WO2007105891A1 - Reconnaissance de la coupure d'un billet de banque par transformée en ondelettes - Google Patents

Reconnaissance de la coupure d'un billet de banque par transformée en ondelettes Download PDF

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Publication number
WO2007105891A1
WO2007105891A1 PCT/KR2007/001195 KR2007001195W WO2007105891A1 WO 2007105891 A1 WO2007105891 A1 WO 2007105891A1 KR 2007001195 W KR2007001195 W KR 2007001195W WO 2007105891 A1 WO2007105891 A1 WO 2007105891A1
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WO
WIPO (PCT)
Prior art keywords
note
wavelet transform
sub
wavelet
denomination
Prior art date
Application number
PCT/KR2007/001195
Other languages
English (en)
Inventor
Eui Sun Choi
Original Assignee
Nautilus Hyosung Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nautilus Hyosung Inc. filed Critical Nautilus Hyosung Inc.
Publication of WO2007105891A1 publication Critical patent/WO2007105891A1/fr

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Classifications

    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07D—HANDLING OF COINS OR VALUABLE PAPERS, e.g. TESTING, SORTING BY DENOMINATIONS, COUNTING, DISPENSING, CHANGING OR DEPOSITING
    • G07D7/00—Testing specially adapted to determine the identity or genuineness of valuable papers or for segregating those which are unacceptable, e.g. banknotes that are alien to a currency
    • G07D7/20—Testing patterns thereon
    • G07D7/202—Testing patterns thereon using pattern matching
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/52—Scale-space analysis, e.g. wavelet analysis
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07D—HANDLING OF COINS OR VALUABLE PAPERS, e.g. TESTING, SORTING BY DENOMINATIONS, COUNTING, DISPENSING, CHANGING OR DEPOSITING
    • G07D7/00—Testing specially adapted to determine the identity or genuineness of valuable papers or for segregating those which are unacceptable, e.g. banknotes that are alien to a currency
    • G07D7/06—Testing specially adapted to determine the identity or genuineness of valuable papers or for segregating those which are unacceptable, e.g. banknotes that are alien to a currency using wave or particle radiation
    • G07D7/12—Visible light, infrared or ultraviolet radiation

