embodiment of the present invention.
The present invention described herein provides a verification method for determining areas within an image corresponding to monetary banknotes. The image is provided from a hardware scanner or similar device, where the image contains a sample monetary banknote of a particular currency type. Characteristics derived from the sample monetary banknote is compared with that of known values and/or ranges of valid monetary banknotes in verification of its location within the image. The types of currencies can include, but are not limited to United States of America currency and Japanese denomination currencies.
The method can be applied for use in the detection of counterfeit currency. The scanned image can provide the sample monetary banknote with an arbitrary rotational axis and shift alignment within the image. Additionally, the scanned image can contain the sample monetary banknote while superimposed onto any arbitrary background, can contain multiple isolated or independent banknotes, or have overlapping banknotes. The method can be used in conjunction with basic stand-alone scanners, copiers, stand-alone printers, and other related detection and scanning hardware.
The verification method described in this present invention makes use of new innovations not introduced by the prior art. This not only provides an increased means of security measures when used in application for counterfeit banknote detection, it also provides ease of integration with common hardware devices and a viable low cost approach. Accurate detection rates, and low false alarm rates can therefore be attained. It is also robust and flexible enough to be applied to different image types and conditions.
Prior to a concise description of the present invention color processing method, it is important to understand that certain terms used throughout the following description and claims will refer to particular processes or steps. As one skilled in the art will appreciate, designers may refer to such processes by different names. This document does not intend to distinguish between items that differ in name but not function. In the following discussion and in the claims, the terms “including” and “comprising” are used in an open-ended fashion, and thus should be interpreted to mean “including, but not limited to . . . ”.
An overview of a verification method for determining areas within an image corresponding to monetary banknotes according to the present invention is illustrated with reference to
Object determination 140 manages to isolate and count objects in the texture decision map. An object ideally corresponds to a monetary banknote, but may include other identified items in the texture decision map. Each object is separated from each other by removing texture sections in the texture decision map that correspond to the border sections in the banknote boundary map.
Following object determination 140 are texture property determination 150, and shape property determination 160, each performed on identified objects in the prior step. Texture property determination 150 calculates a texture property value for each object according to a texture feature map having a texture feature value for each image section. Shape property determination 160 calculates a shape property value for each object.
Finally, based on the results of texture property determination 150 and shape property determination 160, object removal 170 operates in further removing texture sections from the texture decision map corresponding to objects that do not have the texture property value within a first predetermined range and the shape property value within a second predetermined range. The resulting texture decision map displays verified areas corresponding to monetary banknotes in the scanned image.
A detailed description for each of the above identified process steps in
Image Division
The goal of image division 110 is to divide a scanned image into multiple image sections for computational efficiency. Each image section can then be processed individually, as opposed to an entire image, to provide for a greater resolution in related calculations and processes. The size and shape of the image sections can vary according to various embodiments of the present invention, and the examples provided below are in no way meant to be limiting.
Banknote Boundary Map Generation
Banknote boundary map generation 120 focuses on the creation of a banknote boundary map.
The exact implementation for discerning the border sections 430 from the original scanned image 410 can vary according to a number of embodiments. One embodiment involves comparing color histogram data of image sections 214 of the scanned image 410 to color histogram data corresponding to boundaries of valid monetary banknotes. Another embodiment involves comparing texture data of the image sections 214 to texture data corresponding to boundaries of valid monetary banknotes. The exact implementation of the banknote boundary map is intermediate, as long as the banknote boundary map suffices in identifying border sections from the image sections corresponding to a boundary of monetary banknotes within the scanned image.
Texture Decision Map Generation
Texture decision map generation 130 produces a binary texture decision map based on the scanned image. Texture values for each image section of the scanned image are first calculated, and then compared to texture values of a valid monetary banknote. Texture sections are then selected from the image sections having texture values within a valid range of a valid monetary banknote.
The texture values utilized in discerning the texture sections 530 can vary according to a number of embodiments. One embodiment involves utilizing gray levels as the texture value, and comparing gray levels of image sections to gray levels of a valid monetary banknote to determine the texture sections. Other embodiments may use different texture values, such as contrast levels, halftone levels, and edge frequencies. The exact type of texture value utilized is in fact intermediate, as long as the texture decision map 520 suffices in identifying texture sections 530 from the image sections having texture values within a valid range according to a valid monetary banknote.
Object Determination
Having both a banknote boundary map 420 and texture decision map 520 in place, object determination can be resolved. The goal of object determination is to distinguish a number of objects within the scanned image, any of which can potentially be a monetary banknote. In order to accomplish this, overlapping regions in the texture decision map must have individual objects separated from each other. This is accomplished by removing texture sections in the texture decision map that correspond to the border sections in the banknote boundary map. Because the border sections in the banknote boundary map outline the banknotes, it can be used to separate individual banknote regions in the texture decision map.
Texture Property Value Determination
Having identified and isolated a number of objects in object determination 140, texture property value determination 150 focuses on calculation of a texture property value for each of the individual objects. This texture property value will then be compared to known values corresponding to valid monetary banknotes to verify whether the texture of the relevant object agrees with the valid monetary banknote.
The exact calculation for the texture property value can vary according to the different embodiments of the present invention. For example, in one embodiment, it is calculated according to a texture feature map, which possesses a texture feature value for each image section of the scanned image. The texture feature map therefore already contains texture characteristics of the scanned image. Texture feature values for the image sections that correspond to the object in question are used in calculation of the texture property value of the object.
