WO2007105892A1 - Recognizing the denomination of a note using wavelet transform - Google Patents

Recognizing the denomination of a note using wavelet transform Download PDF

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Publication number
WO2007105892A1
WO2007105892A1 PCT/KR2007/001196 KR2007001196W WO2007105892A1 WO 2007105892 A1 WO2007105892 A1 WO 2007105892A1 KR 2007001196 W KR2007001196 W KR 2007001196W WO 2007105892 A1 WO2007105892 A1 WO 2007105892A1
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Prior art keywords
note
wavelet transform
sub
wavelet
bands
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PCT/KR2007/001196
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French (fr)
Inventor
Eui Sun Choi
Jong Seok Lee
Joon Hyun Yoon
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Nautilus Hyosung Inc.
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Publication of WO2007105892A1 publication Critical patent/WO2007105892A1/en

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    • GPHYSICS
    • G07CHECKING-DEVICES
    • G07DHANDLING OF COINS OR VALUABLE PAPERS, e.g. TESTING, SORTING BY DENOMINATIONS, COUNTING, DISPENSING, CHANGING OR DEPOSITING
    • G07D7/00Testing 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
    • G07D7/20Testing patterns thereon
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00Arrangements for image or video recognition or understanding
    • G06V10/40Extraction of image or video features
    • G06V10/52Scale-space analysis, e.g. wavelet analysis
    • GPHYSICS
    • G07CHECKING-DEVICES
    • G07DHANDLING OF COINS OR VALUABLE PAPERS, e.g. TESTING, SORTING BY DENOMINATIONS, COUNTING, DISPENSING, CHANGING OR DEPOSITING
    • G07D7/00Testing 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
    • G07D7/06Testing 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
    • G07D7/12Visible light, infrared or ultraviolet radiation
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20048Transform domain processing
    • G06T2207/20064Wavelet transform [DWT]

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 performing wavelet transform for an inputted note image by a predetermined number of times and comparing the magnitudes of the absolute values of wavelet coefficients with a reference value.
  • 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.
  • the present invention is conceived to solve the aforementioned problems.
  • the present invention 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 performing wavelet transform for an inputted note image by a predetermined number of times and comparing the magnitudes of the absolute values of wavelet coefficients with a reference value.
  • a method of recognizing the denomination of a note using wavelet transform comprises the steps of scanning the note to create an original image thereof, and performing the wavelet transform for the original image 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 m x n cells in 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 a predetermined determination criterion vector.
  • 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. [13] Basic operations of the wavelet transform are applied to discrete signals having n samples. A pair of filters are applied to a signal to separate the signal into a low frequency bandwidth and a high frequency bandwidth. Since each of the bandwidths is sub-sampled with a factor of 2, it contains n/2 samples.
  • 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).
  • the 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.
  • the present invention 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 performing wavelet transform for an inputted note image by a predetermined number of times and comparing the magnitudes of the absolute values of wavelet coefficients with a reference value.
  • 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 performing wavelet transform for the original image twice according to the present invention.
  • Fig. 6 shows an example in which a d2 sub-band is divided into m x n cells according to the present invention.
  • Fig. 7 exemplarily shows a process of extracting a feature vector of the d2 sub-band according to the present invention.