Definitions

  • the present invention relates to a method of recognizing the denomination of a note using wavelet transform, and more particularly, to a method of recognizing a note using a feature vector that is configured by dividing an inputted note image into blocks, performing wavelet transform for each of the blocks, and selecting the entire wavelet transformed image or a block where the performance of recognizing the denomination of the note is maximized.
  • one-dimensional data array or image are obtained from a note using a plurality of single element type transmissive and reflective optical sensors, and an image pattern of the note is examined and compared with patterns of other notes in terms of specific regions that are considered to have a lot of distinguishable factors (template method). That is, the denomination of a note is identified by comparing image patterns with one another in terms of a set of specific regions (a template) or by measuring the size of the note.
  • position information on local regions in a state where a given note (a front face or a rear face of the note) is upside down becomes an important variable in determining the denomination of the note.
  • a feature vector is extracted by performing wavelet transform for an original note image as it is
  • features are extracted after dividing a wavelet transformed sub-band region into m x n cells, thereby restrictively reflecting position information on local regions of feature points.
  • a note image is divided into a certain number of blocks, and wavelet features are extracted from each of the divided images.
  • the present invention is conceived to solve the aforementioned problems.
  • the present invention enables reduction in processing time required for recognition of the denomination of a note to be identified and correct recognition of the note even though the note is replaced by a new type of note, and makes it easy to identify all kinds of notes quite precisely, using a feature vector that is configured by dividing an inputted note image into blocks, performing wavelet transform for each of the blocks, and selecting the entire wavelet transformed image or a block where the performance of recognizing the denomination of the note is maximized.
  • a method of recognizing a denomination of a note using wavelet transform comprises the steps of dividing an original image created by scanning the note into a predetermined number of blocks, i.e., M x N blocks, in horizontal and vertical directions; performing wavelet transform for each of the divided images by a predetermined number of times; obtaining wavelet coefficients for each of sub-bands created through the wavelet transform, and dividing each of the sub-bands into a predetermined number of cells, i.e., m x n cells, in the horizontal and vertical directions; comparing a magnitude of an absolute value of each of the wavelet coefficients with a reference value for each of the m x n cells, and extracting the number of wavelet coefficients with the magnitudes larger than or equal to the reference value; completing the extracting step for each of the sub-bands, and configuring a feature vector with a vertical structure; and recognizing the denomination of the note by measuring similarity between the feature vector and
  • the present invention relates to a method of recognizing the denomination of a note using wavelet transform, which enables correct recognition of a note even though the note to be identified is replaced by a new type of note, and makes it easy to identify all kinds of notes quite precisely, using a feature vector that is configured by dividing an inputted note image into blocks, performing wavelet transform for each of the blocks, and selecting the entire wavelet transformed image or a block where the performance of recognizing the denomination of the note is maximized.
  • a note can be stably recognized by excluding error factors such as the degree of contamination of the note or changes in the performance of an object included in a note identifying apparatus.
  • Fig. 1 shows a typical wavelet transform process.
  • Fig. 2 shows results of a general two-dimensional wavelet transform performed on an image.
  • FIG. 3 is a flowchart illustrating a method of recognizing the denomination of a note using wavelet transform according to the present invention.
  • FIG. 4 shows an example of an original image created according to the present invention.
  • Fig. 5 shows results of dividing an image into 2 x 2 blocks.
  • FIG. 6 shows results of performing wavelet transform for a first block image twice according to the present invention.
  • Fig. 7 shows an example in which a d2 sub-band in the first block image is divided into m x n cells according to the present invention.
  • Fig. 8 exemplarily shows a process of extracting a feature vector of the d2 sub-band in the first block image according to the present invention.
  • Fig. 9 exemplarily shows a process of extracting a feature vector by performing wavelet transform for the first block image twice according to the present invention.
  • Fig. 10 shows a process of extracting a feature vector according to the present invention.
  • the wavelet transform is a process of reconfiguring a signal into very simple basic functions. That is, the wavelet transform can be considered as a method of decomposing data, a function, or an operator into different frequency components and examining each of the components associated with a resolution corresponding to each scale.
  • the fundamental principle of the wavelet transform is similar to that of the Fourier analysis.
  • the use of the wavelet transform for signal processing can restore a weak signal mixed with noise.
  • the wavelet transform is different in that a narrow window is used for a high frequency bandwidth and a wide window is used for a low frequency bandwidth.
  • the wavelet transform has been proved particularly to be useful for X-ray or magnetic resonance image processing in the medical field. An image processed in such a method can be processed clearly without a blur in its details. In addition, since the wavelet transform exactly reflects the fact that a person first recognizes an overall outline of a thing and then gradually concentrates on detailed portions, the wavelet transform is suitable for image processing.
  • LPF low band pass filters
  • HPF high band pass filters
  • the sub-bands LL, LH, HL, and HH are differentiated according to the filters applied to the image as shown in Fig. 1.
  • the LL sub-band 110 comprises coefficients in which high frequency components are excluded from the image by applying low pass filters to the original image in the horizontal and vertical directions.
  • the HH sub-band 140 for which high pass filters are applied to the original image in the horizontal and vertical directions contains only high frequency components contrary to the LL sub-band 110.
  • the HL sub-band 130 for which a high pass filter is applied in the vertical direction contains error components of a frequency in the vertical direction
  • the LH sub-band 120 for which a high pass filter is applied in the horizontal direction contains error components of a frequency in the horizontal direction.
  • the LH sub-band and the HL sub-band have an effect obtained by employing edge detection for horizontal and vertical components from the original image
  • the HH sub-band has an effect obtained by employing edge detection for diagonal components.
  • the wavelet transform is recursively performed over a plurality of steps, not limited to a first transform, and thus, the respective steps have different resolutions (multi-resolutions) and frequency characteristics (scalability).
  • a specific example of the two-dimensional wavelet transform will be explained with reference to Fig. 2.
  • Four sub-bands shown in Fig. 2 (b) (hereinafter, referred to as LL region 220, HL region 222, LH region, 224, and HH region 226) are created by performing wavelet transform once.
  • LL region 220 If the LL region 220 is subjected once more to wavelet transform, four sub-bands of LL region 230, LH2 region, 234, HL2 region 232, and HH2 region 236 are created, and thus, the original image is decomposed into total seven sub-bands (LL 230, LHl 224, HLl 222, HHl 226, LH2 234, HL2 232, and HH2 236).
