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Author : Indriyati Atmosukarto
Publisher :
ISBN 13 :
Total Pages : 121 pages
Book Rating : 4.:/5 (679 download)
Download or read book 3D Shape Analysis for Quantification, Classification, and Retrieval written by Indriyati Atmosukarto and published by . This book was released on 2010 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Hamid Laga
Publisher : John Wiley & Sons
ISBN 13 : 111940519X
Total Pages : 374 pages
Book Rating : 4.1/5 (194 download)
Download or read book 3D Shape Analysis written by Hamid Laga and published by John Wiley & Sons. This book was released on 2018-12-14 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: An in-depth description of the state-of-the-art of 3D shape analysis techniques and their applications This book discusses the different topics that come under the title of "3D shape analysis". It covers the theoretical foundations and the major solutions that have been presented in the literature. It also establishes links between solutions proposed by different communities that studied 3D shape, such as mathematics and statistics, medical imaging, computer vision, and computer graphics. The first part of 3D Shape Analysis: Fundamentals, Theory, and Applications provides a review of the background concepts such as methods for the acquisition and representation of 3D geometries, and the fundamentals of geometry and topology. It specifically covers stereo matching, structured light, and intrinsic vs. extrinsic properties of shape. Parts 2 and 3 present a range of mathematical and algorithmic tools (which are used for e.g., global descriptors, keypoint detectors, local feature descriptors, and algorithms) that are commonly used for the detection, registration, recognition, classification, and retrieval of 3D objects. Both also place strong emphasis on recent techniques motivated by the spread of commodity devices for 3D acquisition. Part 4 demonstrates the use of these techniques in a selection of 3D shape analysis applications. It covers 3D face recognition, object recognition in 3D scenes, and 3D shape retrieval. It also discusses examples of semantic applications and cross domain 3D retrieval, i.e. how to retrieve 3D models using various types of modalities, e.g. sketches and/or images. The book concludes with a summary of the main ideas and discussions of the future trends. 3D Shape Analysis: Fundamentals, Theory, and Applications is an excellent reference for graduate students, researchers, and professionals in different fields of mathematics, computer science, and engineering. It is also ideal for courses in computer vision and computer graphics, as well as for those seeking 3D industrial/commercial solutions.
Author : Shaojun Liu
Publisher :
ISBN 13 :
Total Pages : 220 pages
Book Rating : 4.:/5 (192 download)
Download or read book 3D Shape Analysis written by Shaojun Liu and published by . This book was released on 2007 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Hamid Laga
Publisher : John Wiley & Sons
ISBN 13 : 1119405106
Total Pages : 368 pages
Book Rating : 4.1/5 (194 download)
Download or read book 3D Shape Analysis written by Hamid Laga and published by John Wiley & Sons. This book was released on 2019-01-07 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: An in-depth description of the state-of-the-art of 3D shape analysis techniques and their applications This book discusses the different topics that come under the title of "3D shape analysis". It covers the theoretical foundations and the major solutions that have been presented in the literature. It also establishes links between solutions proposed by different communities that studied 3D shape, such as mathematics and statistics, medical imaging, computer vision, and computer graphics. The first part of 3D Shape Analysis: Fundamentals, Theory, and Applications provides a review of the background concepts such as methods for the acquisition and representation of 3D geometries, and the fundamentals of geometry and topology. It specifically covers stereo matching, structured light, and intrinsic vs. extrinsic properties of shape. Parts 2 and 3 present a range of mathematical and algorithmic tools (which are used for e.g., global descriptors, keypoint detectors, local feature descriptors, and algorithms) that are commonly used for the detection, registration, recognition, classification, and retrieval of 3D objects. Both also place strong emphasis on recent techniques motivated by the spread of commodity devices for 3D acquisition. Part 4 demonstrates the use of these techniques in a selection of 3D shape analysis applications. It covers 3D face recognition, object recognition in 3D scenes, and 3D shape retrieval. It also discusses examples of semantic applications and cross domain 3D retrieval, i.e. how to retrieve 3D models using various types of modalities, e.g. sketches and/or images. The book concludes with a summary of the main ideas and discussions of the future trends. 3D Shape Analysis: Fundamentals, Theory, and Applications is an excellent reference for graduate students, researchers, and professionals in different fields of mathematics, computer science, and engineering. It is also ideal for courses in computer vision and computer graphics, as well as for those seeking 3D industrial/commercial solutions.
