Multispectral Satellite Image Understanding

This book presents a comprehensive review of image processing methods, for the analysis of land use in residential areas.

Author: Cem Ünsalan

Publisher: Springer Science & Business Media

ISBN: 0857296671

Category: Computers

Page: 186

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This book presents a comprehensive review of image processing methods, for the analysis of land use in residential areas. Combining a theoretical framework with highly practical applications, the book describes a system for the effective detection of single houses and streets in very high resolution. Topics and features: with a Foreword by Prof. Dr. Peter Reinartz of the German Aerospace Center; provides end-of-chapter summaries and review questions; presents a detailed review on remote sensing satellites; examines the multispectral information that can be obtained from satellite images, with a focus on vegetation and shadow-water indices; investigates methods for land-use classification, introducing precise graph theoretical measures over panchromatic images; addresses the problem of detecting residential regions; describes a house and street network-detection subsystem; concludes with a summary of the key ideas covered in the book.
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Multispectral Satellite Image Understanding

This book presents a comprehensive review of image processing methods, for the analysis of land use in residential areas.

Author: Cem Unsalan

Publisher: Springer

ISBN: 085729668X

Category: Computers

Page: 186

View: 359

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This book presents a comprehensive review of image processing methods, for the analysis of land use in residential areas. Combining a theoretical framework with highly practical applications, the book describes a system for the effective detection of single houses and streets in very high resolution. Topics and features: with a Foreword by Prof. Dr. Peter Reinartz of the German Aerospace Center; provides end-of-chapter summaries and review questions; presents a detailed review on remote sensing satellites; examines the multispectral information that can be obtained from satellite images, with a focus on vegetation and shadow-water indices; investigates methods for land-use classification, introducing precise graph theoretical measures over panchromatic images; addresses the problem of detecting residential regions; describes a house and street network-detection subsystem; concludes with a summary of the key ideas covered in the book.
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Multispectral Satellite Image Understanding

Abstract: A problem of major interest to regional planning organizations, disaster relief agencies, and the military is the identification and tracking of land development across large scale regions, and over time.

Author: Cem Ünsalan

Publisher:

ISBN: OCLC:56448631

Category: Computer vision

Page:

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Abstract: A problem of major interest to regional planning organizations, disaster relief agencies, and the military is the identification and tracking of land development across large scale regions, and over time. We develop an autonomous image analysis system to understand land development, especially residential and urban building organizations from satellite images. We introduce a set of measures based on straight lines to assess land development levels in high resolution satellite images. Urban areas exhibit a preponderance of straight line features. Rural areas produce line structures in more random spatial arrangements. We use this observation to perform an initial triage on the image to restrict the attention of subsequent, more computationally intensive analyses. Vegetation indices have been used extensively to estimate the vegetation density from satellite and airborne images for many years. We use these as the multispectral information for classification and house and road extraction. We focus on the normalized difference vegetation index NDVI and introduce a statistical framework to analyze and extend it. Using the established statistical framework, we introduce new a group of shadow-water indices. We then extend our straight line based measures by developing a synergistic approach that combines structural and multispectral information. In particular, the structural features serve as cue regions for multispectral features. After the initial classification of regions, we introduce computationally more expensive but more precise graph theoretical measures over grayscale images to detect residential regions. The graphs are constructed using lines as vertices, while graph edges encode their spatial relationships. We introduce a set of measures based on various properties of the graph. These measures are monotonic with increasing structure (organization) in the image. We present a theoretical basis for the measures. Having detected the residential regions, we introduce a novel system to detect houses and street networks in these. We extensively use the multispectral information and graph theory to extract houses and road networks. We evaluated the performance of each step statistically and obtained very promising results. Especially, detection performances in house and street detection in residential regions is noteworthy. These results indicate the functionality of our satellite image understanding system.
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Satellite Image Analysis Clustering and Classification

Multiresolution satellite images are a set of images acquired at different resolutions. The comparison and analysis of pixel intensities and the statistics of these set of multispectral/ multitemporal/multiresolution images, ...

