Banknote Image Retrieval Using Rotated Quaternion Wavelet Filters

Abstract A new method of banknote image retrieval is proposed by using new set of rotated quaternion wavelet filters (RQWF) and standard quaternion wavelet transform (QWT) jointly. The robust and rotationally invariant features are extracted from QWT and RQWF decomposed sub-bands of banknote image....
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Gai, Shan [verfasserIn] Liu, Peng Liu, Jiafeng Tang, Xianglong

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2011

Schlagwörter:	discrete wavelet transform (DWT) complex wavelet transform (CWT) quaternion wavelet transform (QWT) rotated quaternion wavelet filters (RQWF) feature extraction support vector machine (SVM)

Anmerkung:	© the authors 2011

Übergeordnetes Werk:	Enthalten in: International journal of computational intelligence systems - Paris : Atlantis Press, 2008, 4(2011), 2 vom: 01. Apr., Seite 268-276
Übergeordnetes Werk:	volume:4 ; year:2011 ; number:2 ; day:01 ; month:04 ; pages:268-276

Links:	Volltext

DOI / URN:	10.2991/ijcis.2011.4.2.14

Katalog-ID:	SPR05459457X

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allfields	10.2991/ijcis.2011.4.2.14 doi (DE-627)SPR05459457X (SPR)ijcis.2011.4.2.14-e DE-627 ger DE-627 rakwb eng Gai, Shan verfasserin aut Banknote Image Retrieval Using Rotated Quaternion Wavelet Filters 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © the authors 2011 Abstract A new method of banknote image retrieval is proposed by using new set of rotated quaternion wavelet filters (RQWF) and standard quaternion wavelet transform (QWT) jointly. The robust and rotationally invariant features are extracted from QWT and RQWF decomposed sub-bands of banknote image. Three different sets of databases are used to demonstrate the effectiveness of the proposed method. The experimental results show that the proposed method improves the recognition rate from 78.79% to 91.22% on (16000 images) database D1, form 74.08% to 94.62% on (20000 images) database D2 and from 76.44% to 88.78% on (10000 images) database D3. The proposed method can also obtain a reasonable level of computational complexity. discrete wavelet transform (DWT) (dpeaa)DE-He213 complex wavelet transform (CWT) (dpeaa)DE-He213 quaternion wavelet transform (QWT) (dpeaa)DE-He213 rotated quaternion wavelet filters (RQWF) (dpeaa)DE-He213 feature extraction (dpeaa)DE-He213 support vector machine (SVM) (dpeaa)DE-He213 Liu, Peng aut Liu, Jiafeng aut Tang, Xianglong aut Enthalten in International journal of computational intelligence systems Paris : Atlantis Press, 2008 4(2011), 2 vom: 01. Apr., Seite 268-276 (DE-627)777781514 (DE-600)2754752-8 1875-6883 nnns volume:4 year:2011 number:2 day:01 month:04 pages:268-276 https://dx.doi.org/10.2991/ijcis.2011.4.2.14 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2011 2 01 04 268-276
spelling	10.2991/ijcis.2011.4.2.14 doi (DE-627)SPR05459457X (SPR)ijcis.2011.4.2.14-e DE-627 ger DE-627 rakwb eng Gai, Shan verfasserin aut Banknote Image Retrieval Using Rotated Quaternion Wavelet Filters 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © the authors 2011 Abstract A new method of banknote image retrieval is proposed by using new set of rotated quaternion wavelet filters (RQWF) and standard quaternion wavelet transform (QWT) jointly. The robust and rotationally invariant features are extracted from QWT and RQWF decomposed sub-bands of banknote image. Three different sets of databases are used to demonstrate the effectiveness of the proposed method. The experimental results show that the proposed method improves the recognition rate from 78.79% to 91.22% on (16000 images) database D1, form 74.08% to 94.62% on (20000 images) database D2 and from 76.44% to 88.78% on (10000 images) database D3. The proposed method can also obtain a reasonable level of computational complexity. discrete wavelet transform (DWT) (dpeaa)DE-He213 complex wavelet transform (CWT) (dpeaa)DE-He213 quaternion wavelet transform (QWT) (dpeaa)DE-He213 rotated quaternion wavelet filters (RQWF) (dpeaa)DE-He213 feature extraction (dpeaa)DE-He213 support vector machine (SVM) (dpeaa)DE-He213 Liu, Peng aut Liu, Jiafeng aut Tang, Xianglong aut Enthalten in International journal of computational intelligence systems Paris : Atlantis Press, 2008 4(2011), 2 vom: 01. Apr., Seite 268-276 (DE-627)777781514 (DE-600)2754752-8 1875-6883 nnns volume:4 year:2011 number:2 day:01 month:04 pages:268-276 https://dx.doi.org/10.2991/ijcis.2011.4.2.14 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2011 2 01 04 268-276