  • Fig. 8 exemplarily shows a process of extracting a feature vector by performing wavelet transform twice according to the present invention.
  • Fig. 9 shows a process of extracting a feature vector according to the present invention.
  • 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 then wavelet transformed as many times by a decomposition value (S320).
  • the wavelet transform is to apply conventional wavelet transform to recognition of a note. If the original image 410 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, correspondence 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 original image 410 is decomposed into LL region 510, LH region 512, HL region 514, and HH region 516 through the first wavelet transform as shown in Fig. 5 (b).
  • the LL region 510 is decomposed once more through the second wavelet transform, LL region 520, LHl region 521, HLl region 522, HHl region 523, LH2 region 524, HL2 region 525 and HH2 region 526 are created as shown in Fig. 5 (c).
  • the decomposition value is set to 'J level'.
  • the original image 410 is decomposed into seven (3 x 2 + 1) regions, and six regions of dl region 524, d2 region 525, d3 region 526, d4 region 521, d5 region 522, and d6 region 523 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 (S330).
  • the wavelet coefficient the LL region 520 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 522 and d2 region 525, 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 521 and dl region 524 i.e., HL regions for which a high pass filter is applied in the vertical direction
  • d6 region 523 and d3 region 526 i.e., HH regions for which high pass filters are applied in the horizontal and vertical directions, are respectively configured with coefficient values of diagonal components of the image.
  • a feature vector is configured according to each of the 3 J regions (e.g., regions dl to d6 if J level is two) created by the wavelet transform. At this time, all of the 3J regions do not need to participate in configuring the feature vectors. Similarily to determining the decomposition value of the wavelet transform, it is possible to determine the positions and number of regions for maximizing the performance of recognition of the denomination of a note. In this case, an optimal combination of the regions can be obtained by examining the recognition performance of all combinations of the regions (full search, binary tree search, and the like).
  • 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 d2 region 525 will be described. First, d2 region 525 is divided into m x n cells in the horizontal and vertical directions as shown in Fig. 6 (S340). For the respective m x n cells, the magnitudes of the absolute values of wavelet coefficients are compared with a reference value (S350) 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 (S360).
  • S350 a reference value
  • d2 region 525 is divided into m x n (l l x 4) cells as shown in Fig. 7, and the number of coefficients of which the magnitudes are larger than the reference value 7 is counted among coefficients in first cell mini 710. 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 710 comprising coefficients of ⁇ 5, 7, 9, 10, 11, 9, ..., 9, 9, 8, 7, 7, 6 ⁇ , and the number 35 becomes a feature vector 712 of cell mini.
  • Fig. 8 shows values obtained when J is 2. If the values are expressed as a formula, a feature vector having a vertical structure of total 3Jmn bits, i.e. 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. 9.
  • each of the aforementioned 3 x J + 1 wavelet sub-band regions is another expression of a specific frequency resolution for the entire image.
  • division of a sub-band region into m x n cells implies that the entire image is divided into m x n portions. Accordingly, by extracting a feature vector on a cell basis, information on the position of a feature point in the entire image can be expressed as a feature vector.
  • the present invention is applicable to a technique that 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 performing wavelet transform for an inputted note image by a predetermined number of times and comparing the magnitudes of the absolute values of wavelet coefficients with a reference value.
  • the present invention is applicable to a technique by which 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.

