Traffic-Sign Recognition Systems (SpringerBriefs in Computer Science) - Softcover

Buch 9 von 322: SpringerBriefs in Computer Science

Escalera, Sergio

 
9781447122449: Traffic-Sign Recognition Systems (SpringerBriefs in Computer Science)
Softcover
ISBN 10: 1447122445 ISBN 13: 9781447122449
Verlag: Springer, 2011

Inhaltsangabe

This work presents a full generic approach to the detection and recognition of traffic signs. The approach is based on the latest computer vision methods for object detection, and on powerful methods for multiclass classification. The challenge was to robustly detect a set of different sign classes in real time, and to classify each detected sign into a large, extensible set of classes. To address this challenge, several state-of-the-art methods were developed that can be used for different recognition problems. Following an introduction to the problems of traffic sign detection and categorization, the text focuses on the problem of detection, and presents recent developments in this field. The text then surveys a specific methodology for the problem of traffic sign categorization - Error-Correcting Output Codes - and presents several algorithms, performing experimental validation on a mobile mapping application. The work ends with a discussion on future research and continuing challenges.

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Über die Autorin bzw. den Autor

Rubén Ballester is a PhD student in Topological Machine Learning at the Department of Mathematics and Computer Science of the University of Barcelona (UB). He received his bachelor’s degrees in Mathematics and Computer Science from UB in 2021 and completed the Advanced Mathematics and Mathematical Engineering MSc at Universitat Politècnica de Catalunya (UPC) in 2022, achieving the highest master’s degree GPA recognition. He has published articles on the connection between generalizations of neural networks and persistent homology and on the design of neural networks for topological domains. He won the Topological Deep Learning Challenge in the modality of combinatorial complexes, organized within the 2nd Annual Workshop on Topology, Algebra, and Geometry in Machine Learning at ICML 2023. In addition, he has actively contributed to the TopoX software suite for topological neural networks. Carles Casacuberta is Full Professor of Geometry and Topology at the University of Barcelona (UB) since 2001. He earned his doctoral degree in 1988, specializing in algebraic topology. He has edited ten books and authored 55 research articles in areas such as homotopy theory, category theory, homological algebra, and more recently, topological data analysis. He serves on the editorial board of the Springer Universitext series and two research journals. Currently, he coordinates the Topological Machine Learning Seminar at UB and participates in several Horizon Europe projects focused on applications of artificial intelligence in biomedicine. Sergio Escalera is Full Professor at the Department of Mathematics and Computer Science of the University of Barcelona. He is action editor of the Journal of Data-centric Machine Learning Research and IEEE Transactions on Pattern Analysis and Machine Intelligence. He is vice-president of ChaLearn Challenges in Machine Learning, leading ChaLearn Looking at People events. He is co-creator of the Codalab open source platform for challenge organization and co-founder of the NeurIPS competition and Datasets and Benchmarks tracks. He has published more than 400 research papers and participated in the organization of scientific events. His research interests include machine learning fundamentals, and inclusive and transparent analysis of humans from visual and multi-modal data by means of deep learning mechanisms.

Von der hinteren Coverseite

This work presents a full generic approach to the detection and recognition of traffic signs. The approach, originally developed for a mobile mapping application, is based on the latest computer vision methods for object detection, and on powerful methods for multiclass classification. The challenge was to robustly detect a set of different sign classes in real time, and to classify each detected sign into a large, extensible set of classes. To address this challenge, several state-of-the-art methods were developed that can be used for different recognition problems. Following an introduction to the problems of traffic sign detection and categorization, the text focuses on the problem of detection, and presents recent developments in this field. The text then surveys a specific methodology for the problem of traffic sign categorization – Error-Correcting Output Codes – and presents several algorithms, performing experimental validation on a mobile mapping application. The work ends with a discussion on future lines of research, and continuing challenges for traffic sign recognition.

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Verlag
Springer
Erscheinungsdatum
2011
Sprache
Englisch
ISBN 10 
1447122445
ISBN 13 
9781447122449
Einband
Taschenbuch
Anzahl der Seiten
104
Kontakt zum Hersteller
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Darmstadt
64293
Deutschland

Weitere beliebte Ausgaben desselben Titels

9781447122463: Traffic-Sign Recognition Systems

Vorgestellte Ausgabe

ISBN 10:  1447122461 ISBN 13:  9781447122463
Verlag: Springer, 2011
Softcover