Structural Pattern Recognition using Graph Matching: Approximate and Error-Tolerant Algorithms - Tapa dura
Sinopsis
This book presents a comprehensive exploration of structural pattern recognition with a clear understanding of graph representation and manipulation. It explains graph matching techniques, unearthing the core principles of graph similarity measures, subgraph isomorphism, and advanced algorithms tailored to various pattern recognition tasks. It bridges the gap between theory and application by providing case studies, hands-on examples, and applications. It is a reference book for academicians, researchers, and students working in the fields of structural pattern recognition, computer vision, artificial intelligence, and data science.
• Begins with the fundamentals of graph theory, graph matching algorithms, and structural pattern recognition concepts and explains the principles, methodologies, and practical implementations
• Presents relevant case studies and hands-on examples across chapters to guide making informed decisions by graph matching
• Discusses various graph-matching algorithms, including exact and approximate methods, geometric methods, spectral techniques, graph kernels, and graph neural networks, including practical examples to illustrate the strengths and limitations of each approach
• Showcases the versatility of graph matching in real-world applications, such as image analysis, biological molecule identification, object recognition, social network clustering, and recommendation systems
• Describes deep learning models for graph matching, including graph convolutional networks (GCNs) and graph neural networks (GNNs)
"Sinopsis" puede pertenecer a otra edición de este libro.
Acerca del autor
Dr. Shri Prakash Dwivedi is an esteemed researcher and academic in the field of Computer Science and Engineering, specializing in pattern recognition, graph matching, and algorithms. He is currently serving as an Assistant Professor in the Department of Information Technology at G.B. Pant University of Agriculture & Technology, Pantnagar, where he has been actively involved in teaching and research. Dr. Dwivedi obtained his Ph.D. in Computer Science and Engineering from the Indian Institute of Technology (BHU), Varanasi, where he worked on graph matching algorithms for structural pattern recognition. Prior to that, he earned his M.E. in Computer Science from the Indian Institute of Science (IISc), Bangalore. Dr. Dwivedi has published extensively in leading journals and conferences, including Pattern Recognition Letters, Springer’s Lecture Notes in Computer Science, Lecture Notes in Electrical Engineering, ACM and IEEE. His work primarily focuses on error-tolerant graph matching, similarity measure, approximate algorithms, and computational methods in structural pattern recognition.
Beyond academia, Dr. Dwivedi has industry experience as a Software Engineer (R&D) at various software companies, where he worked on scientific computing, algorithm design, and software development. He has also contributed to research projects integrating IoT and cloud-based automation systems. A recipient of several accolades, Dr. Dwivedi secured All India Rank 1 in GATE 2006 and has been a holder of the MHRD Scholarship for his academic excellence.
Dr. Ravi Shankar Singh is working as an Associate Professor in the Department of Computer Science and Engineering, Indian Institute of Technology (BHU) Varanasi, India and has an academic background of B.Tech.(CSE), M.Tech.(CSE) and Ph.D.(CSE). He has been a Senior Member of IEEE and ACM. More than 50 research papers have been included in his account. His research interests include high-performance and cloud computing.
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STRUCTURAL PATTERN RECOGNITION USING GRAPH MATCHING APPROXIMATE AND ERROR TOLERANT ALGORITHMS (HB 2025)
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Hardcover. Condición: new. Hardcover. This book presents a comprehensive exploration of structural pattern recognition with a clear understanding of graph representation and manipulation. It explains graph matching techniques, unearthing the core principles of graph similarity measures, subgraph isomorphism, and advanced algorithms tailored to various pattern recognition tasks. It bridges the gap between theory and application by providing case studies, hands-on examples, and applications. It is a reference book for academicians, researchers, and students working in the fields of structural pattern recognition, computer vision, artificial intelligence, and data science. Begins with the fundamentals of graph theory, graph matching algorithms, and structural pattern recognition concepts and explains the principles, methodologies, and practical implementations Presents relevant case studies and hands-on examples across chapters to guide making informed decisions by graph matching Discusses various graph-matching algorithms, including exact and approximate methods, geometric methods, spectral techniques, graph kernels, and graph neural networks, including practical examples to illustrate the strengths and limitations of each approach Showcases the versatility of graph matching in real-world applications, such as image analysis, biological molecule identification, object recognition, social network clustering, and recommendation systems Describes deep learning models for graph matching, including graph convolutional networks (GCNs) and graph neural networks (GNNs) This book presents a comprehensive exploration of structural pattern recognition with a clear understanding of graph representation and manipulation. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9781032850344
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Structural Pattern Recognition Using Graph Matching : Approximate and Error-tolerant Algorithms
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Chapman and Hall/CRC, 2025
ISBN 10: 1032850345
ISBN 13: 9781032850344
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Hardcover. Condición: new. Hardcover. This book presents a comprehensive exploration of structural pattern recognition with a clear understanding of graph representation and manipulation. It explains graph matching techniques, unearthing the core principles of graph similarity measures, subgraph isomorphism, and advanced algorithms tailored to various pattern recognition tasks. It bridges the gap between theory and application by providing case studies, hands-on examples, and applications. It is a reference book for academicians, researchers, and students working in the fields of structural pattern recognition, computer vision, artificial intelligence, and data science. Begins with the fundamentals of graph theory, graph matching algorithms, and structural pattern recognition concepts and explains the principles, methodologies, and practical implementations Presents relevant case studies and hands-on examples across chapters to guide making informed decisions by graph matching Discusses various graph-matching algorithms, including exact and approximate methods, geometric methods, spectral techniques, graph kernels, and graph neural networks, including practical examples to illustrate the strengths and limitations of each approach Showcases the versatility of graph matching in real-world applications, such as image analysis, biological molecule identification, object recognition, social network clustering, and recommendation systems Describes deep learning models for graph matching, including graph convolutional networks (GCNs) and graph neural networks (GNNs) This book presents a comprehensive exploration of structural pattern recognition with a clear understanding of graph representation and manipulation. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Nº de ref. del artículo: 9781032850344
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Structural Pattern Recognition using Graph Matching
Publicado por
Chapman And Hall/CRC Sep 2025, 2025
ISBN 10: 1032850345
ISBN 13: 9781032850344
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents a comprehensive exploration of structural pattern recognition with a clear understanding of graph representation and manipulation. It explains graph matching techniques, unearthing the core principles of graph similarity measures, subgraph isomorphism, and advanced algorithms tailored to various pattern recognition tasks. It bridges the gap between theory and application by providing case studies, hands-on examples, and applications. It is a reference book for academicians, researchers, and students working in the fields of structural pattern recognition, computer vision, artificial intelligence, and data science.- Begins with the fundamentals of graph theory, graph matching algorithms, and structural pattern recognition concepts and explains the principles, methodologies, and practical implementations- Presents relevant case studies and hands-on examples across chapters to guide making informed decisions by graph matching- Discusses various graph-matching algorithms, including exact and approximate methods, geometric methods, spectral techniques, graph kernels, and graph neural networks, including practical examples to illustrate the strengths and limitations of each approach- Showcases the versatility of graph matching in real-world applications, such as image analysis, biological molecule identification, object recognition, social network clustering, and recommendation systems- Describes deep learning models for graph matching, including graph convolutional networks (GCNs) and graph neural networks (GNNs) 250 pp. Englisch. Nº de ref. del artículo: 9781032850344
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