Iris Image Recognition: Wavelet Filter-banks Based Iris Feature Extraction Schemes: SpringerBriefs in Electrical and Computer Engineering
Autor Amol D. Rahulkar, Raghunath S. Holambeen Limba Engleză Paperback – 27 mai 2014
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Specificații
ISBN-13: 9783319067667
ISBN-10: 3319067664
Pagini: 84
Ilustrații: XIII, 84 p. 33 illus., 17 illus. in color.
Dimensiuni: 155 x 235 x 17 mm
Greutate: 0.15 kg
Ediția:2014
Editura: Springer International Publishing
Colecția Springer
Seriile SpringerBriefs in Electrical and Computer Engineering, SpringerBriefs in Signal Processing
Locul publicării:Cham, Switzerland
ISBN-10: 3319067664
Pagini: 84
Ilustrații: XIII, 84 p. 33 illus., 17 illus. in color.
Dimensiuni: 155 x 235 x 17 mm
Greutate: 0.15 kg
Ediția:2014
Editura: Springer International Publishing
Colecția Springer
Seriile SpringerBriefs in Electrical and Computer Engineering, SpringerBriefs in Signal Processing
Locul publicării:Cham, Switzerland
Public țintă
ResearchCuprins
Introduction.- 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.
Textul de pe ultima copertă
This 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.
Caracteristici
Brings together new results in wavelet filter-banks based feature extraction Offers a compilation of basic material on the design of wavelets that otherwise requires the reading many different books and other sources Provides an easy path for the new-comers, researchers to master the contents Includes supplementary material: sn.pub/extras