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Author: Roumen Kountchev
Publisher: Springer Nature
ISBN: 9811978425
Category : Technology & Engineering
Languages : en
Pages : 287
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Book Description
This book is a collection of papers presented at the International Workshop on New Approaches for Multidimensional Signal Processing (NAMSP 2022), held at Technical University of Sofia, Sofia, Bulgaria, during 23–25 June 2022. The book covers research papers in the field of N-dimensional multicomponent image processing, multidimensional image representation and super-resolution, 3D image processing and reconstruction, MD computer vision systems, multidimensional multimedia systems, neural networks for MD image processing, data-based MD image retrieval and knowledge data mining, watermarking, hiding and encryption of MD images, MD image processing in robot systems, tensor-based data processing, 3D and multi-view visualization, forensic analysis systems for MD images and many more.
Author: Roumen Kountchev
Publisher: Springer Nature
ISBN: 9811978425
Category : Technology & Engineering
Languages : en
Pages : 287
Get Book Here
Book Description
This book is a collection of papers presented at the International Workshop on New Approaches for Multidimensional Signal Processing (NAMSP 2022), held at Technical University of Sofia, Sofia, Bulgaria, during 23–25 June 2022. The book covers research papers in the field of N-dimensional multicomponent image processing, multidimensional image representation and super-resolution, 3D image processing and reconstruction, MD computer vision systems, multidimensional multimedia systems, neural networks for MD image processing, data-based MD image retrieval and knowledge data mining, watermarking, hiding and encryption of MD images, MD image processing in robot systems, tensor-based data processing, 3D and multi-view visualization, forensic analysis systems for MD images and many more.
Author: John W. Woods
Publisher: Academic Press
ISBN: 0123814219
Category : Computers
Languages : en
Pages : 617
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Book Description
Multidimensional Signal, Image, and Video Processing and Coding gives a concise introduction to both image and video processing, providing a balanced coverage between theory, applications and standards. It gives an introduction to both 2-D and 3-D signal processing theory, supported by an introduction to random processes and some essential results from information theory, providing the necessary foundation for a full understanding of the image and video processing concepts that follow. A significant new feature is the explanation of practical network coding methods for image and video transmission. There is also coverage of new approaches such as: super-resolution methods, non-local processing, and directional transforms. Multidimensional Signal, Image, and Video Processing and Coding also has on-line support that contains many short MATLAB programs that complement examples and exercises on multidimensional signal, image, and video processing. There are numerous short video clips showing applications in video processing and coding, plus a copy of the vidview video player for playing .yuv video files on a Windows PC and an illustration of the effect of packet loss on H.264/AVC coded bitstreams. New to this edition: - New appendices on random processes, information theory - New coverage of image analysis – edge detection, linking, clustering, and segmentation - Expanded coverage on image sensing and perception, including color spaces - Now summarizes the new MPEG coding standards: scalable video coding (SVC) and multiview video coding (MVC), in addition to coverage of H.264/AVC - Updated video processing material including new example on scalable video coding and more material on object- and region-based video coding - More on video coding for networks including practical network coding (PNC), highlighting the significant advantages of PNC for both video downloading and streaming - New coverage of super-resolution methods for image and video - Only R&D level tutorial that gives an integrated treatment of image and video processing - topics that are interconnected - New chapters on introductory random processes, information theory, and image enhancement and analysis - Coverage and discussion of the latest standards in video coding: H.264/AVC and the new scalable video standard (SVC)
Author: Roumen Kountchev
Publisher: Springer Nature
ISBN: 9813346760
Category : Technology & Engineering
Languages : en
Pages : 268
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Book Description
This book is a collection of papers presented at the International Workshop on New Approaches for Multidimensional Signal Processing (NAMSP 2020), held at Technical University of Sofia, Sofia, Bulgaria, during 09–11 July 2020. The book covers research papers in the field of N-dimensional multicomponent image processing, multidimensional image representation and super-resolution, 3D image processing and reconstruction, MD computer vision systems, multidimensional multimedia systems, neural networks for MD image processing, data-based MD image retrieval and knowledge data mining, watermarking, hiding and encryption of MD images, MD image processing in robot systems, tensor-based data processing, 3D and multi-view visualization, forensic analysis systems for MD images and many more.