Author : Hedi Tabia
Publisher :
ISBN 13 :
Total Pages : 146 pages
Book Rating : 4.:/5 (81 download)
Download or read book Contributions to 3D-shape Matching, Retrieval and Classification written by Hedi Tabia and published by . This book was released on 2011 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: Three dimensional object representations have become an integral part of modern computer graphic applications such as computer-aided design, game development and audio-visual production. At the Meanwhile, the 3D data has also become extremely common in fields such as computer vision, computation geometry, molecular biology and medicine. This is due to the rapid evolution of graphics hardware and software development, particularly the availability of low cost 3D scanners which has greatly facilitated 3D model acquisition, creation and manipulation. Content-based search is a necessary solution for structuring, managing these multimedia data, and browsing within these data collections. In this context, we are looking for a system that can automatically retrieve the 3D-models visually similar to a requested 3D-object. Existing solutions for 3D-shape retrieval and classification suffer from high variability towards shape-preserving transformations like affine or isometric transformations (non-rigid transformations). In this context, the aim of my research is to develop a system that can automatically retrieve quickly and with precision 3D models visually similar to a 3D-object query. The system has to be robust to non-rigid transformation that a shape can undergo.During my PhD thesis:We have developed a novel approach to match 3D objects in the presence of nonrigid transformation and partially similar models. We have proposed to use a new representation of 3D-surfaces using 3D curves extracted around feature points. Tools from shape analysis of curves are applied to analyze and to compare curves of two 3D-surfaces. We have used the belief functions, as fusion technique, to define a global distance between 3D-objects. We have also experimented this technique in the retrieval and classification tasks. We have proposed the use of Bag of Feature techniques in 3D-object retrieval and classification.
Author : Nick Pears
Publisher : Springer Science & Business Media
ISBN 13 : 144714063X
Total Pages : 506 pages
Book Rating : 4.4/5 (471 download)
Download or read book 3D Imaging, Analysis and Applications written by Nick Pears and published by Springer Science & Business Media. This book was released on 2012-05-22 with total page 506 pages. Available in PDF, EPUB and Kindle. Book excerpt: 3D Imaging, Analysis and Applications brings together core topics, both in terms of well-established fundamental techniques and the most promising recent techniques in the exciting field of 3D imaging and analysis. Many similar techniques are being used in a variety of subject areas and applications and the authors attempt to unify a range of related ideas. With contributions from high profile researchers and practitioners, the material presented is informative and authoritative and represents mainstream work and opinions within the community. Composed of three sections, the first examines 3D imaging and shape representation, the second, 3D shape analysis and processing, and the last section covers 3D imaging applications. Although 3D Imaging, Analysis and Applications is primarily a graduate text, aimed at masters-level and doctoral-level research students, much material is accessible to final-year undergraduate students. It will also serve as a reference text for professional academics, people working in commercial research and development labs and industrial practitioners.
