Applied Graph Theory In Computer Vision And Pattern Recognition


Applied Graph Theory In Computer Vision And Pattern Recognition
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Applied Graph Theory In Computer Vision And Pattern Recognition


Applied Graph Theory In Computer Vision And Pattern Recognition
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Author : Abraham Kandel
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-03-12

Applied Graph Theory In Computer Vision And Pattern Recognition written by Abraham Kandel and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-03-12 with Computers categories.


This book presents novel graph-theoretic methods for complex computer vision and pattern recognition tasks. It presents the application of graph theory to low-level processing of digital images, presents graph-theoretic learning algorithms for high-level computer vision and pattern recognition applications, and provides detailed descriptions of several applications of graph-based methods to real-world pattern recognition tasks.



Graph Based Methods In Computer Vision Developments And Applications


Graph Based Methods In Computer Vision Developments And Applications
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Author : Bai, Xiao
language : en
Publisher: IGI Global
Release Date : 2012-07-31

Graph Based Methods In Computer Vision Developments And Applications written by Bai, Xiao and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-07-31 with Computers categories.


Computer vision, the science and technology of machines that see, has been a rapidly developing research area since the mid-1970s. It focuses on the understanding of digital input images in many forms, including video and 3-D range data. Graph-Based Methods in Computer Vision: Developments and Applications presents a sampling of the research issues related to applying graph-based methods in computer vision. These methods have been under-utilized in the past, but use must now be increased because of their ability to naturally and effectively represent image models and data. This publication explores current activity and future applications of this fascinating and ground-breaking topic.



Graph Based Representations In Pattern Recognition


Graph Based Representations In Pattern Recognition
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Author : Xiaoyi Jiang
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-05-10

Graph Based Representations In Pattern Recognition written by Xiaoyi Jiang and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-05-10 with Computers categories.


This book constitutes the refereed proceedings of the 8th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2011, held in Münster, Germany, in May 2011. The 34 revised full papers presented were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on graph-based representation and characterization, graph matching, classification, and querying, graph-based learning, graph-based segmentation, and applications.



Graph Based Representations In Pattern Recognition


Graph Based Representations In Pattern Recognition
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Author : Pasquale Foggia
language : en
Publisher: Springer
Release Date : 2017-05-08

Graph Based Representations In Pattern Recognition written by Pasquale Foggia and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-08 with Computers categories.


This book constitutes the refereed proceedings of the 11th IAPR-TC-15 International Workshop on Graph-Based Representation in Pattern Recognition, GbRPR 2017, held in Anacapri, Italy, in May 2017. The 25 full papers and 2 abstracts of invited papers presented in this volume were carefully reviewed and selected from 31 submissions. The papers discuss research results and applications in the intersection of pattern recognition, image analysis, graph theory, and also the application of graphs to pattern recognition problems in other fields like computational topology, graphic recognition systems and bioinformatics.



Graph Based Representations In Pattern Recognition


Graph Based Representations In Pattern Recognition
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Author : Cheng-Lin Liu
language : en
Publisher: Springer
Release Date : 2015-05-04

Graph Based Representations In Pattern Recognition written by Cheng-Lin Liu and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-05-04 with Computers categories.


This book constitutes the refereed proceedings of the 10th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2015, held in Beijing, China, in May 2015. The 36 papers presented in this volume were carefully reviewed and selected from 53 submissions. The accepted papers cover diverse issues of graph-based methods and applications, with 7 in graph representation, 15 in graph matching, 7 in graph clustering and classification, and 7 in graph-based applications.



Graph Based Representations In Pattern Recognition


Graph Based Representations In Pattern Recognition
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Author : Donatello Conte
language : en
Publisher: Springer
Release Date : 2019-06-10

Graph Based Representations In Pattern Recognition written by Donatello Conte and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-10 with Computers categories.


This book constitutes the refereed proceedings of the 12th IAPR-TC-15 International Workshop on Graph-Based Representation in Pattern Recognition, GbRPR 2019, held in Tours, France, in June 2019. The 22 full papers included in this volume together with an invited talk were carefully reviewed and selected from 28 submissions. The papers discuss research results and applications at the intersection of pattern recognition, image analysis, and graph theory. They cover topics such as graph edit distance, graph matching, machine learning for graph problems, network and graph embedding, spectral graph problems, and parallel algorithms for graph problems.



Graph Based Representations In Pattern Recognition


Graph Based Representations In Pattern Recognition
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Author : Francisco Escolano
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-05-31

Graph Based Representations In Pattern Recognition written by Francisco Escolano and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-05-31 with Computers categories.


This book constitutes the refereed proceedings of the 6th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2007, held in Alicante, Spain in June 2007. The 23 revised full papers and 14 revised poster papers presented were carefully reviewed and selected from 54 submissions. The papers are organized in topical sections on matching, distances and measures, graph-based segmentation and image processing, graph-based clustering, graph representations, pyramids, combinatorial maps and homologies, as well as graph clustering, embedding and learning.



Graph Based Representations In Pattern Recognition


Graph Based Representations In Pattern Recognition
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Author : Andrea Torsello
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-07-09

Graph Based Representations In Pattern Recognition written by Andrea Torsello and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-07-09 with Computers categories.


This book constitutes the refereed proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2009, held in Venice, Italy in May 2009. The 37 revised full papers presented were carefully reviewed and selected from 47 submissions. The papers are organized in topical sections on graph-based representation and recognition, graph matching, graph clustering and classification, pyramids, combinatorial maps, and homologies, as well as graph-based segmentation.



Graph Based Representations In Pattern Recognition


Graph Based Representations In Pattern Recognition
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Author : Mario Vento
language : en
Publisher: Springer Nature
Release Date : 2023-08-23

Graph Based Representations In Pattern Recognition written by Mario Vento and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-23 with Computers categories.


This book constitutes the refereed proceedings of the 13th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2023, which took place in Vietri sul Mare, Italy, in September 2023. The 16 full papers included in this book were carefully reviewed and selected from 18 submissions. They were organized in topical sections on graph kernels and graph algorithms; graph neural networks; and graph-based representations and applications.



Applied Graph Theory In Computer Vision And Pattern Recognition


Applied Graph Theory In Computer Vision And Pattern Recognition
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Author : Abraham Kandel
language : en
Publisher: Springer
Release Date : 2007-04-11

Applied Graph Theory In Computer Vision And Pattern Recognition written by Abraham Kandel and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-04-11 with Technology & Engineering categories.


This book presents novel graph-theoretic methods for complex computer vision and pattern recognition tasks. It presents the application of graph theory to low-level processing of digital images, presents graph-theoretic learning algorithms for high-level computer vision and pattern recognition applications, and provides detailed descriptions of several applications of graph-based methods to real-world pattern recognition tasks.