Author: Eric Dubois
Publisher: John Wiley & Sons
ISBN: 1119111757
Category : Technology & Engineering
Languages : en
Pages : 367
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Book Description
An Innovative Approach to Multidimensional Signals and Systems Theory for Image and Video Processing In this volume, Eric Dubois further develops the theory of multi-D signal processing wherein input and output are vector-value signals. With this framework, he introduces the reader to crucial concepts in signal processing such as continuous- and discrete-domain signals and systems, discrete-domain periodic signals, sampling and reconstruction, light and color, random field models, image representation and more. While most treatments use normalized representations for non-rectangular sampling, this approach obscures much of the geometrical and scale information of the signal. In contrast, Dr. Dubois uses actual units of space-time and frequency. Basis-independent representations appear as much as possible, and the basis is introduced where needed to perform calculations or implementations. Thus, lattice theory is developed from the beginning and rectangular sampling is treated as a special case. This is especially significant in the treatment of color and color image processing and for discrete transform representations based on symmetry groups, including fast computational algorithms. Other features include: An entire chapter on lattices, giving the reader a thorough grounding in the use of lattices in signal processing Extensive treatment of lattices as used to describe discrete-domain signals and signal periodicities Chapters on sampling and reconstruction, random field models, symmetry invariant signals and systems and multidimensional Fourier transformation properties Supplemented throughout with MATLAB examples and accompanying downloadable source code Graduate and doctoral students as well as senior undergraduates and professionals working in signal processing or video/image processing and imaging will appreciate this fresh approach to multidimensional signals and systems theory, both as a thorough introduction to the subject and as inspiration for future research.
Author: Krzysztof Galkowski
Publisher: CRC Press
ISBN: 9780203303405
Category : Technology & Engineering
Languages : en
Pages : 284
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Book Description
Although research on general multidimensional systems theory has been developing rapidly in recent years, this is the first research text to appear on the subject since the early 1980s. The text describes the current state of the art nD systems and sets out a number of open problems, and gives several different perspectives on the subject. It presents a number of different solutions to major theoretical problems as well as some interesting practical results. The book comprises of a selection of plenary and other lectures given at The First International Workshop on Multidimensional (nD) Systems (NDS-98) held in 1998 in Poland, and is written by leading world specialists in the field.
Author: Roumen Kountchev
Publisher: Springer Nature
ISBN: 9811685584
Category : Technology & Engineering
Languages : en
Pages : 330
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Book Description
This book comprises a collection of papers presented at the International Workshop on New Approaches for Multidimensional Signal Processing (NAMSP 2021), held at Technical University of Sofia, Sofia, Bulgaria, during 08–10 July 2021. The book covers research papers in the field of N-dimensional multicomponent image processing, multidimensional image representation and super-resolution, 3D image processing and reconstruction, MD computer vision systems, multidimensional multimedia systems, neural networks for MD image processing, data-based MD image retrieval and knowledge data mining, watermarking, hiding and encryption of MD images, MD image processing in robot systems, tensor-based data processing, 3D and multi-view visualization, forensic analysis systems for MD images and many more.