Author : Silvia Biasotti
Publisher : Springer Nature
ISBN 13 : 303179558X
Total Pages : 124 pages
Book Rating : 4.0/5 (317 download)
Download or read book Mathematical Tools for Shape Analysis and Description written by Silvia Biasotti and published by Springer Nature. This book was released on 2022-06-01 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a guide for researchers and practitioners to the new frontiers of 3D shape analysis and the complex mathematical tools most methods rely on. The target reader includes students, researchers and professionals with an undergraduate mathematics background, who wish to understand the mathematics behind shape analysis. The authors begin with a quick review of basic concepts in geometry, topology, differential geometry, and proceed to advanced notions of algebraic topology, always keeping an eye on the application of the theory, through examples of shape analysis methods such as 3D segmentation, correspondence, and retrieval. A number of research solutions in the field come from advances in pure and applied mathematics, as well as from the re-reading of classical theories and their adaptation to the discrete setting. In a world where disciplines (fortunately) have blurred boundaries, the authors believe that this guide will help to bridge the distance between theory and practice. Table of Contents: Acknowledgments / Figure Credits / About this Book / 3D Shape Analysis in a Nutshell / Geometry, Topology, and Shape Representation / Differential Geometry and Shape Analysis / Spectral Methods for Shape Analysis / Maps and Distances between Spaces / Algebraic Topology and Topology Invariants / Differential Topology and Shape Analysis / Reeb Graphs / Morse and Morse-Smale Complexes / Topological Persistence / Beyond Geometry and Topology / Resources / Bibliography / Authors' Biographies
Author : Luciano da Fontoura Costa
Publisher : CRC Press
ISBN 13 : 9781420037555
Total Pages : 688 pages
Book Rating : 4.0/5 (375 download)
Download or read book Shape Analysis and Classification written by Luciano da Fontoura Costa and published by CRC Press. This book was released on 2010-12-12 with total page 688 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in shape analysis impact a wide range of disciplines, from mathematics and engineering to medicine, archeology, and art. Anyone just entering the field, however, may find the few existing books on shape analysis too specific or advanced, and for students interested in the specific problem of shape recognition and characterization, traditio
Author : Luciano da Fona Costa
Publisher : CRC Press
ISBN 13 : 0849379407
Total Pages : 693 pages
Book Rating : 4.8/5 (493 download)
Download or read book Shape Classification and Analysis written by Luciano da Fona Costa and published by CRC Press. This book was released on 2018-10-03 with total page 693 pages. Available in PDF, EPUB and Kindle. Book excerpt: Because the properties of objects are largely determined by their geometric features, shape analysis and classification are essential to almost every applied scientific and technological area. A detailed understanding of the geometrical features of real-world entities (e.g., molecules, organs, materials and components) can provide important clues about their origin and function. When properly and carefully applied, shape analysis offers an exceedingly rich potential to yield useful applications in diverse areas ranging from material sciences to biology and neuroscience. Get Access to the Authors’ Own Cutting-Edge Open-Source Software Projects—and Then Actually Contribute to Them Yourself! The authors of Shape Analysis and Classification: Theory and Practice, Second Edition have improved the bestselling first edition by updating the tremendous progress in the field. This exceptionally accessible book presents the most advanced imaging techniques used for analyzing general biological shapes, such as those of cells, tissues, organs, and organisms. It implements numerous corrections and improvements—many of which were suggested by readers of the first edition—to optimize understanding and create what can truly be called an interactive learning experience. New Material in This Second Edition Addresses Graph and complex networks Dimensionality reduction Structural pattern recognition Shape representation using graphs Graphically reformulated, this edition updates equations, figures, and references, as well as slides that will be useful in related courses and general discussion. Like the popular first edition, this text is applicable to many fields and certain to become a favored addition to any library. Visit http://www.vision.ime.usp.br/~cesar/shape/ for Useful Software, Databases, and Videos
Author : Hamid Laga
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (884 download)
Download or read book 3D Shape Classification and Retrieval Using Heterogenous Features and Supervised Learning written by Hamid Laga and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We proposed in this chapter a new framework for 3D model retrieval based on an off-line learning of the most salient features of the shapes. By using a boosting approach we are able to use a large set of features, which can be heterogeneous, in order to capture the high-level semantic concepts of different shape classes. The retrieval process is a combination of classification and intra-class search. The experimental results showed that (1) the boosted descriptors outperform their non-boosted counter part, and (2) an efficient combination of descriptors of different types improves significantly the retrieval performance.
Author : Masoumi Majid
Publisher :
ISBN 13 :
Total Pages : 94 pages
Book Rating : 4.:/5 (113 download)
Download or read book Feature Encoding of Spectral Descriptors for 3D Shape Recognition written by Masoumi Majid and published by . This book was released on 2017 with total page 94 pages. Available in PDF, EPUB and Kindle. Book excerpt: Feature descriptors have become a ubiquitous tool in shape analysis. Features can be extracted and subsequently used to design discriminative signatures for solving a variety of 3D shape analysis problems. In particular, shape classification and retrieval are intriguing and challenging problems that lie at the crossroads of computer vision, geometry processing, machine learning and medical imaging.In this thesis, we propose spectral graph wavelet approaches for the classification and retrieval of deformable 3D shapes. First, we review the recent shape descriptors based on the spectral decomposition of the Laplace-Beltrami operator, which provides a rich set of eigenbases that are invariant to intrinsic isometries. We then provide a detailed overview of spectral graph wavelets. In an effort to capture both local and global characteristics of a 3D shape, we propose a three-step feature description framework. Local descriptors are first extracted via the spectral graph wavelet transform having the Mexican hat wavelet as a generating kernel. Then, mid-level features are obtained by embedding local descriptors into the visual vocabulary space using the soft-assignment coding step of the bag-of-features model. A global descriptor is subsequently constructed by aggregating mid-level features weighted by a geodesic exponential kernel, resulting in a matrix representation that describes the frequency of appearance of nearby codewords in the vocabulary. In order to analyze the performance of the proposed algorithms on 3D shape classification, support vector machines and deep belief networks are applied to mid-level features. To assess the performance of the proposed approach for nonrigid 3D shape retrieval, we compare the global descriptor of a query to the global descriptors of the rest of shapes in the dataset using a dissimilarity measure and find the closest shape. Experimental results on three standard 3D shape benchmarks demonstrate the effectiveness of the proposed classification and retrieval approaches in comparison with state-of-the-art methods.
