Graph spectral image processing / / edited by Gene Cheung, Enrico Magli

• Record Nr.: UNINA9910830490103321
• Pubbl/distr/stampa: Hoboken, New Jersey : , : John Wiley & Sons, , [2021]; ©2021
• ISBN: 1-119-85081-9; 1-119-85083-5; 1-119-85082-7
• Descrizione fisica: 1 online resource (325 pages)
• Collana: Sciences. Image. Compression, coding and protection of images and videos
• Disciplina: 621.367
• Soggetti: Image processing; Spectral imaging; Graph theory
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Includes index.
• Nota di contenuto: Cover -- Half-Title Page -- Title Page -- Copyright Page -- Contents -- Introduction to Graph Spectral Image Processing -- I.1. Introduction -- I.2. Graph definition -- I.3. Graph spectrum -- I.4. Graph variation operators -- I.5. Graph signal smoothness priors -- I.6. References -- Part 1. Fundamentals of Graph Signal Processing -- Chapter 1. Graph Spectral Filtering -- 1.1. Introduction -- 1.2. Review: filtering of time-domain signals -- 1.3. Filtering of graph signals -- 1.3.1. Vertex domain filtering -- 1.3.2. Spectral domain filtering -- 1.3.3. Relationship between graph spectral filtering and classical filtering -- 1.4. Edge-preserving smoothing of images as graph spectral filters -- 1.4. Edge-preserving smoothing of images as graph spectral filters -- 1.4.1. Early works -- 1.4.2. Edge-preserving smoothing -- 1.5. Multiple graph filters: graph filter banks -- 1.5.1. Framework -- 1.5.2. Perfect reconstruction condition -- 1.6. Fast computation -- 1.6.1. Subdivision -- 1.6.3. Precomputing GFT -- 1.6.4. Partial eigendecomposition -- 1.6.5. Polynomial approximation -- 1.6.6. Krylov subspace method -- 1.7. Conclusion -- 1.8. References -- ; Chapter 2. Graph Learning -- 2.1. Introduction -- 2.2. Literature review -- 2.2.1. Statistical models -- 2.2.2. Physically motivated models -- 2.3. Graph learning: a signal representation perspective -- 2.3.1. Models based on signal smoothness -- 2.3.2. Models based on spectral filtering of graph signals -- 2.3.3. Models based on causal dependencies on graphs -- 2.3.4. Connections with the broader literature -- 2.4. Applications of graph learning in image processing -- 2.5. Concluding remarks and future directions -- 2.6. References -- Chapter 3. Graph Neural Networks -- 3.1. Introduction -- 3.2. Spectral graph-convolutional layers -- 3.3. Spatial graph-convolutional layers -- 3.4. Concluding remarks.; 3.5. References -- Part 2. Imaging Applications of Graph Signal Processing -- Chapter 4. Graph Spectral Image and Video Compression -- 4.1. Introduction -- 4.1.1. Basics of image and video compression -- 4.1.2. Literature review -- 4.1.3. Outline of the chapter -- 4.2. Graph-based models for image and video signals -- 4.2.1. Graph-based models for residuals of predicted signals -- 4.2.2. DCT/DSTs as GFTs and their relation to 1D models -- 4.2.3. Interpretation of graph weights for predictive transform coding -- 4.3. Graph spectral methods for compression -- 4.3.1. GL-GFT design -- 4.3.2. EA-GFT design -- 4.3.3. Empirical evaluation of GL-GFT and EA-GFT -- 4.4. Conclusion and potential future work -- 4.5. References -- Chapter 5. Graph Spectral 3D Image Compression -- 5.1. Introduction to 3D images -- 5.1.1. 3D image definition -- 5.1.2. Point clouds and meshes -- 5.1.3. Omnidirectional images -- 5.1.4. Light field images -- 5.1.5. Stereo/multi-view images -- 5.2. Graph-based 3D image coding: overview -- 5.3. Graph construction -- 5.3.1. Geometry-based approaches -- 5.3.2. Joint geometry and color-based approaches -- 5.3.3. Separable transforms -- 5.4. Concluding remarks -- 5.5. References -- Chapter 6. Graph Spectral Image Restoration -- 6.1. Introduction -- 6.1.1. A simple image degradation model -- 6.1.2. Restoration with signal priors -- 6.1.3. Restoration via filtering -- 6.1.4. GSP for image restoration -- 6.2. Discrete-domain methods -- 6.2.1. Non-local graph-based transform for depth image denoising -- 6.2.2. Doubly stochastic graph Laplacian -- 6.2.3. Reweighted graph total variation prior -- 6.2.4. Left eigenvectors of random walk graph Laplacian -- 6.2.5. Graph-based image filtering -- 6.3. Continuous-domain methods -- 6.3.1. Continuous-domain analysis of graph Laplacian regularization.; 6.3.2. Low-dimensional manifold model for image restoration -- 6.3.3. LDMM as graph Laplacian regularization -- 6.4. Learning-based methods -- 6.4.1. CNN with GLR -- 6.4.2. CNN with graph wavelet filter -- 6.5. Concluding remarks -- 6.6. References -- Chapter 7. Graph Spectral Point Cloud Processing -- 7.1. Introduction -- 7.2. Graph and graph-signals in point cloud processing -- 7.3. Graph spectral methodologies for point cloud processing -- 7.3.1. Spectral-domain graph filtering for point clouds -- 7.3.2. Nodal-domain graph filtering for point clouds -- 7.3.3. Learning-based graph spectral methods for point clouds -- 7.4. Low-level point cloud processing -- 7.4.1. Point cloud denoising -- 7.4.2. Point cloud resampling -- 7.4.3. Datasets and evaluation metrics -- 7.5. High-level point cloud understanding -- 7.5.1. Data auto-encoding for point clouds -- 7.5.2. Transformation auto-encoding for point clouds -- 7.5.3. Applications of GraphTER in point clouds -- 7.5.4. Datasets and evaluation metrics -- 7.6. Summary and further reading -- 7.7. References -- Chapter 8. Graph Spectral Image Segmentation -- 8.1. Introduction -- 8.2. Pixel membership functions -- 8.2.1. Two-class problems -- 8.2.2. Multiple-class problems -- 8.2.3. Multiple images -- 8.3. Matrix ; properties -- 8.4. Graph cuts -- 8.4.1. The Mumford-Shah model -- 8.4.2. Graph cuts minimization -- 8.5. Summary -- 8.6. References -- Chapter 9. Graph Spectral Image Classification -- 9.1. Formulation of graph-based classification problems -- 9.1.1. Graph spectral classifiers with noiseless labels -- 9.1.2. Graph spectral classifiers with noisy labels -- 9.2. Toward practical graph classifier implementation -- 9.2.1. Graph construction -- 9.2.2. Experimental setup and analysis -- 9.3. Feature learning via deep neural network -- 9.3.1. Deep feature learning for graph construction.; 9.3.2. Iterative graph construction -- 9.3.3. Toward practical implementation of deep feature learning -- 9.3.4. Analysis on iterative graph construction for robust classification -- 9.3.5. Graph spectrum visualization -- 9.3.6. Classification error rate comparison using insufficient training data -- 9.3.7. Classification error rate comparison using sufficient training data with label noise -- 9.4. Conclusion -- 9.5. References -- Chapter 10. Graph Neural Networks for Image Processing -- 10.1. Introduction -- 10.2. Supervised learning problems -- 10.2.1. Point cloud classification -- 10.2.2. Point cloud segmentation -- 10.2.3. Image denoising -- 10.3. Generative models for point clouds -- 10.3.1. Point cloud generation -- 10.3.2. Shape completion -- 10.4. Concluding remarks -- 10.5. References -- List of Authors -- Index -- EULA.
• Sommario/riassunto: Graph spectral image processing is the study of imaging data from a graph frequency perspective. Modern image sensors capture a wide range of visual data including high spatial resolution/high bit-depth 2D images and videos, hyperspectral images, light field images and 3D point clouds. The field of graph signal processing - extending traditional Fourier analysis tools such as transforms and wavelets to handle data on irregular graph kernels - provides new flexible computational tools to analyze and process these varied types of imaging data. Recent methods combine graph signal processing ideas with deep neural network architectures for enhanced performances, with robustness and smaller memory requirements. The book is divided into two parts. The first is centered on the fundamentals of graph signal processing theories, including graph filtering, graph learning and graph neural networks. The second part details several imaging applications using graph signal processing tools, including image and video compression, 3D image compression, image restoration, point cloud processing, image segmentation and image classification, as well as the use of graph neural networks for image processing.