Abstract

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 performing wavelet transform for an inputted note image by a predetermined number of times and comparing the magnitudes of the absolute values of wavelet coefficients with a reference value. A method of recognizing a denomination of a note using wavelet transform according to the present invention comprises the steps of scanning the note and performing the wavelet transform for the original image obtaining wavelet coefficients for each of sub-bands and dividing each of the sub-bands into m x n cells comparing a magnitude of an absolute value and extracting the number of wavelet coefficients completing the extracting step for each of the sub-bands, and recognizing the denomination of the note by measuring similarity.

Description

Description
RECOGNIZING THE DENOMINATION OF A NOTE USING
WAVELET TRANSFORM
Technical Field
[1] 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 performing wavelet transform for an inputted note image by a predetermined number of times and comparing the magnitudes of the absolute values of wavelet coefficients with a reference value.
[2]
Background Art
[3] In a conventional method of identifying a note, one-dimensional data
(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.
[4] However, in the method of comparing and examining surface patterns of specific regions, if the type of a note to be identified is changed or a note is replaced by a new type of note, the contents and position of an existing template should be changed. Further, the contents and position of an existing template should be also changed if the position or orientation of a light emitting device or a light receiving device is adjusted. Accordingly, the design of an apparatus employing the method should be frequently changed.
[5] Furthermore, since the type of a note is identified only through the surface pattern scheme in the conventional method, it is impossible to determine the position of a portion to be examined so that optimized surface patterns can be obtained for all kinds of notes. This makes it difficult to distinguish all kinds of notes quite precisely.
[6]
Disclosure of Invention
Technical Problem
[7] The present invention is conceived to solve the aforementioned problems. The present invention 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 performing wavelet transform for an inputted note image by a predetermined number of times and comparing the magnitudes of the absolute values of wavelet coefficients with a reference value.
[8]
Technical Solution
[9] To achieve the object described above, a method of recognizing the denomination of a note using wavelet transform according to the present invention comprises the steps of scanning the note to create an original image thereof, and performing the wavelet transform for the original image 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 m x n cells in 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 a predetermined determination criterion vector.
[10] The outline of general wavelet transform will be described before describing the present invention.
[11] 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. However, compared with the Fourier analysis in which filters with the same size are used for all frequency bandwidths, 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.
[12] 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. [13] Basic operations of the wavelet transform are applied to discrete signals having n samples. A pair of filters are applied to a signal to separate the signal into a low frequency bandwidth and a high frequency bandwidth. Since each of the bandwidths is sub-sampled with a factor of 2, it contains n/2 samples.
[14] An example of the wavelet transform will be explained with reference to Fig. 1.
When signals are extracted from an image using low band pass filters (LPF) and high band pass filters (HPF) in spatial X- and Y-axis directions and then subjected to wavelet transform, four sub-bands LL 110, LH 120, HL 130 and HH 140 are created for each frequency bandwidth.
[15] At this time, 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.
[16] 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, and 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, and the HH sub-band has an effect obtained by employing edge detection for diagonal components.
[17] Then, 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. 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).
[18] The reason why the LL regions 220, 230, 240 and 250 are decomposed is that each
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. [19] 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.
[20] In the present invention, 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. In addition, 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.
[21]
Advantageous Effects
[22] The present invention 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 performing wavelet transform for an inputted note image by a predetermined number of times and comparing the magnitudes of the absolute values of wavelet coefficients with a reference value. [23] Further, 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. [24]
Brief Description of the Drawings [25] Fig. 1 shows a typical wavelet transform process.
[26] Fig. 2 shows results of a general two-dimensional wavelet transform performed on an image. [27] Fig. 3 is a flowchart illustrating a method of recognizing the denomination of a note using wavelet transform according to the present invention. [28] Fig. 4 shows an example of an original image created according to the present invention. [29] Fig. 5 shows results of performing wavelet transform for the original image twice according to the present invention. [30] Fig. 6 shows an example in which a d2 sub-band is divided into m x n cells according to the present invention. [31] Fig. 7 exemplarily shows a process of extracting a feature vector of the d2 sub-band according to the present invention.
[32] Fig. 8 exemplarily shows a process of extracting a feature vector by performing wavelet transform twice according to the present invention.
[33] Fig. 9 shows a process of extracting a feature vector according to the present invention.
[34]
Best Mode for Carrying Out the Invention
[35] Fig. 3 is a flowchart illustrating a method of recognizing the denomination of a note using wavelet transform according to the present invention.
[36] In order to recognize the demolition of a note 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 then wavelet transformed as many times by a decomposition value (S320).
[37] The wavelet transform is to apply conventional wavelet transform to recognition of a note. If the original image 410 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.
[38] The recursive wavelet transforms of the LL region are performed as many times as the decomposition value. In order to determine an appropriate decomposition value in case of the note recognition, 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, correspondence 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.