Author : Ian H. Jermyn
Publisher : Morgan & Claypool Publishers
ISBN 13 : 1681730286
Total Pages : 187 pages
Book Rating : 4.6/5 (817 download)
Download or read book Elastic Shape Analysis of Three-Dimensional Objects written by Ian H. Jermyn and published by Morgan & Claypool Publishers. This book was released on 2017-09-15 with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical analysis of shapes of 3D objects is an important problem with a wide range of applications. This analysis is difficult for many reasons, including the fact that objects differ in both geometry and topology. In this manuscript, we narrow the problem by focusing on objects with fixed topology, say objects that are diffeomorphic to unit spheres, and develop tools for analyzing their geometries. The main challenges in this problem are to register points across objects and to perform analysis while being invariant to certain shape-preserving transformations. We develop a comprehensive framework for analyzing shapes of spherical objects, i.e., objects that are embeddings of a unit sphere in R, including tools for: quantifying shape differences, optimally deforming shapes into each other, summarizing shape samples, extracting principal modes of shape variability, and modeling shape variability associated with populations. An important strength of this framework is that it is elastic: it performs alignment, registration, and comparison in a single unified framework, while being invariant to shape-preserving transformations. The approach is essentially Riemannian in the following sense. We specify natural mathematical representations of surfaces of interest, and impose Riemannian metrics that are invariant to the actions of the shape-preserving transformations. In particular, they are invariant to reparameterizations of surfaces. While these metrics are too complicated to allow broad usage in practical applications, we introduce a novel representation, termed square-root normal fields (SRNFs), that transform a particular invariant elastic metric into the standard L2 metric. As a result, one can use standard techniques from functional data analysis for registering, comparing, and summarizing shapes. Specifically, this results in: pairwise registration of surfaces; computation of geodesic paths encoding optimal deformations; computation of Karcher means and covariances under the shape metric; tangent Principal Component Analysis (PCA) and extraction of dominant modes of variability; and finally, modeling of shape variability using wrapped normal densities. These ideas are demonstrated using two case studies: the analysis of surfaces denoting human bodies in terms of shape and pose variability; and the clustering and classification of the shapes of subcortical brain structures for use in medical diagnosis. This book develops these ideas without assuming advanced knowledge in differential geometry and statistics. We summarize some basic tools from differential geometry in the appendices, and introduce additional concepts and terminology as needed in the individual chapters.
Author : Maytham H. Safar
Publisher : Springer Science & Business Media
ISBN 13 : 1461503493
Total Pages : 146 pages
Book Rating : 4.4/5 (615 download)
Download or read book Shape Analysis and Retrieval of Multimedia Objects written by Maytham H. Safar and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: Shape Analysis and Retrieval of Multimedia Objects provides a comprehensive survey of the most advanced and powerful shape retrieval techniques used in practice today. In addition, this monograph addresses key methodological issues for evaluation of the shape retrieval methods. Shape Analysis and Retrieval of Multimedia Objects is designed to meet the needs of practitioners and researchers in industry, and graduate-level students in Computer Science.
