compared to stereoscopic systems currently in
use. DIBR techniques make it possible to generate additional
viewpoints using 3D warping techniques to adjust the perceived
depth of stereoscopic videos and provide for auto-stereoscopic
displays that do not require glasses for viewing the 3D image. The
material includes a technical review and literature survey of
components and complete systems, solutions for technical issues,
and implementation of prototypes. The book is organized into four
sections: System Overview, Content Generation, Data Compression and
Transmission, and 3D Visualization and Quality Assessment. This
book will benefit researchers, developers, engineers, and
innovators, as well as advanced undergraduate and graduate students
working in relevant areas.
Extremal Optimization: Fundamentals, Algorithms, and Applications
introduces state-of-the-art extremal optimization (EO) and modified
EO (MEO) solutions from fundamentals, methodologies, and algorithms
to applications based on numerous classic publications and the
authors' recent original research results. It promotes the movement
of EO from academic study to practical applications. The book
covers four aspects, beginning with a general review of real-world
optimization problems and popular solutions with a focus on
computational complexity, such as "NP-hard" and the "phase
transitions" occurring on the search landscape. Next, it introduces
computational extremal dynamics and its applications in EO from
principles, mechanisms, and algorithms to the experiments on some
benchmark problems such as TSP, spin glass, Max-SAT (maximum
satisfiability), and graph partition. It then presents studies on
the fundamental features of search dynamics and mechanisms in EO
with a focus on self-organized optimization, evolutionary
probability distribution, and structure features (e.g., backbones),
which are based on the authors' recent research results. Finally,
it discusses applications of EO and MEO in multiobjective
optimization, systems modeling, intelligent control, and production
scheduling. The authors present the advanced features of EO in
solving NP-hard problems through problem formulation, algorithms,
and simulation studies on popular benchmarks and industrial
applications. They also focus on the development of MEO and its
applications. This book can be used as a reference for graduate
students, research developers, and practical engineers who work on
developing optimization solutions for those complex systems with
hardness that cannot be solved with mathematical optimization or
other computational intelligence, such as evolutionary
computations.
Riding on the success of 3D cinema blockbusters and advances in
stereoscopic display technology, 3D video applications have
gathered momentum in recent years. 3D-TV System with
Depth-Image-Based Rendering: Architectures, Techniques and
Challenges surveys depth-image-based 3D-TV systems, which are
expected to be put into applications in the near future.
Depth-image-based rendering (DIBR) significantly enhances the 3D
visual experience compared to stereoscopic systems currently in
use. DIBR techniques make it possible to generate additional
viewpoints using 3D warping techniques to adjust the perceived
depth of stereoscopic videos and provide for auto-stereoscopic
displays that do not require glasses for viewing the 3D image. The
material includes a technical review and literature survey of
components and complete systems, solutions for technical issues,
and implementation of prototypes. The book is organized into four
sections: System Overview, Content Generation, Data Compression and
Transmission, and 3D Visualization and Quality Assessment. This
book will benefit researchers, developers, engineers, and
innovators, as well as advanced undergraduate and graduate students
working in relevant areas.
Extremal Optimization: Fundamentals, Algorithms, and Applications
introduces state-of-the-art extremal optimization (EO) and modified
EO (MEO) solutions from fundamentals, methodologies, and algorithms
to applications based on numerous classic publications and the
authors' recent original research results. It promotes the movement
of EO from academic study to practical applications. The book
covers four aspects, beginning with a general review of real-world
optimization problems and popular solutions with a focus on
computational complexity, such as "NP-hard" and the "phase
transitions" occurring on the search landscape. Next, it introduces
computational extremal dynamics and its applications in EO from
principles, mechanisms, and algorithms to the experiments on some
benchmark problems such as TSP, spin glass, Max-SAT (maximum
satisfiability), and graph partition. It then presents studies on
the fundamental features of search dynamics and mechanisms in EO
with a focus on self-organized optimization, evolutionary
probability distribution, and structure features (e.g., backbones),
which are based on the authors' recent research results. Finally,
it discusses applications of EO and MEO in multiobjective
optimization, systems modeling, intelligent control, and production
scheduling. The authors present the advanced features of EO in
solving NP-hard problems through problem formulation, algorithms,
and simulation studies on popular benchmarks and industrial
applications. They also focus on the development of MEO and its
applications. This book can be used as a reference for graduate
students, research developers, and practical engineers who work on
developing optimization solutions for those complex systems with
hardness that cannot be solved with mathematical optimization or
other computational intelligence, such as evolutionary
computations.
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