allfields_unstemmed	10.2991/ijcis.2011.4.2.14 doi (DE-627)SPR05459457X (SPR)ijcis.2011.4.2.14-e DE-627 ger DE-627 rakwb eng Gai, Shan verfasserin aut Banknote Image Retrieval Using Rotated Quaternion Wavelet Filters 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © the authors 2011 Abstract A new method of banknote image retrieval is proposed by using new set of rotated quaternion wavelet filters (RQWF) and standard quaternion wavelet transform (QWT) jointly. The robust and rotationally invariant features are extracted from QWT and RQWF decomposed sub-bands of banknote image. Three different sets of databases are used to demonstrate the effectiveness of the proposed method. The experimental results show that the proposed method improves the recognition rate from 78.79% to 91.22% on (16000 images) database D1, form 74.08% to 94.62% on (20000 images) database D2 and from 76.44% to 88.78% on (10000 images) database D3. The proposed method can also obtain a reasonable level of computational complexity. discrete wavelet transform (DWT) (dpeaa)DE-He213 complex wavelet transform (CWT) (dpeaa)DE-He213 quaternion wavelet transform (QWT) (dpeaa)DE-He213 rotated quaternion wavelet filters (RQWF) (dpeaa)DE-He213 feature extraction (dpeaa)DE-He213 support vector machine (SVM) (dpeaa)DE-He213 Liu, Peng aut Liu, Jiafeng aut Tang, Xianglong aut Enthalten in International journal of computational intelligence systems Paris : Atlantis Press, 2008 4(2011), 2 vom: 01. Apr., Seite 268-276 (DE-627)777781514 (DE-600)2754752-8 1875-6883 nnns volume:4 year:2011 number:2 day:01 month:04 pages:268-276 https://dx.doi.org/10.2991/ijcis.2011.4.2.14 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2011 2 01 04 268-276
allfieldsGer	10.2991/ijcis.2011.4.2.14 doi (DE-627)SPR05459457X (SPR)ijcis.2011.4.2.14-e DE-627 ger DE-627 rakwb eng Gai, Shan verfasserin aut Banknote Image Retrieval Using Rotated Quaternion Wavelet Filters 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © the authors 2011 Abstract A new method of banknote image retrieval is proposed by using new set of rotated quaternion wavelet filters (RQWF) and standard quaternion wavelet transform (QWT) jointly. The robust and rotationally invariant features are extracted from QWT and RQWF decomposed sub-bands of banknote image. Three different sets of databases are used to demonstrate the effectiveness of the proposed method. The experimental results show that the proposed method improves the recognition rate from 78.79% to 91.22% on (16000 images) database D1, form 74.08% to 94.62% on (20000 images) database D2 and from 76.44% to 88.78% on (10000 images) database D3. The proposed method can also obtain a reasonable level of computational complexity. discrete wavelet transform (DWT) (dpeaa)DE-He213 complex wavelet transform (CWT) (dpeaa)DE-He213 quaternion wavelet transform (QWT) (dpeaa)DE-He213 rotated quaternion wavelet filters (RQWF) (dpeaa)DE-He213 feature extraction (dpeaa)DE-He213 support vector machine (SVM) (dpeaa)DE-He213 Liu, Peng aut Liu, Jiafeng aut Tang, Xianglong aut Enthalten in International journal of computational intelligence systems Paris : Atlantis Press, 2008 4(2011), 2 vom: 01. Apr., Seite 268-276 (DE-627)777781514 (DE-600)2754752-8 1875-6883 nnns volume:4 year:2011 number:2 day:01 month:04 pages:268-276 https://dx.doi.org/10.2991/ijcis.2011.4.2.14 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2011 2 01 04 268-276
allfieldsSound	10.2991/ijcis.2011.4.2.14 doi (DE-627)SPR05459457X (SPR)ijcis.2011.4.2.14-e DE-627 ger DE-627 rakwb eng Gai, Shan verfasserin aut Banknote Image Retrieval Using Rotated Quaternion Wavelet Filters 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © the authors 2011 Abstract A new method of banknote image retrieval is proposed by using new set of rotated quaternion wavelet filters (RQWF) and standard quaternion wavelet transform (QWT) jointly. The robust and rotationally invariant features are extracted from QWT and RQWF decomposed sub-bands of banknote image. Three different sets of databases are used to demonstrate the effectiveness of the proposed method. The experimental results show that the proposed method improves the recognition rate from 78.79% to 91.22% on (16000 images) database D1, form 74.08% to 94.62% on (20000 images) database D2 and from 76.44% to 88.78% on (10000 images) database D3. The proposed method can also obtain a reasonable level of computational complexity. discrete wavelet transform (DWT) (dpeaa)DE-He213 complex wavelet transform (CWT) (dpeaa)DE-He213 quaternion wavelet transform (QWT) (dpeaa)DE-He213 rotated quaternion wavelet filters (RQWF) (dpeaa)DE-He213 feature extraction (dpeaa)DE-He213 support vector machine (SVM) (dpeaa)DE-He213 Liu, Peng aut Liu, Jiafeng aut Tang, Xianglong aut Enthalten in International journal of computational intelligence systems Paris : Atlantis Press, 2008 4(2011), 2 vom: 01. Apr., Seite 268-276 (DE-627)777781514 (DE-600)2754752-8 1875-6883 nnns volume:4 year:2011 number:2 day:01 month:04 pages:268-276 https://dx.doi.org/10.2991/ijcis.2011.4.2.14 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2011 2 01 04 268-276
language	English
source	Enthalten in International journal of computational intelligence systems 4(2011), 2 vom: 01. Apr., Seite 268-276 volume:4 year:2011 number:2 day:01 month:04 pages:268-276
sourceStr	Enthalten in International journal of computational intelligence systems 4(2011), 2 vom: 01. Apr., Seite 268-276 volume:4 year:2011 number:2 day:01 month:04 pages:268-276