Author: Surekha Borra

Publisher: Springer

ISBN: 9789811364242

Category: Technology & Engineering

Page: 97

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Thanks to recent advances in sensors, communication and satellite technology, data storage, processing and networking capabilities, satellite image acquisition and mining are now on the rise. In turn, satellite images play a vital role in providing essential geographical information. Highly accurate automatic classification and decision support systems can facilitate the efforts of data analysts, reduce human error, and allow the rapid and rigorous analysis of land use and land cover information. Integrating Machine Learning (ML) technology with the human visual psychometric can help meet geologists’ demands for more efficient and higher-quality classification in real time. This book introduces readers to key concepts, methods and models for satellite image analysis; highlights state-of-the-art classification and clustering techniques; discusses recent developments and remaining challenges; and addresses various applications, making it a valuable asset for engineers, data analysts and researchers in the fields of geographic information systems and remote sensing engineering.
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Remote Sensing Digital Image Analysis

This book seeks to redress that situation.

Author: John A. Richards

Publisher: Springer Science & Business Media

ISBN: 9783662024621

Category: Technology & Engineering

Page: 281

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With the widespread availability of satellite and aircraft remote sensing image data in digital form, and the ready access most remote sensing practitioners have to computing systems for image interpretation, there is a need to draw together the range of digital image processing procedures and methodologies commonly used in this field into a single treatment. It is the intention of this book to provide such a function, at a level meaningful to the non-specialist digital image analyst, but in sufficient detail that algorithm limitations, alternative procedures and current trends can be appreciated. Often the applications specialist in remote sensing wishing to make use of digital processing procedures has had to depend upon either the mathematically detailed treatments of image processing found in the electrical engineering and computer science literature, or the sometimes necessarily superficial treatments given in general texts on remote sensing. This book seeks to redress that situation. Both image enhancement and classification techniques are covered making the material relevant in those applications in which photointerpretation is used for information extraction and in those wherein information is obtained by classification.
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Photogrammetric Image Analysis

Remote Sensing of Environment 8(2), 127–150 (1979) Unsalan, C., Boyer, K.: A system to detect houses and residential street networks in multispectral satellite images. Computer Vision and Image Understanding 98(3), 423–461 (2005) Zhang, ...

Author: Uwe Stilla

Publisher: Springer Science & Business Media

ISBN: 9783642243929

Category: Computers

Page: 309

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This book constitutes the refereed proceedings of the ISPRS Conference on Photogrammetric Image Analysis, held in Munich, Germany, in October 2011. The 25 revised full papers presented were carefully reviewed and selected from 54 submissions. The papers are organized in topical sections on orientation, matching, object detection, 3D reconstruction and DEM, classification, people and tracking, as well as image processing.
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Progress in Pattern Recognition Image Analysis Computer Vision and Applications

Gonzalez, R.C., Woods, R.E.: Digital image processing. Prentice-Hall, Englewood Cliffs (2007) 6. Healey, G., Jain, A.: Retrieving multispectral satellite images using physics-based invariant representations. IEEE Transactions on Pattern ...

Author: Eduardo Bayro Corrochano

Publisher: Springer Science & Business Media

ISBN: 9783642102677

Category: Computers

Page: 1082

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This book constitutes the refereed proceedings of the 14th Iberoamerican Congress on Pattern Recognition, CIARP 2009, held in Guadalajara, Mexico, in November 2009. The 64 revised full papers presented together with 44 posters were carefully reviewed and selected from 187 submissions. The papers are organized in topical sections on image coding, processing and analysis; segmentation, analysis of shape and texture; geometric image processing and analysis; analysis of signal, speech and language; document processing and recognition; feature extraction, clustering and classification; statistical pattern recognition; neural networks for pattern recognition; computer vision; video segmentation and tracking; robot vision; intelligent remote sensing, imagery research and discovery techniques; intelligent computing for remote sensing imagery; as well as intelligent fusion and classification techniques.
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Image Processing Analysis and Machine Vision

of machine vision, medical image analysis being a very promising field because living organisms and organs are naturally ... appear in image understanding – classification-based segmentation of multispectral images (satellite images, ...

