LEADER 01712nam 2200493Ia 450 001 9910711112203321 005 20180711120951.0 024 8 $aGOVPUB-C13-746e277e286d245f01fa25882d4da3d2 035 $a(CKB)5470000002480893 035 $a(OCoLC)957470663 035 $a(OCoLC)995470000002480893 035 $a(EXLCZ)995470000002480893 100 $a20160829d1961 ua 0 101 0 $aeng 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aInvestigation of fungus-resistance tests for show pan materials /$fSelden D. Cole, Paul R. Achenbach 210 1$aGaithersburg, MD :$cU.S. Dept. of Commerce, National Institute of Standards and Technology,$d1961. 215 $a1 online resource 225 1 $aNBS report ;$v7382 300 $a1961. 300 $aContributed record: Metadata reviewed, not verified. Some fields updated by batch processes. 300 $aTitle from PDF title page. 320 $aIncludes bibliographical references. 606 $aBuilding materials$xTesting 606 $aMolds (Fungi)$xTesting 606 $aBuilding materials$xTesting$2fast 615 0$aBuilding materials$xTesting. 615 0$aMolds (Fungi)$xTesting. 615 7$aBuilding materials$xTesting. 700 $aCole$b Selden D$01391428 701 $aAchenbach$b Paul R$01389871 701 $aCole$b Selden D$01391428 712 02$aUnited States.$bNational Bureau of Standards. 801 0$bNBS 801 1$bNBS 801 2$bOCLCO 801 2$bOCLCQ 801 2$bOCLCF 906 $aBOOK 912 $a9910711112203321 996 $aInvestigation of fungus-resistance tests for show pan materials$93470268 997 $aUNINA LEADER 02952nam 22006855 450 001 9911007357803321 005 20250531130314.0 010 $a3-031-89560-6 024 7 $a10.1007/978-3-031-89560-9 035 $a(CKB)39124522900041 035 $a(DE-He213)978-3-031-89560-9 035 $a(MiAaPQ)EBC32142495 035 $a(Au-PeEL)EBL32142495 035 $a(OCoLC)1524421710 035 $a(EXLCZ)9939124522900041 100 $a20250531d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvances in Gait-Based Identification $eA Systematic Review of Deep Learning Models Leveraging Computer Vision Techniques /$fby Diogo R. M. Bastos, Joćo Manuel R. S. Tavares 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (XIX, 96 p. 22 illus., 18 illus. in color.) 225 1 $aStudies in Systems, Decision and Control,$x2198-4190 ;$v593 311 08$a3-031-89559-2 327 $aIntroduction -- Background -- Research objectives and method -- Datasets -- Comparison of the reviewed methods -- Conclusion. 330 $aThis book provides a systematic review of gait-based person identification, categorizing studies into deep-learning and non-deep-learning approaches while analyzing key datasets and performance metrics. It explores challenges such as covariant factors, e.g., viewing angles, clothing, and accessories, and highlights advancements in real-world gait recognition systems. With a structured methodology and transparent review process, this work serves as a valuable reference for researchers and a foundation for future developments in biometric identification. 410 0$aStudies in Systems, Decision and Control,$x2198-4190 ;$v593 606 $aComputer vision 606 $aImage processing 606 $aBiomechanics 606 $aArtificial intelligence 606 $aBiometric identification 606 $aComputer Vision 606 $aImage Processing 606 $aBiomechanics 606 $aArtificial Intelligence 606 $aBiometrics 615 0$aComputer vision. 615 0$aImage processing. 615 0$aBiomechanics. 615 0$aArtificial intelligence. 615 0$aBiometric identification. 615 14$aComputer Vision. 615 24$aImage Processing. 615 24$aBiomechanics. 615 24$aArtificial Intelligence. 615 24$aBiometrics. 676 $a006.37 700 $aBastos$b Diogo R. M$4aut$4http://id.loc.gov/vocabulary/relators/aut$01823668 702 $aR. S. Tavares$b Joćo Manuel$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911007357803321 996 $aAdvances in Gait-Based Identification$94390478 997 $aUNINA