3D Shape Analysis for Quantification, Classification, and Retrieval

3D Shape Analysis for Quantification, Classification, and Retrieval PDF Author: Indriyati Atmosukarto
Publisher:
ISBN:
Category : Digital images
Languages : en
Pages : 121

Get Book Here

Book Description

3D Shape Analysis for Quantification, Classification, and Retrieval

3D Shape Analysis for Quantification, Classification, and Retrieval PDF Author: Indriyati Atmosukarto
Publisher:
ISBN:
Category : Digital images
Languages : en
Pages : 121

Get Book Here

Book Description


3D Shape Analysis

3D Shape Analysis PDF Author: Hamid Laga
Publisher: John Wiley & Sons
ISBN: 111940519X
Category : Mathematics
Languages : en
Pages : 374

Get Book Here

Book Description
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.

3D Shape Analysis

3D Shape Analysis PDF Author: Shaojun Liu
Publisher:
ISBN:
Category : Computer vision
Languages : en
Pages : 220

Get Book Here

Book Description


Contributions to 3D-shape Matching, Retrieval and Classification

Contributions to 3D-shape Matching, Retrieval and Classification PDF Author: Hedi Tabia
Publisher:
ISBN:
Category :
Languages : en
Pages : 146

Get Book Here

Book Description
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.

3D Shape Analysis

3D Shape Analysis PDF Author: Hamid Laga
Publisher: John Wiley & Sons
ISBN: 1119405106
Category : Mathematics
Languages : en
Pages : 368

Get Book Here

Book Description
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.

Shape Classification and Analysis

Shape Classification and Analysis PDF Author: Luciano da Fona Costa
Publisher: CRC Press
ISBN: 0849379407
Category : Technology & Engineering
Languages : en
Pages : 693

Get Book Here

Book Description
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

Mathematical Tools for Shape Analysis and Description

Mathematical Tools for Shape Analysis and Description PDF Author: Silvia Biasotti
Publisher: Springer Nature
ISBN: 303179558X
Category : Mathematics
Languages : en
Pages : 124

Get Book Here

Book Description
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

Shape Analysis and Classification

Shape Analysis and Classification PDF Author: Luciano da Fontoura Costa
Publisher: CRC Press
ISBN: 9781420037555
Category : Computers
Languages : en
Pages : 688

Get Book Here

Book Description
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

3D Shape Classification and Retrieval Using Heterogenous Features and Supervised Learning

3D Shape Classification and Retrieval Using Heterogenous Features and Supervised Learning PDF Author: Hamid Laga
Publisher:
ISBN:
Category :
Languages : en
Pages :

Get Book Here

Book Description
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.

Feature Encoding of Spectral Descriptors for 3D Shape Recognition

Feature Encoding of Spectral Descriptors for 3D Shape Recognition PDF Author: Masoumi Majid
Publisher:
ISBN:
Category :
Languages : en
Pages : 94

Get Book Here

Book Description
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.