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020 _a9783031598111
_9978-3-031-59811-1
024 7 _a10.1007/978-3-031-59811-1
_2doi
050 4 _aTA345-345.5
072 7 _aUN
_2bicssc
072 7 _aCOM018000
_2bisacsh
072 7 _aUN
_2thema
100 1 _aChen, Yen-Wei.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_977837
245 1 0 _aRecent Advances in Logo Detection Using Machine Learning Paradigms
_h[electronic resource] :
_bTheory and Practice /
_cby Yen-Wei Chen, Xiang Ruan, Rahul Kumar Jain.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2024.
300 _aXII, 119 p. 64 illus., 63 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aIntelligent Systems Reference Library,
_x1868-4408 ;
_v255
505 0 _aDeep Convolutional Neural networks -- Introduction to Logo Detection -- Weakly Supervised Logo Detection Approach.
520 _aThis 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 _aEngineering
_xData processing.
650 0 _aComputational intelligence.
_964052
650 0 _aArtificial intelligence.
_958441
650 1 4 _aData Engineering.
_966059
650 2 4 _aComputational Intelligence.
_964052
650 2 4 _aArtificial Intelligence.
_958441
700 1 _aRuan, Xiang.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_977838
700 1 _aJain, Rahul Kumar.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_977839
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031598104
776 0 8 _iPrinted edition:
_z9783031598128
776 0 8 _iPrinted edition:
_z9783031598135
830 0 _aIntelligent Systems Reference Library,
_x1868-4408 ;
_v255
_967034
856 4 0 _uhttps://doi.org/10.1007/978-3-031-59811-1
912 _aZDB-2-INR
912 _aZDB-2-SXIT
999 _c60293
_d60293