Torrent details for "Dwivedi S. Structural Pattern Recognition using Graph Matching..…" Log in to bookmark
Controls:
×
Report Torrent
Please select a reason for reporting this torrent:
Your report will be reviewed by our moderation team.
×
Report Information
Loading report information...
This torrent has been reported 0 times.
Report Summary:
| User | Reason | Date |
|---|
Failed to load report information.
×
Success
Your report has been submitted successfully.
Checked by:
Category:
Language:
None
Total Size:
11.9 MB
Info Hash:
06D68845D1BB035C077673D627A5627AE4330BC6
Added By:
Added:
Oct. 2, 2025, 10:27 a.m.
Stats:
|
(Last updated: Oct. 2, 2025, 10:29 a.m.)
| File | Size |
|---|---|
| Dwivedi S. Structural Pattern Recognition using Graph Matching...Algorithms 2026.pdf | 11.9 MB |
Name
DL
Uploader
Size
S/L
Added
-
15.2 MB
[10
/
11]
2025-10-09
| Uploaded by andryold1 | Size 15.2 MB | Health [ 10 /11 ] | Added 2025-10-09 |
NOTE
SOURCE: Dwivedi S. Structural Pattern Recognition using Graph Matching...Algorithms 2026
-----------------------------------------------------------------------------------
COVER
-----------------------------------------------------------------------------------
MEDIAINFO
Textbook in PDF format 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 (AI), 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) Graphs permeate virtually every facet of Computer Science. For example, within operating systems, it plays a crucial role in characterizing resource allocation. Nodes denote processes and resources, while edges represent allocations. An edge extending from a resource to a process indicates allocation, whereas an edge from a process to a resource signifies a request for the corresponding resource. This model is fundamental for the efficient management of resources in multitasking environments. In the domain of computer networking, graphs are instrumental in selecting the shortest path for routing data packets across communication networks. This application is essential for ensuring optimal data transmission, minimizing latency, and maximizing network efficiency in the age of global connectivity and data-driven technologies. Within the area of software engineering, control flow graphs provide valuable insights into program complexity. These graphs allow developers to visually map the flow of control within a program, enabling the identification of potential areas for optimization and refinement. They also serve as a means to understand the dependencies and associations between different components of a software project, assisting in the design and maintenance of robust and scalable software systems. Graphs provide a natural and intuitive framework for representing complex relationships among data points, making them indispensable tools for pattern identification across a wide range of domains, such as image analysis, bioinformatics, social network analysis, and beyond. The objective of this book is to study the representational power of graph and apply it to structural pattern recognition and graph matching. Bridging the gap between theory and practical applications, this book aims to explore the exciting world of structural pattern recognition and graph matching techniques
×