Author: Roumen Kountchev
Publisher: Springer Nature
ISBN: 9819701090
Category :
Languages : en
Pages : 336
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Book Description
Author: Raúl San José Estépar
Publisher: Presses univ. de Louvain
ISBN: 2874631019
Category : Computers
Languages : en
Pages : 320
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Book Description
Feature extraction and, particularly, orientation estimation of multidimensional images is of paramount importance for the Image Processing and Computer Vision communities. This dissertation focuses on this topic; specifically, we deal with the problem of local structure tensor (LST) estimation, as a mean of characterizing the local behavior of a multidimensional signal. The LST can be seen as a measure of the uncertainty of a multidimensional signal with respect to a given orientation. LST estimation can be achieved by estimating the local energy of a signal in different orientations. Then, the LST is computed as a linear combination of the local energy for each orientation with a tensor basis whose elements are calculated for each orientation. This kind of methods for the estimation of the LST are based on quadrature filters to obtain the local energy of the signal. While the LST based on quadrature filters is well defined for signals that vary locally only in one orientation (simple signals), the estimation method fails with complex signals, i.e. signals that consist of several differently-oriented simple signals. In this dissertation, an analytical study of the distortions of the tensor eigenvalues due to such complex neighborhoods is carried out. From this analytical study, two constructive methods are proposed for the estimation of the LST. The first method is based on a maximum likelihood estimation of the quadrature filter outputs. The second method uses a measure of phase consistency based on generalized quadrature filters which are formally derived from an extension of the analytic signal to multidimensional signals known as the monogenic signal. The interpretation of a multidimensional image as a function graph, i.e. a Riemannian manifold, instead of just intensity variations on the Euclidean space, has important implications that are exploited in this dissertation. Image processing tasks can then be performed by solving partial differential equations on the Riemannian manifold. In this dissertation, Riemannian geometry is used to study the evolution of fronts under mean curvature flow on a Riemannian manifold using a level set framework. For our purposes, the Riemannian manifold is defined by the induced metric of the image that is related to the LST. The Riemannian mean curvature flow is the theoretical basis for the definition of a level set segmentation method. The methods proposed in this dissertation are applied to two medical image applications. The first consists in a freehand 3D ultrasound reconstruction technique that uses the LST to perform an adaptive interpolation based on normalized convolution. Our results show that our method outperforms traditional technique for this interpolation problem. The second application uses the level set method based on Riemannian mean curvature flow to segment anatomical structures in dataset from magnetic resonance imaging (MRI), computed tomography (CT) and ultrasound (US). This novel method reveals as a feasible approach to medical image segmentation.
Author: Yingbo Hua
Publisher: CRC Press
ISBN: 9780824747527
Category : Technology & Engineering
Languages : en
Pages : 488
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Book Description
High-Resolution and Robust Signal Processing describes key methodological and theoretical advances achieved in this domain over the last twenty years, placing emphasis on modern developments and recent research pursuits. Applications-grounded, this sophisticated resource links theoretical background with high-resolution methods used in wireless communications, brain signal analysis, and space-time radar signal processing. Chapter extras include theorem proofs, derivations, and computational shortcuts, as well as open problems, numerical measurement, and performance examples, and simulation results Sixteen illustrious field leaders invest High-Resolution and Robust Signal Processing with: in-depth reviews of parametric high-resolution estimation and detection techniques; robust array processing solutions for adaptive beam forming and high-resolution direction finding; Parafac techniques for high-resolution array processing and specific areas of application; high-resolution nonparametric methods and implementation tactics for spectral analysis; multidimensional high-resolution data models and discussion of R-D unitary ESPRIT with colored noise; multidimensional high-resolution parameter estimation techniques applicable to channel sounding; estimation procedures for high-resolution space-time radar signal processing using 2-D or 1-D/1-D models; and models and methods for EEG/MEG space-time dipole source estimation and sensory array design.
Author: Dan E. Dudgeon
Publisher: Prentice Hall
ISBN:
Category : Technology & Engineering
Languages : en
Pages : 424
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Book Description
Multidimensional signals and systems. Discrete fourier analysis of multidimensional signals. Design and implementation of two-dimensional fir filters. Multidimensional recursive systems. Design and implementation of two-dimensional iir filters. Processing signals carried by propagation waves. Inverse problems.