The Tacit Dimension : Architecture Knowledge and Scientific Research / / Lara Schrijver (ed.)

• Record Nr.: UNINA9910473653303321
• Pubbl/distr/stampa: Leuven (belgium) : , : Leuven University Press, , 2021; ©2021
• ISBN: 9789461663801; 9461663803
• Descrizione fisica: 1 online resource (1 online resource 1 volume)
• Soggetti: ARCHITECTURE / Criticism; Research - Philosophy; Knowledge, Theory of; Architecture - Philosophy; Electronic books.
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Cover title.
• Sommario/riassunto: Within architecture, tacit knowledge plays a substantial role both within the design process and its reception. This book explores the tacit dimension of architecture in its aesthetic, material, cultural, design-based, and reflexive understanding of what we build. Much of architecture's knowledge resides beneath the surface, in nonverbal instruments such as drawings and models that articulate the spatial imagination of the design process. Tacit knowledge, described in 1966 by Michael Polanyi as what we "can know but cannot tell", often denotes knowledge that escapes quantifiable dimensions of research. Beginning in the studio, where students are guided into becoming architects, the book follows a path through the tacit knowledge present in models, materials, conceptual structures, and the design process, revealing how the tacit dimension leads to craftsmanship and the situated knowledge of architecture-in-the-world. Awareness of the tacit dimension helps to understand the many facets of the spaces we inhabit, from the ideas of the architect to the more hidden assumptions of our cultures.