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topic_facet	discrete wavelet transform (DWT) complex wavelet transform (CWT) quaternion wavelet transform (QWT) rotated quaternion wavelet filters (RQWF) feature extraction support vector machine (SVM)
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container_title	International journal of computational intelligence systems
authorswithroles_txt_mv	Gai, Shan @@aut@@ Liu, Peng @@aut@@ Liu, Jiafeng @@aut@@ Tang, Xianglong @@aut@@
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author	Gai, Shan
spellingShingle	Gai, Shan misc discrete wavelet transform (DWT) misc complex wavelet transform (CWT) misc quaternion wavelet transform (QWT) misc rotated quaternion wavelet filters (RQWF) misc feature extraction misc support vector machine (SVM) Banknote Image Retrieval Using Rotated Quaternion Wavelet Filters
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topic_title	Banknote Image Retrieval Using Rotated Quaternion Wavelet Filters discrete wavelet transform (DWT) (dpeaa)DE-He213 complex wavelet transform (CWT) (dpeaa)DE-He213 quaternion wavelet transform (QWT) (dpeaa)DE-He213 rotated quaternion wavelet filters (RQWF) (dpeaa)DE-He213 feature extraction (dpeaa)DE-He213 support vector machine (SVM) (dpeaa)DE-He213
topic	misc discrete wavelet transform (DWT) misc complex wavelet transform (CWT) misc quaternion wavelet transform (QWT) misc rotated quaternion wavelet filters (RQWF) misc feature extraction misc support vector machine (SVM)
topic_unstemmed	misc discrete wavelet transform (DWT) misc complex wavelet transform (CWT) misc quaternion wavelet transform (QWT) misc rotated quaternion wavelet filters (RQWF) misc feature extraction misc support vector machine (SVM)
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title	Banknote Image Retrieval Using Rotated Quaternion Wavelet Filters
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title_full	Banknote Image Retrieval Using Rotated Quaternion Wavelet Filters
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title_sort	banknote image retrieval using rotated quaternion wavelet filters
title_auth	Banknote Image Retrieval Using Rotated Quaternion Wavelet Filters
abstract	Abstract A new method of banknote image retrieval is proposed by using new set of rotated quaternion wavelet filters (RQWF) and standard quaternion wavelet transform (QWT) jointly. The robust and rotationally invariant features are extracted from QWT and RQWF decomposed sub-bands of banknote image. Three different sets of databases are used to demonstrate the effectiveness of the proposed method. The experimental results show that the proposed method improves the recognition rate from 78.79% to 91.22% on (16000 images) database D1, form 74.08% to 94.62% on (20000 images) database D2 and from 76.44% to 88.78% on (10000 images) database D3. The proposed method can also obtain a reasonable level of computational complexity. © the authors 2011
abstractGer	Abstract A new method of banknote image retrieval is proposed by using new set of rotated quaternion wavelet filters (RQWF) and standard quaternion wavelet transform (QWT) jointly. The robust and rotationally invariant features are extracted from QWT and RQWF decomposed sub-bands of banknote image. Three different sets of databases are used to demonstrate the effectiveness of the proposed method. The experimental results show that the proposed method improves the recognition rate from 78.79% to 91.22% on (16000 images) database D1, form 74.08% to 94.62% on (20000 images) database D2 and from 76.44% to 88.78% on (10000 images) database D3. The proposed method can also obtain a reasonable level of computational complexity. © the authors 2011
abstract_unstemmed	Abstract A new method of banknote image retrieval is proposed by using new set of rotated quaternion wavelet filters (RQWF) and standard quaternion wavelet transform (QWT) jointly. The robust and rotationally invariant features are extracted from QWT and RQWF decomposed sub-bands of banknote image. Three different sets of databases are used to demonstrate the effectiveness of the proposed method. The experimental results show that the proposed method improves the recognition rate from 78.79% to 91.22% on (16000 images) database D1, form 74.08% to 94.62% on (20000 images) database D2 and from 76.44% to 88.78% on (10000 images) database D3. The proposed method can also obtain a reasonable level of computational complexity. © the authors 2011
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title_short	Banknote Image Retrieval Using Rotated Quaternion Wavelet Filters
url	https://dx.doi.org/10.2991/ijcis.2011.4.2.14
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author2	Liu, Peng Liu, Jiafeng Tang, Xianglong
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up_date	2024-07-04T02:18:56.446Z
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Banknote Image Retrieval Using Rotated Quaternion Wavelet Filters

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