LEADER 03577nam 22006015 450 001 9910865235503321 005 20250807133322.0 010 $a3-031-59811-3 024 7 $a10.1007/978-3-031-59811-1 035 $a(MiAaPQ)EBC31359063 035 $a(Au-PeEL)EBL31359063 035 $a(CKB)32200396100041 035 $a(DE-He213)978-3-031-59811-1 035 $a(EXLCZ)9932200396100041 100 $a20240530d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aRecent Advances in Logo Detection Using Machine Learning Paradigms $eTheory and Practice /$fby Yen-Wei Chen, Xiang Ruan, Rahul Kumar Jain 205 $a1st ed. 2024. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2024. 215 $a1 online resource (128 pages) 225 1 $aIntelligent Systems Reference Library,$x1868-4408 ;$v255 311 08$a3-031-59810-5 320 $aIncludes bibliographical references. 327 $aDeep Convolutional Neural networks -- Introduction to Logo Detection -- Weakly Supervised Logo Detection Approach. 330 $aThis book presents the current trends in deep learning-based object detection framework with a focus on logo detection tasks. It introduces a variety of approaches, including attention mechanisms and domain adaptation for logo detection, and describes recent advancement in object detection frameworks using deep learning. We offer solutions to the major problems such as the lack of training data and the domain-shift issues. This book provides numerous ways that deep learners can use for logo recognition, including: Deep learning-based end-to-end trainable architecture for logo detection Weakly supervised logo recognition approach using attention mechanisms Anchor-free logo detection framework combining attention mechanisms to precisely locate logos in the real-world images Unsupervised logo detection that takes into account domain-shift issues from synthetic to real-world images Approach for logo detection modelingdomain adaption task in the context of weakly supervised learning to overcome the lack of object-level annotation problem. The merit of our logo recognition technique is demonstrated using experiments, performance evaluation, and feature distribution analysis utilizing different deep learning frameworks. The book is directed to professors, researchers, practitioners in the field of engineering, computer science, and related fields as well as anyone interested in using deep learning techniques and applications in logo and various object detection tasks. . 410 0$aIntelligent Systems Reference Library,$x1868-4408 ;$v255 606 $aEngineering$xData processing 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aData Engineering 606 $aComputational Intelligence 606 $aArtificial Intelligence 615 0$aEngineering$xData processing. 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 14$aData Engineering. 615 24$aComputational Intelligence. 615 24$aArtificial Intelligence. 676 $a741.6 700 $aChen$b Yen-Wei$01362820 702 $aRuan$b Xiang 702 $aJain$b Rahul Kumar 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910865235503321 996 $aRecent Advances in Logo Detection Using Machine Learning Paradigms$94169779 997 $aUNINA