03659nam a22005175i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001600172072001500188072002300203072001400226100008300240245016700323250001800490264007500508300006500583336002600648337002600674338003600700347002400736490006100760505011900821520149200940650003402432650003902466650003602505650002902541650003902570650003602609700008102645700008702726710003402813773002602847776003602873776003602909776003602945830006802981856004603049912001403095912001503109999001703124978-3-031-59811-1DE-He21320251030112243.0cr nn 008mamaa240530s2024    sz |    s    |||| 0|eng d  a97830315981119978-3-031-59811-17 a10.1007/978-3-031-59811-12doi 4aTA345-345.5 7aUN2bicssc 7aCOM0180002bisacsh 7aUN2thema1 aChen, Yen-Wei.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut97783710aRecent Advances in Logo Detection Using Machine Learning Paradigmsh[electronic resource] :bTheory and Practice /cby Yen-Wei Chen, Xiang Ruan, Rahul Kumar Jain.  a1st ed. 2024. 1aCham :bSpringer International Publishing :bImprint: Springer,c2024.  aXII, 119 p. 64 illus., 63 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aIntelligent Systems Reference Library,x1868-4408 ;v2550 aDeep Convolutional Neural networks -- Introduction to Logo Detection -- Weakly Supervised Logo Detection Approach.  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. . 0aEngineeringxData processing. 0aComputational intelligence.964052 0aArtificial intelligence.95844114aData Engineering.96605924aComputational Intelligence.96405224aArtificial Intelligence.9584411 aRuan, Xiang.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut9778381 aJain, Rahul Kumar.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut9778392 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303159810408iPrinted edition:z978303159812808iPrinted edition:z9783031598135 0aIntelligent Systems Reference Library,x1868-4408 ;v25596703440uhttps://doi.org/10.1007/978-3-031-59811-1  aZDB-2-INR  aZDB-2-SXIT  c60293d60293