LEADER 00777nam--2200277---450 001 990001011300203316 005 20230220150014.0 010 $a978-88-06-25223-6 100 $a20020304d2021----km y1itay5003----ba 101 0 $aita 102 $aIT 200 1 $aC'era una volta un paradosso$estorie di illusioni e verità nascoste$fPiergiorgio Odifreddi 210 $aTorino$cEinaudi$d2021 215 $aXV, 304 p.$d23 cm 225 2 $aSaggi 410 0$12001$aSaggi 606 0 $aParadossi$2BNCF 676 $a165 700 1$aODIFREDDI,$bPiergiorgio$028537 801 0$aIT$bsalbc$gISBD 912 $a990001011300203316 951 $aII.3. 579a$b278856 L.M.$cII.3.$d553111 959 $aBK 969 $aUMA 996 $aC'era una volta un paradosso$9147568 997 $aUNISA LEADER 00787nam a2200229 a 4500 001 991004017879707536 008 030224s1971 it 000 0 ita d 035 $ab11898860-39ule_inst 040 $aDip.to Scienze Storiche Fil. e Geogr.$bita 100 1 $aMichelet, Jules$0160263 245 13$aLa Strega /$cJules Michelet 260 $aTorino :$bEinaudi,$c1971 300 $aXXII,249 p. : tav. ;$c22 cm. 440 2$aI millenni 500 $aTit.orig.: La sorciere 740 32$aLa sorciere 907 $a.b11898860$b04-03-22$c24-02-03 912 $a991004017879707536 945 $aLE009 STOR. 08.2-17$g1$i2009000127845$lle009$o-$pE0.00$q-$rl$s- $t0$u4$v1$w4$x0$y.i12161408$z24-02-03 996 $aStrega$991618 997 $aUNISALENTO 998 $ale009$b - - $cm$da $e-$fita$git $h0$i0 LEADER 04507nam 2200517 450 001 9910798288903321 005 20230124193612.0 010 $a1-68108-110-5 035 $a(CKB)3710000000627710 035 $a(EBL)4504167 035 $a(MiAaPQ)EBC4504167 035 $a(Au-PeEL)EBL4504167 035 $a(CaPaEBR)ebr11204299 035 $a(OCoLC)948924362 035 $a(EXLCZ)993710000000627710 100 $a20160503h20162016 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 00$aAdvances in face image analysis $etheory and application /$fedited by Fadi Dornaika ; contributors Ammar Assoum [and fifteen others] 210 1$aSharjah, United Arab Emirates :$cBentham Science Publishers,$d2016. 210 4$d©2016 215 $a1 online resource (264 p.) 300 $aDescription based upon print version of record. 311 $a1-68108-111-3 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aCONTENTS; FOREWORD ; PREFACE ; LIST OF CONTRIBUTORS ; Facial Expression Classification Based on Convolutional Neural Networks ; INTRODUCTION; Convolutional Neural Networks; Facial Expression Analysis; GRADIENT-BASED LEARNING FOR CNNS; FEATURE GENERALIZATION; EXPERIMENTS; Datasets; CK-Regianini Dataset; CK-Zheng Dataset; CMU-Pittsburgh dataset ; Experiments on CNN-based Facial Expression Classification; Design; Results and Analysis; Experiments on Feature Generalization; Design; Results and Analysis; DISCUSSION; CONFLICT OF INTEREST; ACKNOWLEDGEMENTS; REFERENCES 327 $aSparsity Preserving Projection Based Constrained Graph Embedding and Its Application to Face Recognition INTRODUCTION; RELATED WORK; Locality Preserving Projection; Neighborhood Preserving Embedding; Sparsity Preserving Projection; Constrained Graph Embedding; SPP BASED CONSTRAINED GRAPH EMBEDDING; SPP-CGE; Out-of-Sample Extension; EXPERIMENTAL RESULTS; CONCLUSION; NOTES; CONFLICT OF INTEREST; ACKNOWLEDGEMENTS; REFERENCES; Face Recognition Using Exponential Local Discriminant Embedding ; INTRODUCTION; Contribution and Related Work; REVIEW OF LOCAL DISCRIMINANT EMBEDDING (LDE) 327 $aIntrinsic Graph and Penalty GraphOptimal Mapping; The Small Sample Size Problem; EXPONENTIAL LDE; Matrix Exponential; Exponential LDE; THEORETICAL ANALYSIS OF ELDE; Solving the SSS Problem; Distance Diffusion Mapping; PERFORMANCE EVALUATION; Face Databases; Recognition Accuracy; Comparison between Regularized LDE and ELDE; CONCLUSION; NOTES; CONFLICT OF INTEREST; ACKNOWLEDGMENTS; REFERENCES; Adaptive Locality Preserving Projections for Face Recognition ; INTRODUCTION; LOCALITY PRESERVING PROJECTIONS; ENHANCED AND PARAMETERLESS LPP; PERFORMANCE EVALUATION; Face Databases; Experimental Results 327 $aPerformance Comparison for OLPP and SLPPCONCLUSION; NOTES; CONFLICT OF INTEREST; ACKNOWLEDGMENTS; REFERENCES; Face Recognition Using 3D Face Rectification ; INTRODUCTION; PROPOSED METHOD ; FACE DATABASE ; PREPROCESSING ; FACIAL FEATURE DETECTION ; POSE ESTIMATION; IRAD Contours; Ellipse Fitting And Roll Correction; Yaw Correction; Pitch Correction; Accuracy Of The Pose Estimation Method; ROTATION AND POST PROCESSING; EXPERIMENTS; CONCLUSION; NOTES; CONFLICT OF INTEREST; ACKNOWLEDGMENTS; REFERENCES; 3D Face Recognition ; INTRODUCTION; 3D FACE ACQUISITION; 3D FACE REPRESENTATION; PREPROCESSING 327 $a3D FACE ALIGNMENTFACE RECOGNITION; CONCLUDING REMARKS; CONFLICT OF INTEREST; ACKNOWLEDGEMENTS; REFERENCES; Model-Less 3D Face Pose Estimation ; INTRODUCTION; STATE OF THE ART; THE MACHINE LEARNING METHODOLOGY; Locality Preserving Projections; LPP Algorithm; Supervised Locality Preserving Projections; LABEL-SENSITIVE LOCALITY PRESERVING PROJECTION; Presetting:; Algorithm:; PROPOSED APPROACH: SPARSE GRAPH BASED LSLPP; EXPERIMENTAL RESULTS; CONCLUSION; NOTES; CONFLICT OF INTEREST; ACKNOWLEDGEMENTS; REFERENCES; Efficient Deformable 3D Face Model Fitting to Monocular Images ; INTRODUCTION 327 $aLIGHTWEIGHT FACIAL FEATURE DETECTION 606 $aHuman face recognition (Computer science) 615 0$aHuman face recognition (Computer science) 676 $a006.37 702 $aDornaika$b Fadi 702 $aAssoum$b Ammar 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910798288903321 996 $aAdvances in face image analysis$93674168 997 $aUNINA