In one embodiment, the texture feature map is a gray level feature map having gray levels as the texture feature value for each section. In other embodiments, the texture feature map is a contrast feature map having contrast values as the texture feature value for each section, or even halftone feature map having halftone values as the texture feature value for each section. The exact type or format of the texture feature map and corresponding texture feature value for image sections is intermediate, as long as the texture feature map suffices in characterizing image sections of the scanned image in terms of texture. The principles taught in the present invention are equally applicable for any type of texture map which may be implemented.
With a texture feature map selected, the texture property value can then be determined. One preferred embodiment jointly utilizes a mean value and a variance value of the texture feature values for image sections corresponding to the object in calculation of the texture property value. However, other embodiments may singularly use a mean value, or just a variance value in calculation of the texture property value. Again, the exact calculation or formulae pertaining to the texture property value can vary, and is intermediate, as long as an appropriate texture feature map is utilized that characterizes image sections of the scanned image in terms of texture. The principles taught in the present invention are equally applicable regardless of the precise calculation and implementation of the texture property value.
In order to provide a further degree of resolution in calculating the texture property value, an additional embodiment of the present invention utilizes a second texture feature map having a second texture feature value for each image section in the texture property value calculation. The use of two texture feature maps reduces variability in the calculation, as now it utilizes two distinct texture feature aspects relating to the scanned image. Also, a greater accuracy in verification of texture sections corresponding to valid monetary currency is assumed.
Similar to the first texture feature map, the second texture feature map can be a gray level feature map having gray levels as the second texture feature value for each section, a contrast feature map having contrast values as the second texture feature value for each section, or a halftone feature map having halftone values as the second texture feature value for each section. Again, the exact type or format of the second texture feature map and corresponding second texture feature value is intermediate, as the teachings of the present invention are equally applicable for any type of second texture map implemented.
Shape Property Value Determination
Shape property value determination 160 focuses on calculating a shape property value for each of the identified objects. The shape property value will then be compared to known values corresponding to valid monetary banknotes to verify whether the shape of the relevant object agrees with that of the valid monetary banknote.
The specific formulae for calculating the shape property value can vary according to a number of embodiments. In one embodiment, the shape property value for each object simply comprises determining an area of the object. This may include utilizing four corners of the object to determine the area of the object. Other embodiments can additionally include: determining a distance between center points of two different diagonal lines within the object, determining lengths of two parallel lines within the object, determining an inner product using four angles within the object, and determining a ratio of a width of the object and a height of the object.
Although the exact calculation of the shape property value can vary according to different embodiments, its exact representation is intermediate, as the teachings of the present invention are equally applicable for any calculation for shape property value implemented.
Object Removal
With texture property values and shape property values determined for each object, the object removal 170 focuses on removing objects that do not correspond to a valid monetary banknote. This is accomplished by further removing texture sections from the texture decision map corresponding to objects, which do not have a texture property value within a first predetermined range, and a shape property value within a second predetermined range.
In the preferred embodiment of the invention, the first predetermined range corresponds to valid texture property values of valid monetary banknotes. The second predetermined range corresponds to valid shape property values of valid monetary banknotes. Therefore, should an identified object have both a texture property value and shape property value within the above valid ranges (both corresponding to a valid monetary banknote), its corresponding texture sections are left in the texture decision map to verify a location of valid monetary banknote within the scanned image. Otherwise, if either the texture property value or shape property value of the object are not within the above respective ranges, their corresponding texture sections are removed from the texture decision map.
A process flow chart for the verification method for determining areas within an image corresponding to monetary banknotes is presented in
Step 910: Divide the image into a plurality of image sections.
Step 920: Generate a banknote boundary map having border sections selected from the image sections, the border sections corresponding to a boundary of monetary banknotes within the image.
Step 930: Generate a texture decision map having texture sections selected from the image sections, the texture sections having a texture value within a valid range according to a valid monetary banknote.
Step 940: Determine a number of objects in the texture decision map by removing texture sections in the texture decision map that correspond to the border sections in the banknote boundary map.
Step 950: Calculate a texture property value for each object according to a texture feature map having a texture feature value for each image section.
Step 960: Calculate a shape property value for each object.
Step 970: Remove texture sections from the texture decision map corresponding to objects that do not have the texture property value within a first predetermined range and the shape property value within a second predetermined range.
The above detailed present invention therefore provides a verification method for determining areas within an image corresponding to monetary banknotes. Characteristics of the scanned image are compared with that of known values and/or ranges of valid monetary banknotes for verifying banknote locations within the image.
The method can be applied for use in the detection of counterfeit currency. The scanned image can contain the sample monetary banknote while superimposed onto any arbitrary background, contain multiple isolated or independent banknotes, have overlapping banknotes, or have arbitrary rotational and shift alignments.
Use of the present invention method not only provides an increased means of security measures when used in application for counterfeit banknote detection, it also provides ease of integration with common hardware devices and a viable low cost approach. Accurate detection rates, with low false detection frequencies can therefore be attained. The method is also robust and flexible enough to be applied to different image types and conditions.
Those skilled in the art will readily observe that numerous modifications and alterations of the device and method may be made while retaining the teachings of the invention. Accordingly, the above disclosure should be construed as limited only by the metes and bounds of the appended claims.