LEADER 04082nam 22007335 450 001 9910299744003321 005 20200702173139.0 010 $a3-319-06767-2 024 7 $a10.1007/978-3-319-06767-4 035 $a(CKB)3710000000114408 035 $a(EBL)1731146 035 $a(OCoLC)880316081 035 $a(SSID)ssj0001239470 035 $a(PQKBManifestationID)11711069 035 $a(PQKBTitleCode)TC0001239470 035 $a(PQKBWorkID)11199045 035 $a(PQKB)11282571 035 $a(MiAaPQ)EBC1731146 035 $a(DE-He213)978-3-319-06767-4 035 $a(PPN)178781037 035 $a(EXLCZ)993710000000114408 100 $a20140512d2014 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aIris Image Recognition $eWavelet Filter-banks Based Iris Feature Extraction Schemes /$fby Amol D. Rahulkar, Raghunath S. Holambe 205 $a1st ed. 2014. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2014. 215 $a1 online resource (95 p.) 225 1 $aSpringerBriefs in Signal Processing,$x2196-4076 300 $aDescription based upon print version of record. 311 $a3-319-06766-4 320 $aIncludes bibliographical references. 327 $aIntroduction -- Features Based on Triplet Half-band Wavelet Filter-banks -- Combined Directional Wavelet Filter-banks Based Features -- Iris Representation by Combined Hybrid Directional Wavelet Filter-banks -- Ordinal Measures Based on Directional Ordinal Wavelet Filters. 330 $aThis book provides the new results in wavelet filter banks based feature extraction, and the classifier in the field of iris image recognition. It provides the broad treatment on the design of separable, non-separable wavelets filter banks, and the classifier. The design techniques presented in the book are applied on iris image analysis for person authentication. This book also brings together the three strands of research (wavelets, iris image analysis, and classifier). It compares the performance of the presented techniques with state-of-the-art available schemes. This book contains the compilation of basic material on the design of wavelets that avoids reading many different books. Therefore, it provide an easier path for the new-comers, researchers to master the contents. In addition, the designed filter banks and classifier can also be effectively used than existing filter-banks in many signal processing applications like pattern classification, data-compression, watermarking, denoising etc.  that will give the new directions of the research in the relevant field for the readers. 410 0$aSpringerBriefs in Signal Processing,$x2196-4076 606 $aSignal processing 606 $aImage processing 606 $aSpeech processing systems 606 $aOptical data processing 606 $aSystem safety 606 $aSignal, Image and Speech Processing$3https://scigraph.springernature.com/ontologies/product-market-codes/T24051 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics$3https://scigraph.springernature.com/ontologies/product-market-codes/I22005 606 $aSecurity Science and Technology$3https://scigraph.springernature.com/ontologies/product-market-codes/P31080 615 0$aSignal processing. 615 0$aImage processing. 615 0$aSpeech processing systems. 615 0$aOptical data processing. 615 0$aSystem safety. 615 14$aSignal, Image and Speech Processing. 615 24$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aSecurity Science and Technology. 676 $a006.4 700 $aRahulkar$b Amol D$4aut$4http://id.loc.gov/vocabulary/relators/aut$0999579 702 $aHolambe$b Raghunath S$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299744003321 996 $aIris Image Recognition$92294350 997 $aUNINA