[39] For example, if the wavelet transform is repeated twice as shown in Fig. 5, the original image 410 is decomposed into LL region 510, LH region 512, HL region 514, and HH region 516 through the first wavelet transform as shown in Fig. 5 (b). If the LL region 510 is decomposed once more through the second wavelet transform, LL region 520, LHl region 521, HLl region 522, HHl region 523, LH2 region 524, HL2 region 525 and HH2 region 526 are created as shown in Fig. 5 (c). Hereinafter, description will be made in connection with an example in which the decomposition value is set to 'J level'.
[40] An example in which the wavelet transforms are applied to recognition of a note up to J level as the decomposition value will be described in detail. When the original image 410 is wavelet transformed as many times as the decomposition value, it is decomposed into 3 J + 1 regions. Through the decomposition, 3 J (dl to d3j) regions can be obtained, except the LL region 520 that is created in the last transform. For example, if the J level is set to two, as shown in Fig. 5 (c), the original image 410 is decomposed into seven (3 x 2 + 1) regions, and six regions of dl region 524, d2 region 525, d3 region 526, d4 region 521, d5 region 522, and d6 region 523 can be obtained.
[41] If the wavelet transforms are completed as many times as the value of J level, 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 (S330). As for the wavelet coefficient, the LL region 520 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 522 and d2 region 525, 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.
[42] In addition, d4 region 521 and dl region 524, i.e., HL regions for which a high pass filter is applied in the vertical direction, are respectively configured with coefficient values of vertical components of the image, and d6 region 523 and d3 region 526, i.e., HH regions for which high pass filters are applied in the horizontal and vertical directions, are respectively configured with coefficient values of diagonal components of the image.
[43] Next, a feature vector is configured according to each of the 3 J regions (e.g., regions dl to d6 if J level is two) created by the wavelet transform. At this time, all of the 3J regions do not need to participate in configuring the feature vectors. Similarily to determining the decomposition value of the wavelet transform, it is possible to determine the positions and number of regions for maximizing the performance of recognition of the denomination of a note. In this case, an optimal combination of the regions can be obtained by examining the recognition performance of all combinations of the regions (full search, binary tree search, and the like).
[44] 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 d2 region 525 will be described. First, d2 region 525 is divided into m x n cells in the horizontal and vertical directions as shown in Fig. 6 (S340). For the respective m x n cells, the magnitudes of the absolute values of wavelet coefficients are compared with a reference value (S350) 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 (S360).
[45] For example, in a case where the reference value is 7, d2 region 525 is divided into m x n (l l x 4) cells as shown in Fig. 7, and the number of coefficients of which the magnitudes are larger than the reference value 7 is counted among coefficients in first cell mini 710. 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 710 comprising coefficients of {5, 7, 9, 10, 11, 9, ..., 9, 9, 8, 7, 7, 6}, and the number 35 becomes a feature vector 712 of cell mini. Subsequently, the number of coefficients of which the magnitudes are larger than the reference value 7 is counted among coefficients in a second cell m2nl 720, and the counted number 27 becomes a feature vector 714 of cell m2nl. If the extraction process is completed up to cell mmnn 730, a first vector 740 having m x n elements is created. Likewise, it is understood that if the process of creating a feature vector is repeated for all of the six (J = 2) sub-band regions (dl to d6) shown in Fig. 8, 6 x m x n feature vectors of region d2 are created.
[46] Fig. 8 shows values obtained when J is 2. If the values are expressed as a formula, a feature vector having a vertical structure of total 3Jmn bits, i.e. 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. 9.
[47] Based on the characteristics of the wavelet transform, each of the aforementioned 3 x J + 1 wavelet sub-band regions is another expression of a specific frequency resolution for the entire image. Thus, division of a sub-band region into m x n cells implies that the entire image is divided into m x n portions. Accordingly, by extracting a feature vector on a cell basis, information on the position of a feature point in the entire image can be expressed as a feature vector.
[48] Next, similarity between the configured feature vector and a predetermined determination criterion vector is measured (S370), thereby performing the process of r ecognizing the denomination of the note (S380). In order to verify the two feature vectors, final similarity is determined using a shortest distance distribution that is obtained using a conventional method, such as a shortest distance technique or the like.
[49]
Industrial Applicability
[50] The present invention is applicable to a technique that 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 performing wavelet transform for an inputted note image by a predetermined number of times and comparing the magnitudes of the absolute values of wavelet coefficients with a reference value.
[51] Further, the present invention is applicable to a technique by which 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. [52]

Claims

Claims
[1] A method of recognizing a denomination of a note using wavelet transform, comprising the steps of: scanning the note to create an original image thereof, and performing the wavelet transform for the original image 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 m x n cells in 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 a predetermined determination criterion vector.
[2] The method as claimed in claim 1, wherein the wavelet transform comprises a high speed Haar wavelet transform method.
[3] The method as claimed in claim 1, wherein the wavelet coefficient has features of each of the sub-bands by using a difference in information of neighboring sub- bands according to each of sub-band images.
[4] The method as claimed in claim 1, wherein the step of configuring the feature vector comprises configuring the feature vector in a vertical structure of total 3Jmn bits that is 3 x wavelet transform level (J) x number of cells in a row (m) x number of cells in a column (n).
[5] The method as claimed in claim 1, wherein the step of measuring the similarity comprises determining final similarity using shortest distance distribution obtained through a conventional shortest distance method.
PCT/KR2007/001196 2006-03-13 2007-03-12 Recognizing the denomination of a note using wavelet transform WO2007105892A1 (en)

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