JPH1083472A - Method for identifying paper money - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a method for precisely and quickly identifying a paper money without being influenced by stain, strain or the like. SOLUTION: As an advance processing when using this method, the picture data of reference paper moneys of a plurality of true motes is first obtained (S2). Then, the obtained picture data is classified into some data patterns by cluster analysis (S3). Then, plural reference picture data are generated (S4). Here, using the input picture data of a recognized paper money inputted, the most suitable reference picture data to that input picture data is selected from the plural reference picture data, and whether it is true or false is judged based on the selected reference picture data and the input picture data.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a bill discriminating method, and more particularly to a discriminating method capable of suppressing the influence on the discrimination accuracy due to various types of dirt on a discriminated bill and performing highly accurate and high-speed discrimination.

[0002]

2. Description of the Related Art Japanese Patent Laid-Open No.
-215293. In this publication, a bill is divided into a plurality of zones, detection data for each zone is compared with reference data predetermined for each zone, and the bill is identified based on a comparison result in each zone. In the bill discriminating method, a plurality of bills are set in accordance with the front and back, the orientation, and the displacement at the time of discrimination, and the data of each zone are totaled for one bill, and a standard value is determined by a ratio value to the total value. It is stored as pattern data, a total value of the detection data is obtained, and a ratio value to the total value is calculated as detection pattern data, and whether or not the detection pattern data is within an allowable value range of the reference pattern data is determined. Is determined, and the absolute value of the difference between the reference pattern data and the detected pattern data is calculated for each of the zones, and the total is calculated. The total value banknote identification method and performing to the bill validator determines whether less than the allowable value is disclosed.

[0003]

However, in the above-mentioned conventional technology, there is a problem that a fake bill is erroneously recognized as a genuine bill because variations in identification due to dirt, distortion and other reasons are allowed. Yes, this causes a reduction in identification accuracy.

SUMMARY OF THE INVENTION The present invention has been made to solve the problems of the conventional method, and is capable of accurately and quickly identifying paper sheets without being affected by dirt, distortion and the like. The aim is to provide a way to do so.

[0005]

According to the method of the present invention, a plurality of reference banknotes of genuine bills are obtained, and the obtained image data is subjected to cluster analysis and classified into several data patterns to obtain a plurality of reference images. Creating data, selecting the most suitable reference image data from the plurality of reference image data using the input image data of the input banknote to be identified, selecting the reference image data and the input image This is a method of determining whether the data is true or false.

In particular, a difference between the input image data and the plurality of reference image data is calculated, an irregular component such as dirt or distortion is extracted, and an irregular component is predicted based on the obtained irregular component. Then, the sum of squared differences between the predicted irregular component and the extracted irregular component is calculated, and the reference image data in which the calculated sum of squared differences has the minimum value is selected.

Then, a true bill or a false bill is determined by comparing the value of the sum of squared differences corresponding to the selected reference image data with a predetermined threshold.

[0008]

BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a perspective view of a bill discriminating method according to an embodiment of the present invention;

The method of the present invention is broadly divided into pre-processing and bill insertion processing. FIGS. 1 and 2 are flowcharts showing basic algorithms of the pre-processing and bill insertion processing, respectively.

First, in the preprocessing, when a program is started by inserting a plurality of genuine bills prepared in advance (step S1), multi-value density data by an optical sensor of the bills is fetched (step S2).

The plotted gray-scale data is shown in FIG. 3 by plotting the gradation value on the vertical axis and the point on the horizontal axis. Cluster analysis is performed using the plurality of waveform diagrams, and waveform diagrams that are very similar to each other are classified as one unit (cluster) (Step S).
3).

Then, an average waveform diagram is created for each of the obtained clusters, and a plurality of reference image data (waveform diagrams) are created (step S4). The method of cluster analysis is shown in the flowchart of FIG. When the program is started in step S31, first, in step S32, the number of banknotes to be cluster-sorted as initialization is set to N.

Next, the distance (corresponding to the similarity) between the bills in the image space is calculated (step S33). In step S34, the number K of clusters is calculated as K = N
Set to. Then, using the distance data between the banknotes previously obtained in step S33, a cluster set having the closest distance is selected (step S35).

When a set of clusters is selected, the set of target clusters is integrated into one cluster (step S
36). Therefore, in step S37, K is changed to K-1. In step S38, it is determined whether or not the result K of step S37 has become 1. If K is 2 or more, the nearest cluster set including the newly integrated cluster is searched and selected again (step S39).

The similar cluster integration process described above is repeated until K becomes 1 in step S38. When K = 1, the process ends, and a tree diagram is created in step S40 (see FIG. 5).

Based on the tree diagram thus obtained, an appropriate number of a plurality of reference waveforms are created, and the processing shifts to a bill insertion process. For example, in the example of FIG. 5, when the distance A is a threshold, the number of reference waveforms obtained is one.
Then there are three. FIG. 3 shows an example in which the threshold value is C and three reference waveforms are created.

In the bill insertion process, when the program is started in step S11, first, image data of the bill to be identified is fetched (step S12). This image data is formed in a waveform diagram as in the preprocessing.

Next, after carrying out a transport deviation correcting process, a level adjusting process, and the like, in step S13, a difference from the reference waveform data created in the preprocessing is obtained, and irregularities such as dirt and distortion on the bill are obtained. The component is extracted.

Using the obtained irregular component, AR
(Auto regression) An irregular component prediction process using a model or the like (step S14) is performed. In step S15, a sum of squared differences is calculated using the extracted irregular component and the irregular component corresponding to each predicted reference waveform, and a prediction error is calculated.

Then, the reference image waveform data having the smallest value of the obtained sum of squared differences is selected (step S16), and the value of the sum of squared differences between the selected reference image waveform data and the input image waveform data is determined in advance. By the comparison with the threshold value thus determined, the authenticity is determined (step S17).

[0021]

As described above, according to the present invention, when the authenticity is determined by comparing the captured image data of the banknote to be identified with the reference image data, the reference image data is classified according to the similarity. By comparing the representative data with the image data of the banknote to be identified, the authenticity can be determined by comparing the representative data with the image data of the bill to be identified.

[Brief description of the drawings]

FIG. 1 is a flowchart showing a pre-process in an embodiment of a bill discriminating method of the present invention.

FIG. 2 is a flowchart showing a bill insertion process in a bill discriminating method according to an embodiment of the present invention.

FIG. 3 is a conceptual diagram of one embodiment of a bill discriminating method of the present invention.

FIG. 4 is a flowchart illustrating a reference image data generation method using a cluster analysis method.

FIG. 5 is a diagram showing an example of a dendrogram.

Claims (3)

[Claims] An image data of a plurality of genuine reference banknotes is obtained, and the obtained image data is cluster-analyzed and classified into several data patterns to generate a plurality of reference image data. Selecting the most suitable reference image data from the plurality of pieces of reference image data using the input image data of the identification bill, and determining whether the input image data is true or false based on the selected reference image data and the input image data. A bill identification method characterized by the above-mentioned. 2. A method for calculating a difference between the input image data and the plurality of reference image data, extracting an irregular component such as dirt or distortion, and predicting an irregular component based on the obtained irregular component. And calculating a sum of squared differences between the predicted irregular component and the extracted random component, and selecting reference image data having the calculated sum of difference squared as a minimum value. 1. The bill identification method according to 1. 3. The method according to claim 2, wherein a true bill or a false bill is determined by comparing the value of the sum of squared differences corresponding to the selected reference image data with a predetermined threshold. A bill identifying device according to the above.