Beherrschte Zeit : Lebensorientierung und Zukunftsgestaltung durch Kalenderprognostik zwischen Antike und Neuzeit. Mit einer Edition des Passauer Kalendars (UB/LMB 2° Ms. astron. 1) / Markus Mueller

• Record Nr.: UNINA9911011376703321
• Autore: Mueller Markus
• Pubbl/distr/stampa: Kassel, : Kassel University Press, 2009
• ISBN: 9783899582963; 3899582969
• Descrizione fisica: 1 Online-Ressource (692 Seiten)
• Collana: Schriftenreihe der Universitätsbibliothek Kassel - Landesbibliothek und Murhardsche Bibliothek der Stadt Kassel ; 8
• Soggetti: Hochschulschrift
• Lingua di pubblicazione: Tedesco
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Lizenzpflichtig

IEEE Forty-Second Vehicular Technology, 1992

• Record Nr.: UNINA9910872796203321
• Pubbl/distr/stampa: [Place of publication not identified], : IEEE, 1992
• Descrizione fisica: 1 online resource
• Disciplina: 688.6
• Soggetti: Carriages and carts
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Bibliographic Level Mode of Issuance: Monograph

A Taxonomy for Texture Description and Identification / / by A. Ravishankar Rao

• Record Nr.: UNINA9910962855503321
• Autore: Ravishankar Rao A.
• Pubbl/distr/stampa: New York, NY : , : Springer New York : , : Imprint : Springer, , 1990
• ISBN: 1-4613-9777-4
• Edizione: [1st ed. 1990.]
• Descrizione fisica: 1 online resource (XXIII, 198 p.)
• Collana: Springer Series in Perception Engineering
• Disciplina: 006.6; 006.37
• Soggetti: Computer vision; Computer simulation; Computers; Software engineering; Computer Vision; Computer Modelling; Computer Hardware; Software Engineering
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: "With 79 Illustrations."
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: 1 Introduction -- 1.1 Scope of the book -- 1.2 Importance of texture -- 1.3 Potential applications of this research -- 1.4 Issues in automated process control involving computer vision -- 1.5 A taxonomy for texture -- 1.6 Outline -- 2 Computing oriented texture fields -- 2.1 Introduction -- 2.2 Background -- 2.3 Oriented Texture Fields -- 2.4 Experimental Methods -- 2.5 Experimental Results -- 2.6 Analyzing texture at different scales -- 2.7 Processing of the intrinsic images -- 2.8 Conclusions -- 3 The analysis of oriented textures through phase portraits -- 3.1 Introduction -- 3.2 Background -- 3.3 Geometric theory of differential equations -- 3.4 Experimental Methods -- 3.5 Experimental Results -- 3.6 Experiments with noise addition -- 3.7 A related model from fluid flow analysis -- 3.8 Discussion -- 3.9 Conclusion -- 4 Analyzing strongly ordered textures -- 4.1 Introduction -- 4.2 Extraction of primitives -- 4.3 Extracting structure from primitives -- 4.4 Models for strongly ordered textures -- 4.5 Symbolic descriptions: models from petrography -- 4.6 Frieze groups and wallpaper groups -- 4.7 Implications for computer vision -- 4.8 Summary -- 5 Disordered textures -- 5.1 Statistical measures for disordered textures -- 5.2 Describing disordered textures by means of the fractal dimension -- 5.3 Computing the fractal dimension -- 5.4 Experimental Results -- 5.5 Conclusion -- 6 Compositional textures -- 6.1 Introduction -- 6.2 Primitive textures -- 6.3 A Parametrized symbol set -- 6.4 Three types of composition -- 6.5 Linear combination (transparent overlap) -- 6.6 Functional composition -- 6.7 Opaque overlap -- 6.8 Definition of texture -- 6.9 A complete taxonomy for texture -- 6.10 Implementing the taxonomy -- 6.11 Conclusion -- 7 Conclusion -- 7.1 Summary of results -- 7.2 Contributions -- 7.3 Future Work -- B Region Refinement -- C Preparation of the manuscript -- Permissions.
• Sommario/riassunto: A central issue in computer vision is the problem of signal to symbol transformation. In the case of texture, which is an important visual cue, this problem has hitherto received very little attention. This book presents a solution to the signal to symbol transformation problem for texture. The symbolic de- scription scheme consists of a novel taxonomy for textures, and is based on appropriate mathematical models for different kinds of texture. The taxonomy classifies textures into the broad classes of disordered, strongly ordered, weakly ordered and compositional. Disordered textures are described by statistical mea- sures, strongly ordered textures by the placement of primitives, and weakly ordered textures by an orientation field. Compositional textures are created from these three classes of texture by using certain rules of composition. The unifying theme of this book is to provide standardized symbolic descriptions that serve as a descriptive vocabulary for textures. The algorithms developed in the book have been applied to a wide variety of textured images arising in semiconductor wafer inspection, flow visualization and lumber processing. The taxonomy for texture can serve as a scheme for the identification and description of surface flaws and defects occurring in a wide range of practical applications.