Author : Jean-Luc Mari
Publisher : John Wiley & Sons
ISBN 13 : 1786300419
Total Pages : 194 pages
Book Rating : 4.7/5 (863 download)
Download or read book Geometric and Topological Mesh Feature Extraction for 3D Shape Analysis written by Jean-Luc Mari and published by John Wiley & Sons. This book was released on 2020-01-02 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: Three-dimensional surface meshes are the most common discrete representation of the exterior of a virtual shape. Extracting relevant geometric or topological features from them can simplify the way objects are looked at, help with their recognition, and facilitate description and categorization according to specific criteria. This book adopts the point of view of discrete mathematics, the aim of which is to propose discrete counterparts to concepts mathematically defined in continuous terms. It explains how standard geometric and topological notions of surfaces can be calculated and computed on a 3D surface mesh, as well as their use for shape analysis. Several applications are also detailed, demonstrating that each of them requires specific adjustments to fit with generic approaches. The book is intended not only for students, researchers and engineers in computer science and shape analysis, but also numerical geologists, anthropologists, biologists and other scientists looking for practical solutions to their shape analysis, understanding or recognition problems.
Author : Hayit Greenspan
Publisher : Springer
ISBN 13 : 3642366783
Total Pages : 153 pages
Book Rating : 4.6/5 (423 download)
Download or read book Medical Content-Based Retrieval for Clinical Decision Support written by Hayit Greenspan and published by Springer. This book was released on 2013-02-20 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Third MICCAI Workshop on Medical Content-Based Retrieval for Clinical Decision Support, MCBR-CBS 2012, held in Nice, France, in October 2012. The 10 revised full papers presented together with 2 invited talks were carefully reviewed and selected from 15 submissions. The papers are divided on several topics on image analysis of visual or multimodal medical data (X-ray, MRI, CT, echo videos, time series data), machine learning of disease correlations in visual or multimodal data, algorithms for indexing and retrieval of data from visual or multimodal medical databases, disease model-building and clinical decision support systems based on visual or multimodal analysis, algorithms for medical image retrieval or classification, systems of retrieval or classification using the ImageCLEF collection.
Author : Anis Kacem
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (13 download)
Download or read book Geometric Approaches for 3D Shape Denoising and Retrieval written by Anis Kacem and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Lorenzo Luciano
Publisher :
ISBN 13 :
Total Pages : 111 pages
Book Rating : 4.:/5 (113 download)
Download or read book Geometric Deep Learned Descriptors for 3D Shape Recognition written by Lorenzo Luciano and published by . This book was released on 2018 with total page 111 pages. Available in PDF, EPUB and Kindle. Book excerpt: The availability of large 3D shape benchmarks has sparked a flurry of research activity in the development of efficient techniques for 3D shape recognition, which is a fundamental problem in a variety of domains such as pattern recognition, computer vision, and geometry processing. A key element in virtually any shape recognition method is to represent a 3D shape by a concise and compact shape descriptor aimed at facilitating the recognition tasks. The recent trend in shape recognition is geared toward using deep neural networks to learn features at various levels of abstraction, and has been driven, in large part, by a combination of affordable computing hardware, open source software, and the availability of large-scale datasets. In this thesis, we propose deep learning approaches to 3D shape classification and retrieval. Our approaches inherit many useful properties from the geodesic distance, most notably the capture of the intrinsic geometric structure of 3D shapes and the invariance to isometric deformations. More specifically, we present an integrated framework for 3D shape classification that extracts discriminative geometric shape descriptors with geodesic moments. Further, we introduce a geometric framework for unsupervised 3D shape retrieval using geodesic moments and stacked sparse autoencoders. The key idea is to learn deep shape representations in an unsupervised manner. Such discriminative shape descriptors can then be used to compute pairwise dissimilarities between shapes in a dataset, and to find the retrieved set of the most relevant shapes to a given shape query. Experimental evaluation on three standard 3D shape benchmarks demonstrate the competitive performance of our approach in comparison with existing techniques. We also introduce a deep similarity network fusion framework for 3D shape classification using a graph convolutional neural network, which is an efficient and scalable deep learning model for graph-structured data. The proposed approach coalesces the geometrical discriminative power of geodesic moments and similarity network fusion in an effort to design a simple, yet discriminative shape descriptor. This geometric shape descriptor is then fed into the graph convolutional neural network to learn a deep feature representation of a 3D shape. We validate our method on ModelNet shape benchmarks, demonstrating that the proposed framework yields significant performance gains compared to state-of-the-art approaches.