  • LL region contains important information on the image.
  • the decomposition is repeated as many times as a certain decomposition value until desired information is obtained.
  • the LL regions 220, 230, 240 and 250 are recursively decomposed, thereby obtaining a new processing target image.
  • the wavelet transform is repeated as many times as the decomposition value to reduce the width of a low frequency bandwidth, and thus, a doubled spatial resolution is obtained.
  • the decomposition value provided as a criterion for determining the number of recursive wavelet transforms is determined as an appropriate value in consideration of a loss of information and the size of a feature vector.
  • high speed Haar wavelet transform is used to extract features from a note image.
  • the method of recognizing the denomination of a note according to the present invention allows any kind of wavelet transform to be used.
  • the high speed Haar wavelet transform is advantageous in expressing a wide area of continuous colors, easy to implement, and speedy in transforming.
  • the high speed Haar wavelet transform satisfies conditions of uniqueness, completeness, invariance, sensitivity and abstraction that are characteristics of a system for expressing an image.
  • FIG. 3 is a flowchart illustrating a method of recognizing the denomination of a note using wavelet transform according to the present invention.
  • a note desired to be recognized is first scanned (S310) so that an original image 410 is created by as shown in Fig. 4.
  • the original image 410 is divided into M x N blocks in horizontal and vertical directions as shown in Fig. 5.
  • the division of the original image into blocks is to divide the image into non-overlapped rectangular fragments, which means division of the image into a predetermined number of blocks, i.e., M x N blocks, in the horizontal and vertical directions.
  • the preprocessed original image is divided into blocks and subjected to wavelet transform, further more information can be obtained as compared with performing wavelet transform for the entire image, thereby obtaining further correct information. Since only important blocks are selected and processed, processing time required for recognition of the denomination of a note can be reduced. For example, as shown in Fig. 5, if the preprocessed original image is divided into blocks by setting M to 2 and N to 2, the image is divided into two cells in the horizontal direction and into two cells in the vertical direction, resulting in total four blocks 510, 512, 514 and 516.
  • each of the blocked images is wavelet transformed as many times as a decomposition value (S330).
  • the wavelet transform is to apply conventional wavelet transform to recognition of a note. If each of the blocked images is wavelet transformed, LL, LH, HL and HH regions are created. Next, the LL region is recursively wavelet transformed as many times as the certain decomposition value, resulting in created LH, HL and HH regions.
  • the recursive wavelet transforms of the LL region are performed as many times as the decomposition value.
  • reference data that are stored as representative feature vectors by denomination are compared with vectors of a given note image so as to determine whether such feature vectors can be clearly identified. That is, corre- spondence between two vectors is calculated through one-to-one comparison of the reference data with a given feature vector, and the LL region is then recursively wavelet transformed several times until each note can be identified according to the results of the calculation.
  • the first block image 510 is decomposed into LL region 520, LH region 524, HL region 522, and HH region 526 through the first wavelet transform as shown in Fig. 6 (b).
  • the LL region 520 is decomposed once more through the second wavelet transform, LL region 530, LHl region 524, HLl region 522, HHl region 526, LH2 region 534, HL2 region 532 and HH2 region 536 are created as shown in Fig. 6 (c).
  • the decomposition value is set to 'J level'.
  • the first block image 510 is decomposed into seven (3 x 2 + 1) regions, and six regions of dl region 522, d2 region 524, d3 region 526, d4 region 532, d5 region 534, and d6 region 536 can be obtained.
  • each of the regions dl to d6 (when J is two) is configured with wavelet coefficients having features of each band using differences in information of neighboring regions (S340).
  • the wavelet coefficient the LL region 530 for which low pass filters are applied in the horizontal and vertical directions is configured with approximate coefficient values of the image, and d5 region 534 and d2 region 524, i.e., LH regions for which a high pass filter is applied in the horizontal direction, are respectively configured with coefficient values of horizontal components of the image.
  • d4 region 532 and dl region 522 are respectively configured with coefficient values of vertical components of the image
  • d6 region 536 and d3 region 526 are respectively configured with coefficient values of diagonal components of the image.
  • the feature vector is a vector value configured by comparing the magnitudes of the absolute values of the extracted wavelet coefficients with a predetermined reference value. For example, obtaining feature vectors of the first block image, i.e., d2 region 524, will be described. First, d2 region 524 is divided into m x n cells in the horizontal and vertical directions as shown in Fig. 7 (S350). For the respective m x n cells, the magnitudes of the absolute values of wavelet coefficients are compared with a reference value (S360) to extract the number of wavelet coefficients of which the magnitudes are equal to or larger than the reference value, thereby configuring a first vector (S370).
  • S360 reference value
  • d2 region 524 is divided into m x n (l l x 4) cells as shown in Fig. 8, and the number of coefficients of which the magnitudes are larger than the reference value 7 is counted among coefficients in first cell mini 810. That is, it is understood that the number of coefficients of which the magnitudes are larger than the reference value 7 is 35 in cell mini 810 comprising coefficients of ⁇ 5, 7, 9, 10, 11, 9, ..., 9, 9, 8, 7, 7, 6 ⁇ , and the number 35 becomes a feature vector 812 of cell mini.
  • Fig. 9 shows values obtained when J of a block is 2. If the values are expressed as a formula, a feature vector having a vertical structure of total MN3Jmn bits, i.e. number of blocks in a row (M) x number of blocks in a column (N) x 3 x wavelet transform level (J) x number of cells in a row (m) x number of cells in a column (n), is extracted as shown in Fig. 10.
  • each of the aforementioned M x N (3 x J + 1) wavelet sub-band regions is another expression of a specific frequency resolution for the image of a corresponding block.
  • division of a sub-band region into m x n cells implies that the image of each block is divided into m x n portions. Accordingly, by extracting a feature vector on a block combination basis and on a cell basis, position information on local regions of feature points in the entire image can be maximally expressed.
  • the present invention has been described in connection with a method of recognizing a typical note.
  • the present invention is not limited to the recognition of such a typical note but can be applied to recognition of general types of notes having certain printed patterns or watermark images, such as securities, a variety of paper money guaranties, and checks. Therefore, the scope of the present invention should not be defined by the embodiment described above but apparently defined by the invention of the appended claims and equivalents thereof.
  • the denomination of a note can be recognized using a feature vector that is configured by dividing a note image into blocks, performing wavelet transform for each of the blocks, and selecting the entire wavelet transformed image or a block where the performance of recognizing the denomination of the note is maximized.
  • the present invention can be widely applied to recognition of not only typical notes but also general types of notes having certain printed patterns or watermark images, such as securities, a variety of paper money guaranties, and checks.