Author: Milan Sonka

Publisher: Springer

ISBN: 9781489932167

Category: Computers

Page: 555

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Image Processing, Analysis and Machine Vision represent an exciting part of modern cognitive and computer science. Following an explosion of inter est during the Seventies, the Eighties were characterized by the maturing of the field and the significant growth of active applications; Remote Sensing, Technical Diagnostics, Autonomous Vehicle Guidance and Medical Imaging are the most rapidly developing areas. This progress can be seen in an in creasing number of software and hardware products on the market as well as in a number of digital image processing and machine vision courses offered at universities world-wide. There are many texts available in the areas we cover - most (indeed, all of which we know) are referenced somewhere in this book. The subject suffers, however, from a shortage of texts at the 'elementary' level - that appropriate for undergraduates beginning or completing their studies of the topic, or for Master's students - and the very rapid developments that have taken and are still taking place, which quickly age some of the very good text books produced over the last decade or so. This book reflects the authors' experience in teaching one and two semester undergraduate and graduate courses in Digital Image Processing, Digital Image Analysis, Machine Vision, Pattern Recognition and Intelligent Robotics at their respective institutions.
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Image Analysis and Recognition

Kuan, C.Y., Healey, G.: Retrieving multispectral satellite images using physics-based invariant representations. IEEE Trans. on Pat. Analysis and Mach. Intel. 18, 842–848 (1996) 2. Kuan, C.Y., Healey, G.: Using spatial filtering to ...

Author: Mohamed Kamel

Publisher: Springer

ISBN: 9783642215964

Category: Computers

Page: 409

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The two-volume set LNCS 6753/6754 constitutes the refereed proceedings of the 8th International Conference on Image and Recognition, ICIAR 2011, held in Burnaby, Canada, in June 2011. The 84 revised full papers presented were carefully reviewed and selected from 147 submissions. The papers are organized in topical sections on image and video processing; feature extraction and pattern recognition; computer vision; color, texture, motion and shape; tracking; biomedical image analysis; biometrics; face recognition; image coding, compression and encryption; and applications.
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Image Analysis Classification and Change Detection in Remote Sensing

Multispectral. satellite. images. A multispectral, optical/infrared image such as that shown in Figure 1.1 may be represented as a three-dimensional array of gray-scale values or pixel intensities gk (i, j), 1 ≤ i ≤ c, 1 ≤ j ≤ r, ...

Author: Morton John Canty

Publisher: CRC Press

ISBN: 9780429875359

Category: Technology & Engineering

Page: 508

View: 273

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Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for Python, Fourth Edition, is focused on the development and implementation of statistically motivated, data-driven techniques for digital image analysis of remotely sensed imagery and it features a tight interweaving of statistical and machine learning theory of algorithms with computer codes. It develops statistical methods for the analysis of optical/infrared and synthetic aperture radar (SAR) imagery, including wavelet transformations, kernel methods for nonlinear classification, as well as an introduction to deep learning in the context of feed forward neural networks. New in the Fourth Edition: An in-depth treatment of a recent sequential change detection algorithm for polarimetric SAR image time series. The accompanying software consists of Python (open source) versions of all of the main image analysis algorithms. Presents easy, platform-independent software installation methods (Docker containerization). Utilizes freely accessible imagery via the Google Earth Engine and provides many examples of cloud programming (Google Earth Engine API). Examines deep learning examples including TensorFlow and a sound introduction to neural networks, Based on the success and the reputation of the previous editions and compared to other textbooks in the market, Professor Canty’s fourth edition differs in the depth and sophistication of the material treated as well as in its consistent use of computer codes to illustrate the methods and algorithms discussed. It is self-contained and illustrated with many programming examples, all of which can be conveniently run in a web browser. Each chapter concludes with exercises complementing or extending the material in the text.
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Entropy in Image Analysis

[CrossRef] Giacco, F.; Thiel, C.; Pugliese, L.; Scarpetta, S.; Marinaro, M. Uncertainty analysis for the classification of multispectral satellite images using svms and soms. IEEE Trans. Geosci. Remote Sens. 2010, 48,3769–3779.

Author: Amelia Carolina Sparavigna

Publisher: MDPI

ISBN: 9783039210923

Category: Technology & Engineering

Page: 456

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Image analysis is a fundamental task for extracting information from images acquired across a range of different devices. Since reliable quantitative results are requested, image analysis requires highly sophisticated numerical and analytical methods—particularly for applications in medicine, security, and remote sensing, where the results of the processing may consist of vitally important data. The contributions to this book provide a good overview of the most important demands and solutions concerning this research area. In particular, the reader will find image analysis applied for feature extraction, encryption and decryption of data, color segmentation, and in the support new technologies. In all the contributions, entropy plays a pivotal role.
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Image Analysis

Fig. 3. The reconstructed sunspot image using our method and its corresponding prediction probability image ... The method can be also easily and naturally generalized for multispectral (e.g. colour, multispectral satellite images) or ...

