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Recent Advances in Logo Detection Using Machine Learning Paradigms de Yen-Wei Chen

Recent Advances in Logo Detection Using Machine Learning Paradigms: Intelligent Systems Reference Library, cartea 255

Autor Yen-Wei Chen, Xiang Ruan, Rahul Kumar Jain
en Limba Engleză Hardback – 31 mai 2024
This book presents the current trends in deep learning-based object detection framework with a focus on logo detection tasks. It introduces a variety of approaches, including attention mechanisms and domain adaptation for logo detection, and describes recent advancement in object detection frameworks using deep learning. We offer solutions to the major problems such as the lack of training data and the domain-shift issues.
This book provides numerous ways that deep learners can use for logo recognition, including:
  • Deep learning-based end-to-end trainable architecture for logo detection
  • Weakly supervised logo recognition approach using attention mechanisms
  • Anchor-free logo detection framework combining attention mechanisms to precisely locate logos in the real-world images
  • Unsupervised logo detection that takes into account domain-shift issues from synthetic to real-world images
  • Approach for logo detection modeling domain adaption task in the context of weakly supervised learning to overcome the lack of object-level annotation problem.
The merit of our logo recognition technique is demonstrated using experiments, performance evaluation, and feature distribution analysis utilizing different deep learning frameworks.
The book is directed to professors, researchers, practitioners in the field of engineering, computer science, and related fields as well as anyone interested in using deep learning techniques and applications in logo and various object detection tasks.
 
 
 
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Specificații

ISBN-13: 9783031598104
ISBN-10: 3031598105
Pagini: 132
Ilustrații: XII, 119 p. 64 illus., 63 illus. in color.
Dimensiuni: 160 x 241 x 13 mm
Greutate: 0.34 kg
Ediția:2024
Editura: Springer
Colecția Intelligent Systems Reference Library
Seria Intelligent Systems Reference Library

Locul publicării:Cham, Switzerland

Cuprins

Deep Convolutional Neural networks.- Introduction to Logo Detection.- Weakly Supervised Logo Detection Approach.

Notă biografică

Prof. Yen-Wei Chen received his B.E. degree in 1985 from Kobe University, Kobe, Japan. He received his M.E. degree in 1987 and his D.E. degree in 1990, both from Osaka University, Osaka, Japan. From 1991 to 1994, he was a research fellow at the Institute of Laser Technology, Osaka. From October 1994 to March 2004, he was an associate professor and a professor in the Department of Electrical and Electronic Engineering, University of the Ryukyus, Okinawa, Japan. He is currently a professor at the college of Information Science and Engineering, Ritsumeikan University, Japan. Since April 2024, he has been a Foreign Fellow of the Engineering Academy of Japan. He is associate editors for the International Journal of Image and Graphics (IJIG), and the International Journal of Knowledge-based Intelligent Engineering Systems. His research focuses on computer vision, deep learning and medical image analysis. He has published more than 300 research papers in these fields. Prof. Lanfen Lin received her B.S. and Ph.D. degrees from Northwestern Polytechnical University in 1990, and 1995 respectively. She held a postdoctoral position with the department of Computer Science and Technology, Zhejiang University, China, from January 1996 to December 1997. She was an associate professor from 1998 to 2005. Now she is a full professor and the vice director of the Artificial Intelligence Institute in Zhejiang University. She is also a member of Zhejiang Key Laboratory of Multi-omics Precision Diagnosis and Treatment of Liver Diseases.Her research interests include computer vision, medical image processing, and intelligent manufacturing. She has published more than 200 research papers in these fields. Dr. Rahul Kumar Jain received his Ph.D. degree from Ritsumeikan University, Shiga, Japan, in 2022. He has been an intern trainee at Tiwaki Co., Ltd., Japan, since 2019. He is now working as a senior researcher at the College of Information Science and Engineering, Ritsumeikan University, Japan. His research interests include computer vision, deep learning, and image processing as well as the applications of Artificial Intelligence in areas including engineering, science, computer science, healthcare, and so on.

Textul de pe ultima copertă

This book presents the current trends in deep learning-based object detection framework with a focus on logo detection tasks. It introduces a variety of approaches, including attention mechanisms and domain adaptation for logo detection, and describes recent advancement in object detection frameworks using deep learning. We offer solutions to the major problems such as the lack of training data and the domain-shift issues.
This book provides numerous ways that deep learners can use for logo recognition, including:
  • Deep learning-based end-to-end trainable architecture for logo detection
  • Weakly supervised logo recognition approach using attention mechanisms
  • Anchor-free logo detection framework combining attention mechanisms to precisely locate logos in the real-world images
  • Unsupervised logo detection that takes into account domain-shift issues from synthetic to real-world images
  • Approach for logo detection modelingdomain adaption task in the context of weakly supervised learning to overcome the lack of object-level annotation problem.
The merit of our logo recognition technique is demonstrated using experiments, performance evaluation, and feature distribution analysis utilizing different deep learning frameworks.
The book is directed to professors, researchers, practitioners in the field of engineering, computer science, and related fields as well as anyone interested in using deep learning techniques and applications in logo and various object detection tasks.
 
 
 

Caracteristici

Presents the novel logo detection methods using machine learning paradigms Demonstrates the merits of the presented approaches over the reported approaches using the real-world applications ​ Includes the state-of-the-art machine learning paradigms