Banknote recognition and classification method and system

Patent metadata

Field	Value
Publication number	US-9827599-B2
Application number	US-201414889425-A
Country	US
Kind code	B2
Filing date	Jan 23, 2014
Priority date	Jul 11, 2013
Publication date	Nov 28, 2017
Grant date	Nov 28, 2017

Abstract

A banknote recognition and classification method and system are provided. In the method and system, a large number of reliable samples which are easy to acquire currently are used to establish a sample signal degradation model which meets application requirements by means of a statistical method, sample information is input into the banknote sample signal degeneration model to acquire various banknote sample information corresponding to the brand new banknotes to be learned; the various banknote sample information is input to perform classifier learning, and a banknote classification model is output; then a classifier learns to perform classification and recognition on a sample to be recognized.

First claim

The invention claimed is:

1. A method for recognizing and classifying banknotes in a banknote processing device comprising:
acquiring, by a processor, sample information of brand new banknotes to be learned and banknote sample information to be recognized, wherein the brand new banknotes are banknotes with brightness in a predetermined degree;
establishing, by the processor according to a preset rule, a banknote sample signal degeneration model, wherein the banknote sample signal degeneration model comprises: a banknote condition degeneration model established based on linear change of image brightness, and a banknote image degeneration model established based on randomness of a statistic model, wherein the banknote image degeneration model comprises signal degeneration models for banknote contamination, banknote incompletion, banknote crack, and banknote fold or deflection, and the banknote condition degeneration model comprises degeneration models for banknotes in brand new condition, banknotes in 80%-90% new condition, banknotes in 70%-80% new condition, and banknotes in 0-70% new condition, wherein the establishing the banknote contamination degeneration model according to the preset rule comprises
presetting that a banknote contamination region is circular and a stain is circular, and each banknote only have one contamination region; and
determining, according to statistics analysis, that probability density curves for a position of the contamination region and a position of the stain in the contamination region are constants, i.e., the probability density curves are in uniform distribution X˜U(a,b), and a probability density curve for a size of the contamination region and probability density curves for size, density and gray value of the stain are in normal distribution X˜N(μ,σ 2 );
inputting, by the processor, the sample information into the banknote sample signal degeneration model to acquire various banknote sample information corresponding to the brand new banknotes to be learned, wherein the various banknote sample information corresponding to the brand new banknotes to be learned comprises sample information for banknotes with brightness in varying degrees;
inputting, by the processor, the various banknote sample information to perform classifier learning, and outputting a banknote classification model; and
performing, by the processor, sample signal preprocessing and feature extraction on the sample information to be recognized, performing classification decision on the banknote to be recognized by using the classification model, and outputting a final classification result.

2. The method according to claim 1 , wherein the establishing the banknote condition degeneration model according to the preset rule comprises:
analyzing a gray distribution f(x)=ax+b of an image for a banknote of a specific denomination of a specific currency, and dividing, according to gray similarity, the banknote of the specific denomination of the specific currency into five regions;
selecting a set of samples in brand new condition, and performing statistics on average gray value G for each banknote in the set;
selecting a set of samples in one of the conditions, and performing statistics on average gray value g for respective regions of each sample;
matching the average gray values G to the average gray values g respectively;
combining every two of the formulas f(x)=ax+b for the five regions to calculate a and b for each formula; and
selecting a set of samples in brand new condition, and calculating average gray value for each region of all banknote images, where each average gray value corresponds to a mapping to the gray distribution f(x)=ax+b.

3. The method according to claim 1 , wherein the establishing the banknote incompletion degeneration model according to the preset rule comprises:
determining, according to statistics analysis, a position, a size and a shape of an incompletion, wherein:
a probability density curve of the position of the incompletion is a constant;
a probability density curve of the size of the incompletion is in normal distribution; and
the shape of the incompletion is polygon which is any one of trigon to octagon, convex polygon or concave polygon, and a probability density curve of the shape of the incompletion is a constant.

4. The method according to claim 1 , wherein the establishing the banknote folding or deflection degeneration model according to the preset rule comprises:
dividing the banknote into two columns and two rows to form four uniform rectangular regions each having a long side and a short side which belong to edges of the banknote;
randomly selecting one of the regions, randomly selecting one point of the short side of the region, and randomly selecting another point of the long side of the region;
determining whether a distance between the two points, i.e., the distances x (a distance on the long side) and y (a distance on the short side) from the two points to the vertex, satisfy a constraint condition of √{square root over (x 2 +y 2 )}<k,x<m,y<n; if the distance between the two points satisfies the constraint condition, proceeding to a next step; and if the distance between the points does not satisfy the constraint condition, returning to the previous step; and
filing a deflection region, which has an edge being a straight line determined by the two points and has a point beyond the edge, with background color.

5. The method according to claim 1 , wherein the establishing the banknote crack degeneration model according to the preset rule comprises:
randomly acquiring a line segment s with a length of L on the boundary of the banknote, wherein L is in uniform distribution, Lε(0,MaxL) , and MaxL is a maximum length of the boundary of the banknote;
determining a position of another point N, wherein a distance between the point N and a midpoint M of the line segment s is 1, and an angle between the line segment MN and the line segment s is, wherein lε(0,Maxl) , the angle αε(π/3,2π/3) , and α and 1 are in normal distribution; and
determining a triangle region bounded by the point N and the segment line s as the crack region, and filling the crack region with background color.

6. A system for recognizing and classifying banknotes in a banknote processing device comprising at least one processor and a memory having processor-executable instructions stored therein, and the instructions when executed by the at least one processor, configure the device to:
acquire sample information of brand new banknotes to be learned and banknote sample information to be recognized, wherein the brand new banknotes are banknotes with brightness in a predetermined degree;
establish a banknote sample signal degeneration model according to a preset rule, wherein the banknote sample signal degeneration model comprises: a banknote condition degeneration model established based on linear change of image brightness, and a banknote image degeneration model established based on randomness of a statistic model, wherein the banknote image degeneration model comprises signal degeneration models for banknote contamination, banknote incompletion, banknote crack, and banknote fold or deflection, and the banknote condition degeneration model comprises degeneration models for banknotes in brand new condition, banknotes in 80%-90% new condition, banknotes in 70%-80% new condition, and banknotes in 0-70% new condition;
preset that a banknote contamination region is circular and a stain is circular, and each banknote only have one contamination region;
determine, according to statistics analysis, that probability density curves for a position of the contamination region and a position of the stain in the contamination region are constants, i.e., the probability density curves are

Assignees

• Grg Banking Equipment Co Ltd

Inventors

• Liang Tiancai
• Luo Panfeng
• Liu Siwei
• Chen Dingxi
• Wang Weifeng

Classifications

• G07D7/187

  Detecting defacement or contamination, e.g. dirt · CPC title

• G07D7/12 Primary

  Visible light, infrared or ultraviolet radiation · CPC title

• B07C5/344 Primary

  according to electric or electromagnetic properties {(sorting according to size measured electrically or electronically B07C5/08; material testing by magnetic means G01N24/00, G01N27/00, by electrical means G01N27/00; electrical measuring devices in general G01R; coin testing G07D5/00)} · CPC title