Author: Heikki Kalviainen

Publisher: Springer Science & Business Media

ISBN: 9783540263203

Category: Computers

Page: 1270

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This book constitutes the refereed proceedings of the 14th Scandinavian Conference on Image Analysis, SCIA 2005, held in Joensuu, Finland in June 2005. The 124 papers presented together with 6 invited papers were carefully reviewed and selected from 236 submissions. The papers are organized in topical sections on image segmentation and understanding, color image processing, applications, theory, medical image processing, image compression, digitalization of cultural heritage, computer vision, machine vision, and pattern recognition.
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Multispectral Image Analysis Using the Object Oriented Paradigm

Integrating remote sensing techniques and GIS analysis, Multispectral Image Analysis Using the Object-Oriented Paradigm distills new tools to extract information from remotely sensed data.

Author: Kumar Navulur

Publisher: CRC Press

ISBN: 1420043072

Category: Technology & Engineering

Page: 184

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Bringing a fresh new perspective to remote sensing, object-based image analysis is a paradigm shift from the traditional pixel-based approach. Featuring various practical examples to provide understanding of this new modus operandi, Multispectral Image Analysis Using the Object-Oriented Paradigm reviews the current image analysis methods and demonstrates advantages to improve information extraction from imagery. This reference describes traditional image analysis techniques, introduces object-oriented technology, and discusses the benefits of object-based versus pixel-based classification. It examines the creation of object primitives using image segmentation approaches and the use of various techniques for object classification. The author covers image enhancement methods, how to use ancillary data to constrain image segmentation, and concepts of semantic grouping of objects. He concludes by addressing accuracy assessment approaches. The accompanying two CD-ROMs present sample data that enable the use of different approaches to problem solving. Integrating remote sensing techniques and GIS analysis, Multispectral Image Analysis Using the Object-Oriented Paradigm distills new tools to extract information from remotely sensed data.
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Low latency big data visualisation

[187] C. Ünsalan and K. L. Boyer, “A system to detect houses and residential street networks in multispectral satellite images,” Computer Vision and Image Understanding, vol. 98, no. 3, pp. 423–461, 2005.

Author: Tan Jerome, Nicholas

Publisher: KIT Scientific Publishing

ISBN: 9783731509400

Category: Computers

Page: 242

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Global Land Ice Measurements from Space

The book is the most definitive, comprehensive product of a global glacier remote sensing consortium, Global Land Ice Measurements from Space (GLIMS, http://www.glims.org).

Author: Jeffrey S. Kargel

Publisher: Springer

ISBN: 9783540798187

Category: Technology & Engineering

Page: 876

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An international team of over 150 experts provide up-to-date satellite imaging and quantitative analysis of the state and dynamics of the glaciers around the world, and they provide an in-depth review of analysis methodologies. Includes an e-published supplement. Global Land Ice Measurements from Space - Satellite Multispectral Imaging of Glaciers (GLIMS book for short) is the leading state-of-the-art technical and interpretive presentation of satellite image data and analysis of the changing state of the world's glaciers. The book is the most definitive, comprehensive product of a global glacier remote sensing consortium, Global Land Ice Measurements from Space (GLIMS, http://www.glims.org). With 33 chapters and a companion e-supplement, the world's foremost experts in satellite image analysis of glaciers analyze the current state and recent and possible future changes of glaciers across the globe and interpret these findings for policy planners. Climate change is with us for some time to come, and its impacts are being felt by the world's population. The GLIMS Book, to be released about the same time as the IPCC's 5th Assessment report on global climate warming, buttresses and adds rich details and authority to the global change community's understanding of climate change impacts on the cryosphere. This will be a definitive and technically complete reference for experts and students examining the responses of glaciers to climate change. World experts demonstrate that glaciers are changing in response to the ongoing climatic upheaval in addition to other factors that pertain to the circumstances of individual glaciers. The global mosaic of glacier changes is documented by quantitative analyses and are placed into a perspective of causative factors. Starting with a Foreword, Preface, and Introduction, the GLIMS book gives the rationale for and history of glacier monitoring and satellite data analysis. It includes a comprehensive set of six "how-to" methodology chapters, twenty-five chapters detailing regional glacier state and dynamical changes, and an in-depth summary and interpretation chapter placing the observed glacier changes into a global context of the coupled atmosphere-land-ocean system. An accompanying e-supplement will include oversize imagery and other other highly visual renderings of scientific data.
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Pattern Recognition and Image Analysis

Petrovic [20] has suggested a quality measure based on emphasis to high frequency information in the image. Bovic [26] has provided an Universal approach to image ... Set 3 consists of Multi Spectral Satellite image fusion performance.

