Face recognition using vector de natu prachi (6 resultados)

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    • Idioma: Inglés

      Editorial: LAP LAMBERT Academic Publishing, 2012

      3659154318 / 9783659154317

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      Librería: Mispah books, Redhill, SURRE, Reino UnidoMispah books

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      Condición: Usado - Como Nuevo

      EUR 121,39

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      Cantidad disponible: 1 disponibles

      paperback. Condición: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

    • Idioma: Inglés

      Editorial: LAP LAMBERT Academic Publishing, 2016

      3659154318 / 9783659154317

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      Librería: preigu, Osnabrück, Alemaniapreigu

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      EUR 196,00

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      Taschenbuch. Condición: Neu. Face Recognition using Vector Quantization | A novel approach to Face Recognition | Prachi Natu (u. a.) | Taschenbuch | 96 S. | Englisch | 2016 | LAP LAMBERT Academic Publishing | EAN 9783659154317 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

    • Idioma: Inglés

      Editorial: LAP LAMBERT Academic Publishing Jul 2012, 2012

      3659154318 / 9783659154317

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      Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.

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      EUR 49,00

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      Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents a novel approach for Face Recognition using 'Vector Quantization'. Face Recognition is one of the popular biometric techniques used in today's era. A face recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source. Vector quantization is simple image compression technique. It is efficient for image coding because it reduces computational complexity. VQ compression is highly asymmetric in processing time: choosing an optimal codebook takes huge amounts of calculations, but decompression is lightning-fast-only one table lookup per vector. This makes VQ an excellent choice for face recognition. In this book four different VQ algorithms namely LBG, KPE, KMCG and KFCG are used to observe the efficiency of face recognition system. Efficiency is calculated in terms of recognition rate and computational complexity. It has been observed that KPE, KMCG and KFCG outperform LBG which is known as benchmark in vector quantization. Proposed techniques are compared with traditional DCT and Walsh transform also. It proves better than transform techniques. 96 pp. Englisch.

    • Idioma: Inglés

      Editorial: LAP LAMBERT Academic Publishing, 2012

      3659154318 / 9783659154317

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      Librería: moluna, Greven, Alemaniamoluna

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      Condición: Nuevo

      EUR 41,67

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      Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Natu PrachiPrachi Natu has received M.E. (Computer) degree from Mumbai University with distinction in 2010. She has 07 years of experience in teaching.Her areas of interest are Image Processing, Database Management Systems, Operating.

    • Idioma: Inglés

      Editorial: LAP LAMBERT Academic Publishing, 2012

      3659154318 / 9783659154317

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      Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH

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      EUR 49,00

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      Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book presents a novel approach for Face Recognition using 'Vector Quantization'. Face Recognition is one of the popular biometric techniques used in today's era. A face recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source. Vector quantization is simple image compression technique. It is efficient for image coding because it reduces computational complexity. VQ compression is highly asymmetric in processing time: choosing an optimal codebook takes huge amounts of calculations, but decompression is lightning-fast-only one table lookup per vector. This makes VQ an excellent choice for face recognition. In this book four different VQ algorithms namely LBG, KPE, KMCG and KFCG are used to observe the efficiency of face recognition system. Efficiency is calculated in terms of recognition rate and computational complexity. It has been observed that KPE, KMCG and KFCG outperform LBG which is known as benchmark in vector quantization. Proposed techniques are compared with traditional DCT and Walsh transform also. It proves better than transform techniques.

    • Idioma: Inglés

      Editorial: LAP LAMBERT Academic Publishing Jul 2012, 2012

      3659154318 / 9783659154317

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      Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000

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      Condición: Nuevo

      EUR 196,00

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      Cantidad disponible: 1 disponibles

      Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book presents a novel approach for Face Recognition using 'Vector Quantization'. Face Recognition is one of the popular biometric techniques used in today's era. A face recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source. Vector quantization is simple image compression technique. It is efficient for image coding because it reduces computational complexity. VQ compression is highly asymmetric in processing time: choosing an optimal codebook takes huge amounts of calculations, but decompression is lightning-fast-only one table lookup per vector. This makes VQ an excellent choice for face recognition. In this book four different VQ algorithms namely LBG, KPE, KMCG and KFCG are used to observe the efficiency of face recognition system. Efficiency is calculated in terms of recognition rate and computational complexity. It has been observed that KPE, KMCG and KFCG outperform LBG which is known as benchmark in vector quantization. Proposed techniques are compared with traditional DCT and Walsh transform also. It proves better than transform techniques.OmniScriptum SRL, Str. Armeneasca 28/1, office 1, 2012 Chisinau 96 pp. Englisch.