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
Autor*in: |
Gai, Shan [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2011 |
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Schlagwörter: |
discrete wavelet transform (DWT) complex wavelet transform (CWT) quaternion wavelet transform (QWT) |
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Anmerkung: |
© the authors 2011 |
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Übergeordnetes Werk: |
Enthalten in: International journal of computational intelligence systems - Paris : Atlantis Press, 2008, 4(2011), 2 vom: 01. Apr., Seite 268-276 |
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Übergeordnetes Werk: |
volume:4 ; year:2011 ; number:2 ; day:01 ; month:04 ; pages:268-276 |
Links: |
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DOI / URN: |
10.2991/ijcis.2011.4.2.14 |
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SPR05459457X |
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700 | 1 | |a Tang, Xianglong |4 aut | |
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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 |
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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 |
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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 |
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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 |
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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 |
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Gai, Shan |
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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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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 |
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Banknote Image Retrieval Using Rotated Quaternion Wavelet Filters |
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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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Banknote Image Retrieval Using Rotated Quaternion Wavelet Filters |
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