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  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Toxicology (AREA)
  • Multimedia (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Image Analysis (AREA)
  • Inspection Of Paper Currency And Valuable Securities (AREA)

Abstract

L'invention concerne un procédé de reconnaissance de la coupure d'un billet de banque, et plus particulièrement, un procédé de reconnaissance d'un billet de banque faisant intervenir un vecteur d'attributs configuré par: division d'une image de billet de banque en blocs; application d'une transformée en ondelettes à chacun des blocs; et sélection de la totalité de l'image transformée par ondelettes ou d'un seul bloc dans lequel les résultats de la reconnaissance de la coupure du billet de banque sont optimisés. Un procédé de reconnaissance d'une coupure de billet de banque consiste à: diviser une image originale en un nombre prédéterminé de blocs; appliquer une transformée en ondelettes à chacune des images divisées; obtenir des coefficients d'ondelettes pour chacune des sous-bandes, et diviser chacune des sous-bandes en un nombre prédéterminé de cellules; comparer une grandeur d'une valeur absolue, et extraire le nombre de coefficients d'ondelettes; achever l'opération d'extraction pour chacune des sous-bandes; et enfin, reconnaître la coupure du billet de banque.
PCT/KR2007/001195 2006-03-13 2007-03-12 Reconnaissance de la coupure d'un billet de banque par transformée en ondelettes WO2007105891A1 (fr)

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KR1020060023052A KR101056666B1 (ko) 2006-03-13 2006-03-13 웨이블렛 변환을 이용한 권종인식방법
KR10-2006-0023052 2006-03-13

