國立高雄大學圖資館 |

登入

回首頁

Recent advances in logo detection using machine learning paradigmstheory and practice /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Recent advances in logo detection using machine learning paradigmsby Yen-Wei Chen, Xiang Ruan, Rahul Kumar Jain.
其他題名:	theory and practice /
作者:	Chen, Yen-Wei.
其他作者:	Ruan, Xiang.
出版者:	Cham :Springer International Publishing :2024.
面頁冊數:	xii, 119 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Pattern recognition systems.
電子資源:	https://doi.org/10.1007/978-3-031-59811-1
ISBN:	9783031598111$q(electronic bk.)

Recent advances in logo detection using machine learning paradigmstheory and practice /
Chen, Yen-Wei.

Recent advances in logo detection using machine learning paradigmstheory and practice /[electronic resource] :by Yen-Wei Chen, Xiang Ruan, Rahul Kumar Jain. - Cham :Springer International Publishing :2024. - xii, 119 p. :ill. (some col.), digital ;24 cm. - Intelligent systems reference library,v. 2551868-4408 ;. - Intelligent systems reference library ;v.24..

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

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.

ISBN: 9783031598111$q(electronic bk.)

Standard No.: 10.1007/978-3-031-59811-1doiSubjects--Topical Terms:

183725
Pattern recognition systems.

LC Class. No.: TL7882.P3

Dewey Class. No.: 006.4

Recent advances in logo detection using machine learning paradigmstheory and practice /
LDR:02733nmm a2200337 a 4500 001 659341
003 DE-He213
005 20240530125452.0
006 m d
007 cr nn 008maaau
008 240927s2024 sz s 0 eng d
020 $a 9783031598111$q(electronic bk.)
020 $a 9783031598104$q(paper)
024 7 $a 10.1007/978-3-031-59811-1 $2 doi
035 $a 978-3-031-59811-1
040 $a GP $c GP
041 0 $a eng
050 4 $a TL7882.P3
072 7 $a UN $2 bicssc
072 7 $a COM018000 $2 bisacsh
072 7 $a UN $2 thema
082 0 4 $a 006.4 $2 23
090 $a TL7882.P3 $b C518 2024
100 1 $a Chen, Yen-Wei. $3 680498
245 1 0 $a Recent advances in logo detection using machine learning paradigms $h [electronic resource] : $b theory and practice / $c by Yen-Wei Chen, Xiang Ruan, Rahul Kumar Jain.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2024.
300 $a xii, 119 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Intelligent systems reference library, $x 1868-4408 ; $v v. 255
505 0 $a Deep Convolutional Neural networks -- Introduction to Logo Detection -- Weakly Supervised Logo Detection Approach.
520 $a 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.
650 0 $a Pattern recognition systems. $3 183725
650 0 $a Machine learning. $3 188639
650 0 $a Computer vision. $3 200113
650 0 $a Logos (Symbols) $x Data processing. $3 970709
650 1 4 $a Data Engineering. $3 839346
650 2 4 $a Computational Intelligence. $3 338479
650 2 4 $a Artificial Intelligence. $3 212515
700 1 $a Ruan, Xiang. $3 970707
700 1 $a Jain, Rahul Kumar. $3 970708
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Intelligent systems reference library ; $v v.24. $3 558591
856 4 0 $u https://doi.org/10.1007/978-3-031-59811-1
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)