東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Recent advances in logo detection using machine learning paradigms = theory 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 paradigms/ by 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.
內容註:	Deep Convolutional Neural networks -- Introduction to Logo Detection -- Weakly Supervised Logo Detection Approach.
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

Recent advances in logo detection using machine learning paradigms = theory 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. 255..

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

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

527885
Pattern recognition systems.

LC Class. No.: TL7882.P3

Dewey Class. No.: 006.4

Recent advances in logo detection using machine learning paradigms = theory and practice /
LDR:02733nmm a2200337 a 4500 001 2371628
003 DE-He213
005 20240530125452.0
006 m d
007 cr nn 008maaau
008 241127s2024 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 2062809
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 527885
650 0 $a Machine learning. $3 533906
650 0 $a Computer vision. $3 540671
650 0 $a Logos (Symbols) $x Data processing. $3 3717787
650 1 4 $a Data Engineering. $3 3409361
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Artificial Intelligence. $3 769149
700 1 $a Ruan, Xiang. $3 3717784
700 1 $a Jain, Rahul Kumar. $3 3717785
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Intelligent systems reference library ; $v v. 255. $3 3717786
856 4 0 $u https://doi.org/10.1007/978-3-031-59811-1
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)