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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2557523A1 (fr) * 2010-04-08 2013-02-13 Obshhestvo S Ogranichennoj Otvetstvennost'ju "Konstruktorskoe Bjuro "Dors" (OOO "KB "Dors") Procédé de classification de billets de banque
US8781204B2 (en) 2007-06-01 2014-07-15 Kba-Notasys Sa Authentication of security documents, in particular of banknotes
EP2902976A4 (fr) * 2012-09-29 2015-09-02 Grg Banking Equipment Co Ltd Procédé de génération de caractéristique anti-contrefaçon pour un document de valeur et procédé et dispositif d'authentification correspondants
CN107331031A (zh) * 2017-07-04 2017-11-07 深圳怡化电脑股份有限公司 一种纸币币值的识别方法、装置、设备及存储介质
CN107895424A (zh) * 2017-10-31 2018-04-10 深圳怡化电脑股份有限公司 一种纸币的识别方法、系统及金融终端

Families Citing this family (3)

* Cited by examiner, † Cited by third party
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KR101232684B1 (ko) * 2011-01-21 2013-02-13 노틸러스효성 주식회사 베이시안 접근법을 이용한 지폐 진위 감별 방법
KR101232683B1 (ko) * 2011-01-21 2013-02-13 노틸러스효성 주식회사 베이시안 접근법을 이용한 권종 인식 방법
WO2012099435A2 (fr) * 2011-01-21 2012-07-26 노틸러스효성 주식회사 Procédé de différenciation de billets de banque grâce à une approche bayésienne

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US4611345A (en) * 1983-04-06 1986-09-09 Glory Kogyo Kabushiki Kaisha Bank bill identification device
US6634482B1 (en) * 2001-01-24 2003-10-21 Robert J. Miele Bill and coin checker
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US6883707B2 (en) * 2003-01-08 2005-04-26 Glory Ltd. Bill serial number reading device and bill serial number reading method

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US4611345A (en) * 1983-04-06 1986-09-09 Glory Kogyo Kabushiki Kaisha Bank bill identification device
US6634482B1 (en) * 2001-01-24 2003-10-21 Robert J. Miele Bill and coin checker
JP2004213559A (ja) * 2003-01-08 2004-07-29 Glory Ltd 紙幣記番号読取装置および紙幣記番号読取方法
US6883707B2 (en) * 2003-01-08 2005-04-26 Glory Ltd. Bill serial number reading device and bill serial number reading method

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8781204B2 (en) 2007-06-01 2014-07-15 Kba-Notasys Sa Authentication of security documents, in particular of banknotes
EP2557523A1 (fr) * 2010-04-08 2013-02-13 Obshhestvo S Ogranichennoj Otvetstvennost'ju "Konstruktorskoe Bjuro "Dors" (OOO "KB "Dors") Procédé de classification de billets de banque
EP2557523A4 (fr) * 2010-04-08 2014-04-23 Obshhestvo S Ogranichennoj Otvetstvennost Ju Kb Dors Ooo Kb Dors Procédé de classification de billets de banque
EP2902976A4 (fr) * 2012-09-29 2015-09-02 Grg Banking Equipment Co Ltd Procédé de génération de caractéristique anti-contrefaçon pour un document de valeur et procédé et dispositif d'authentification correspondants
US9499006B2 (en) 2012-09-29 2016-11-22 Grg Banking Equipment Co., Ltd. Anti-counterfeiting feature generation method for valuable document and authentication method and device therefor
AU2013325049B2 (en) * 2012-09-29 2016-12-01 Grg Banking Equipment Co., Ltd. Anti-counterfeiting feature generation method for valuable document and authentication method and device therefor
CN107331031A (zh) * 2017-07-04 2017-11-07 深圳怡化电脑股份有限公司 一种纸币币值的识别方法、装置、设备及存储介质
CN107331031B (zh) * 2017-07-04 2019-10-11 深圳怡化电脑股份有限公司 一种纸币币值的识别方法、装置、设备及存储介质
CN107895424A (zh) * 2017-10-31 2018-04-10 深圳怡化电脑股份有限公司 一种纸币的识别方法、系统及金融终端

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KR101056666B1 (ko) 2011-08-12

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