LEADER 01446nam 2200445 450 001 9910830460803321 005 20230817190749.0 010 $a1-119-50919-X 010 $a1-119-50917-3 010 $a1-119-50916-5 035 $a(CKB)4100000006520234 035 $a(MiAaPQ)EBC5516071 035 $a(EXLCZ)994100000006520234 100 $a20181008d2019 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aBritish poultry standards $ecomplete specifications and judging points of all standardized breeds and varieties of poultry as compiled by the specialist affiliated breed clubs and recognized by the Poultry Club of Great Britain /$fco-edited by J. Ian H. Allonby, Philippe B. Wilson 205 $aSeventh edition. 210 1$aHoboken, New Jersey :$cWiley Blackwell,$d2019. 215 $a1 online resource (517 pages) 311 $a1-119-50914-9 606 $aPoultry$xJudging 606 $aPoultry breeds 606 $aPoultry$xStandards 615 0$aPoultry$xJudging. 615 0$aPoultry breeds. 615 0$aPoultry$xStandards. 676 $a636.51 702 $aAllonby$b J. Ian H. 702 $aWilson$b Philippe B. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910830460803321 996 $aBritish poultry standards$94020859 997 $aUNINA LEADER 01259nam0 22003011i 450 001 UON00385796 005 20231205104549.663 010 $a978-88-8419-429-9 100 $a20101112d2010 |0itac50 ba 101 $aita 102 $aIT 105 $a|||| ||||| 200 1 $aUscire dalla crisi$ecomunicazioni interne sul salvataggio della Banca Commerciale Italiana, 1933-1934$f Raffaele Mattioli$ga cura di Francesca Pino$gcon la collaborazione di Francesca Gaido 210 $aTorino$cNino Aragno Editore$d2010 215 $a214 p.$d23 cm. 410 1$1001UON00277831$12001 $aBiblioteca Aragno 606 $aBanca Commerciale Italiana$xStoria$x1933-1934$3UONC076758$2FI 620 $aIT$dTorino$3UONL000014 700 1$aMATTIOLI$bRaffaele$3UONV127240$075603 702 1$aGAIDO$bFrancesca$3UONV198852 702 1$aPINO$bFrancesca$3UONV198851 712 $aAragno$3UONV271960$4650 801 $aIT$bSOL$c20241122$gRICA 899 $aSIBA - SISTEMA BIBLIOTECARIO DI ATENEO$2UONSI 912 $aUON00385796 950 $aSIBA - SISTEMA BIBLIOTECARIO DI ATENEO$dSI III STORIAEUR D A 3125 $eSI SC 46741 5 3125 $sBuono 996 $aUscire dalla crisi$91353837 997 $aUNIOR LEADER 02657nam 22004695 450 001 9910953877803321 005 20251117113142.0 010 $a3-8325-9013-7 035 $a(CKB)4100000007650689 035 $a(MiAaPQ)EBC5662536 035 $a5c7aad7c-160c-40fd-a4fc-7583b0dd2d03 035 $a(EXLCZ)994100000007650689 100 $a20190115d2019 ||| | 101 0 $aeng 135 $auruuu---uuuuu 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPedestrian Detection Algorithms using Shearlets 210 31$aBerlin$cLogos Verlag$d2019 215 $aOnline-Ressource (186 S.) 300 $aPublicationDate: 20190115 311 08$a3-8325-4840-8 330 $aLong description: In this thesis, we investigate the applicability of the shearlet transform for the task of pedestrian detection. Due to the usage of in several emerging technologies, such as automated or autonomous vehicles, pedestrian detection has evolved into a key topic of research in the last decade. In this time period, a wealth of different algorithms has been developed. According to the current results on pedestrian detection benchmarks, the algorithms can be divided into two categories. First, application of hand-crafted image features and of a classifier trained on these features. Second, methods using Convolutional Neural Networks in which features are learned during the training phase. It is studied how both of these types of procedures can be further improved by the incorporation of shearlets, a framework for image analysis which has a comprehensive theoretical basis. To this end, we adapt the shearlet framework according to the requirements of the practical application of pedestrian detection algorithms. One main application area of pedestrian detection is located in the automotive domain. In this field, algorithms have to be runable on embedded devices. Therefore, we study the embedded realization of a pedestrian detection algorithm based on the shearlet transform. 606 $aKünstliche Intelligenz$2gnd 606 $aBildverarbeitung$2gnd 606 $aDeep Learning$2gnd 606 $aAutonomes Fahrzeug$2gnd 606 $aObjekterkennung$2gnd 615 07$aKünstliche Intelligenz. 615 07$aBildverarbeitung. 615 07$aDeep Learning. 615 07$aAutonomes Fahrzeug. 615 07$aObjekterkennung. 676 $a363.12/563 700 $aPfeifer$b Lienhard$4aut$01854262 801 0$bMiAaPQ 801 2$bNZ-WeVUL 906 $aBOOK 912 $a9910953877803321 996 $aPedestrian Detection Algorithms using Shearlets$94451321 997 $aUNINA