LEADER 04385nam 22005895 450 001 9910366656003321 005 20200630005529.0 010 $a981-13-2387-9 024 7 $a10.1007/978-981-13-2387-4 035 $a(CKB)4100000009844930 035 $a(DE-He213)978-981-13-2387-4 035 $a(MiAaPQ)EBC5978088 035 $a(PPN)248600249 035 $a(EXLCZ)994100000009844930 100 $a20191113d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aHuman Centric Visual Analysis with Deep Learning$b[electronic resource] /$fby Liang Lin, Dongyu Zhang, Ping Luo, Wangmeng Zuo 205 $a1st ed. 2020. 210 1$aSingapore :$cSpringer Singapore :$cImprint: Springer,$d2020. 215 $a1 online resource (XII, 156 p. 53 illus., 46 illus. in color.) 311 $a981-13-2386-0 327 $aPart I Motivation & Overview -- Chapter 1: The Foundation and Advances of Deep Learning -- Chapter 2. Human Centric Visual Analysis: Tasks and Progress -- Part II Localizing Persons in Images -- Chapter 3: Face Localization and Enhancement -- Chapter 4: Pedestrian Detection with RPN and Boosted Forests -- Part III Parsing Person In Details -- Chapter 5: Self-supervised Structure-sensitive Learning for Human Parsing -- Chapter 6: Instance-level Human Parsing -- Chapter 7: Video Instance-level Human Parsing -- Part IV Identifying and Verifying Persons -- Chapter 8: Person Verification -- Chapter 9: Face Verification -- Part V Higher Level Tasks -- Chapter 10: Human Activity Understanding. 330 $aThis book introduces the applications of deep learning in various human centric visual analysis tasks, including classical ones like face detection and alignment and some newly rising tasks like fashion clothing parsing. Starting from an overview of current research in human centric visual analysis, the book then presents a tutorial of basic concepts and techniques of deep learning. In addition, the book systematically investigates the main human centric analysis tasks of different levels, ranging from detection and segmentation to parsing and higher-level understanding. At last, it presents the state-of-the-art solutions based on deep learning for every task, as well as providing sufficient references and extensive discussions. Specifically, this book addresses four important research topics, including 1) localizing persons in images, such as face and pedestrian detection; 2) parsing persons in details, such as human pose and clothing parsing, 3) identifying and verifying persons, such as face and human identification, and 4) high-level human centric tasks, such as person attributes and human activity understanding. This book can serve as reading material and reference text for academic professors / students or industrial engineers working in the field of vision surveillance, biometrics, and human-computer interaction, where human centric visual analysis are indispensable in analysing human identity, pose, attributes, and behaviours for further understanding. 606 $aOptical data processing 606 $aPattern recognition 606 $aBiometrics (Biology) 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 606 $aBiometrics$3https://scigraph.springernature.com/ontologies/product-market-codes/I22040 615 0$aOptical data processing. 615 0$aPattern recognition. 615 0$aBiometrics (Biology). 615 14$aImage Processing and Computer Vision. 615 24$aPattern Recognition. 615 24$aBiometrics. 676 $a006.6 676 $a006.37 700 $aLin$b Liang$4aut$4http://id.loc.gov/vocabulary/relators/aut$0867009 702 $aZhang$b Dongyu$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aLuo$b Ping$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aZuo$b Wangmeng$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910366656003321 996 $aHuman Centric Visual Analysis with Deep Learning$91935194 997 $aUNINA