Graph-Based Representations in Pattern Recognition 12th IAPR-TC-15 International Workshop, GbRPR 2019, Tours, France, June 19-21, 2019, Proceedings / [electronic resource] :
edited by Donatello Conte, Jean-Yves Ramel, Pasquale Foggia.
- 1st ed. 2019.
- X, 247 p. 92 illus., 74 illus. in color. online resource.
- Image Processing, Computer Vision, Pattern Recognition, and Graphics, 11510 3004-9954 ; .
- Image Processing, Computer Vision, Pattern Recognition, and Graphics, 11510 .
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. .
9783030200817
10.1007/978-3-030-20081-7 doi
Pattern recognition systems.
Computer science--Mathematics.
Discrete mathematics.
Computer graphics.
Algorithms.
Artificial intelligence--Data processing.
Artificial intelligence.
Automated Pattern Recognition.
Discrete Mathematics in Computer Science.
Computer Graphics.
Algorithms.
Data Science.
Artificial Intelligence.
Q337.5 TK7882.P3
006.4
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. .
9783030200817
10.1007/978-3-030-20081-7 doi
Pattern recognition systems.
Computer science--Mathematics.
Discrete mathematics.
Computer graphics.
Algorithms.
Artificial intelligence--Data processing.
Artificial intelligence.
Automated Pattern Recognition.
Discrete Mathematics in Computer Science.
Computer Graphics.
Algorithms.
Data Science.
Artificial Intelligence.
Q337.5 TK7882.P3
006.4