Author: Sameer Singh

Publisher: Springer

ISBN: 9783540319993

Category: Computers

Page: 814

View: 651

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This LNCS volume contains the papers presented at the 3rd International Conference on Advances in Pattern Recognition (ICAPR 2005) organized in August, 2005 in the beautiful city of Bath, UK.
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Learning to Understand Remote Sensing Images

With multispectral images, the flood can be analysed in a more straightforward way with simpler pre-processing [14]. In this study, we mainly focus on methods using multispectral satellite images. Numerous methods have been proposed for ...

Author: Qi Wang

Publisher: MDPI

ISBN: 9783038976844

Category: Computers

Page: 426

View: 123

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With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field.
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Scientific and Technical Aerospace Reports

Complex SAR imagery and speckle filtering for ERS - 1 wave mode 04 P0510 N89-13029 Analysis of Seasat SAR sea ... of changing satellite sensor attributes 04 P0515 N89-13059 Efficient classification of multispectral images by a best ...

Author:

Publisher:

ISBN: UIUC:30112075701695

Category: Aeronautics

Page:

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2D and 3D Image Analysis by Moments

wise classification of multispectral and hyperspectral data (see Figure 2.15). Figure 2.15 Multispectral satellite image. The objects are single pixels, the features are their intensities in the individual spectral bands.

Author: Jan Flusser

Publisher: John Wiley & Sons

ISBN: 9781119039365

Category: Technology & Engineering

Page: 560

View: 215

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Presents recent significant and rapid development in the field of 2D and 3D image analysis 2D and 3D Image Analysis by Moments, is a unique compendium of moment-based image analysis which includes traditional methods and also reflects the latest development of the field. The book presents a survey of 2D and 3D moment invariants with respect to similarity and affine spatial transformations and to image blurring and smoothing by various filters. The book comprehensively describes the mathematical background and theorems about the invariants but a large part is also devoted to practical usage of moments. Applications from various fields of computer vision, remote sensing, medical imaging, image retrieval, watermarking, and forensic analysis are demonstrated. Attention is also paid to efficient algorithms of moment computation. Key features: Presents a systematic overview of moment-based features used in 2D and 3D image analysis. Demonstrates invariant properties of moments with respect to various spatial and intensity transformations. Reviews and compares several orthogonal polynomials and respective moments. Describes efficient numerical algorithms for moment computation. It is a "classroom ready" textbook with a self-contained introduction to classifier design. The accompanying website contains around 300 lecture slides, Matlab codes, complete lists of the invariants, test images, and other supplementary material. 2D and 3D Image Analysis by Moments, is ideal for mathematicians, computer scientists, engineers, software developers, and Ph.D students involved in image analysis and recognition. Due to the addition of two introductory chapters on classifier design, the book may also serve as a self-contained textbook for graduate university courses on object recognition.
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Mathematical Methods for Signal and Image Analysis and Representation

This novel formulation allows us to obtain extremely fast adaptive multichannel/multitemporal restoration and it can be easily parallellized as well as generalized for multispectral (e.g. color, multispectral satellite images) or ...

Author: Luc Florack

Publisher: Springer Science & Business Media

ISBN: 9781447123521

Category: Mathematics

Page: 320

View: 195

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Mathematical Methods for Signal and Image Analysis and Representation presents the mathematical methodology for generic image analysis tasks. In the context of this book an image may be any m-dimensional empirical signal living on an n-dimensional smooth manifold (typically, but not necessarily, a subset of spacetime). The existing literature on image methodology is rather scattered and often limited to either a deterministic or a statistical point of view. In contrast, this book brings together these seemingly different points of view in order to stress their conceptual relations and formal analogies. Furthermore, it does not focus on specific applications, although some are detailed for the sake of illustration, but on the methodological frameworks on which such applications are built, making it an ideal companion for those seeking a rigorous methodological basis for specific algorithms as well as for those interested in the fundamental methodology per se. Covering many topics at the forefront of current research, including anisotropic diffusion filtering of tensor fields, this book will be of particular interest to graduate and postgraduate students and researchers in the fields of computer vision, medical imaging and visual perception.
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