JPH08115022A - Image processor - Google Patents

Abstract

PURPOSE: To accurately detect image data even when noise is included in the image data. CONSTITUTION: An original being an object is converted into plural image signals by a sensor 1 and processed to be fitted by every image component by a specified image processor 2. After a threshold processor 3 performs the threshold processing of the image signal processed to be fitted based on one or plural thresholds, the on/off of a picture element is decided by a picture element extraction processor 4. A middle point extraction processor 5 obtains a middle point between two black picture elements in a main scanning direction, detects the coupling of the middle point position in a subscanning direction and calculates a distance from the picture element to the middle point position. A circle extraction processor 6 discriminates a specified circle while accumulating the results by the middle point extraction processor 5 in the subscanning direction. Thus, the circle image is surely detected with high accuracy and high speed processing is realized because real time processing can be possible.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an image processing prevention document, particularly an image processing for preventing image misuse of a document specially printed in order to prevent faithful copying of color documents such as banknotes and securities. Regarding the device.

[0002]

2. Description of the Related Art In recent years, along with the improvement of the performance of color copiers, it is necessary to develop a copier having a function of preventing the copying of securities such as banknotes, stock certificates, bonds, commuter passes, admission tickets for entertainments, and cash vouchers. The nature is high. The technology related to the copy protection function of conventional copiers is roughly divided into
It can be divided into a technique of giving a function of preventing a normal copy on the original side and a technique of giving a function of detecting a copy-inhibited original by an image reading / detecting device and preventing a normal copying operation.

As the former function of preventing normal copying on the original side, for example, a metal powder is mixed into the original, and a copy image based on the metal powder other than the original image is reflected by the metal powder of the original image surface irradiation lamp. Which is formed by using a background pattern of an original recorded image and a moire generated by applying halftone dots at the time of copying or processed by using holography, or by using a fluorescent color that cannot form a clear image on the ink by copying, Techniques such as those that use blue ink that is difficult to reproduce in a copying machine that uses a selenium photoreceptor are known.

In the latter technique, in which the image reading / detecting device detects a copy-inhibited document and imparts a function of preventing a normal copying operation, the read data of the document is compared with the basic data in the memory to perform copying. There is known a method of judging whether the original is a good original or not, and prohibiting copying, fixing the output sheet to unfixed, and changing the output state of the sheet from the normal state.

There are advantages and disadvantages in any of these conventional techniques for preventing unauthorized copying of originals, and the normal copy-preventing function on the image reading / detecting device side is a memory having a huge capacity for storing basic data of a specific original. There is a problem in that cost is increased because a means or the like for comparison and determination with the read document image is required. A technique for solving such a problem is proposed in Japanese Patent Laid-Open No. 4-296323.
That is, a circular image composed of a plurality of concentric circular codes specifying at least one of the thickness of each circle, the interval between the lines of each circle, and the diameter of each circle is printed on the original surface, and the concentric codes are printed. The image processing apparatus is designed to prevent the abused image processing by recognizing it.

[0006]

By the way, the conventional circular image for preventing the abusive image processing focuses on the fact that the original can be recognized at any angle. When detecting a circle, only the image data on the main scanning line is referred to. However, the image data input from the manuscript contains noise, and in the prior art, this noise adversely affected the accuracy of circle detection. An object of the present invention is to provide an image processing device that can accurately detect image data even if it contains noise.

[0007]

In order to achieve the above object, the present invention according to claim 1 is mainly provided in an image processing apparatus for performing image processing on an original image read in pixel units and outputting the image. Position calculating means for calculating the midpoint position between pixels in the scanning direction, distance calculating means for calculating the distance from the pixel to the midpoint position, and connectivity for detecting connectivity of the midpoint position in the sub-scanning direction. The configuration includes a detection unit and a circle extraction unit that performs circle extraction based on the connectivity of the midpoint position and the distance from the pixel to the midpoint position.

[0008]

According to the above construction, the information for extracting the circle is obtained by obtaining the connectivity between the midpoint position of the pixels in the main scanning direction, the distance from the pixel to the midpoint position, and the midpoint position in the sub scanning direction. By doing so, it is possible to reliably detect the circle image with high accuracy. Moreover, since real-time processing is possible, high-speed processing can be performed.

[0009]

Embodiments of the present invention will be described below with reference to the drawings. FIG. 1 shows the overall configuration of the image processing apparatus system. The target document is converted into a plurality of image signals by the sensor 1. Each image signal is a specific image processing device 2
By this, processing suitable for each image component is performed and input to the threshold processing device 3. The threshold processing device 3 performs threshold processing based on one or a plurality of thresholds. The pixel extraction processing device 4 determines ON / OFF of the pixel from the result of the threshold processing device 3. The midpoint extraction processing device 5 finds the midpoint between two black pixels in the main scanning direction, detects the connectivity of the midpoint positions in the sub-scanning direction, and determines the distance from the pixel to the midpoint position. calculate. The circle extraction processing device 6 identifies a specific circle while accumulating the results of the midpoint extraction processing device 5 in the sub-scanning direction.

Next, each of the above processes will be described in detail. FIG. 2 shows the configuration of the input process. The document is read by the sensor 1 and processed as a plurality of image components. Where R
The case of three components of GB will be described. Generally, the input image has blurring from the optical system.
Image enhancement is performed. In this process, the purpose is to extract characteristic pixels rather than to reproduce an image, so image processing specialized for a specific color is effective. For example, in the specific image processing device 2, the R component is blurred, the G component is image enhanced,
The B component is used for through processing.

The data processed by the specific image processing apparatus 2 is threshold-processed by the threshold processing apparatus 3, respectively. The threshold value processing device 3 performs processing with one or a plurality of threshold values. In general threshold value processing, the output signals are turned ON when THr <Dr THg <Dg THb <Db by threshold values THr, THg, and THb. Further, by defining the color components, it is possible to provide two threshold values and to turn on the output signal when THr1 <Dr <THr2 THg1 <Dg <THg2 THb1 <Db <THb2.

Further, a plurality of thresholds are used to provide steps such as THr1 <Dr <THr2 THr3 <Dr <THr4 THg1 <Dg <THg2 THg3 <Dg <THg4 THb1 <Db <THb2 THb3 <Db <THb4. Thresholding can also be performed.

In this embodiment, the output from the sensor is R,
Although described as G and B, a single signal (for example, only the G component) or a plurality of signals can be realized with a similar configuration. Further, the signals are not limited to R, G, B, but the luminance signals L,
Similar effects can be obtained with signals such as a and b.

The threshold-processed signal is sent to the pixel extraction processing device 4, and pixels are extracted based on the data from the threshold processing device 3 of each signal component. The pixel extraction processing device 4 generally outputs the signal out =
It is output as Ro & Go & Bo, but by specifying a color, it is possible to extract only a specific color by an operation such as out = Ro | Go | Bo out = Ro | (Go & Bo) out = Ro & Go & Bo. Further, it is possible to give certainty of data by performing arithmetic operation instead of logical operation.

FIG. 2 shows a case in which one threshold processing device is provided for each image signal, but as shown in FIG. 3, a plurality of threshold processing devices and pixel extraction processing devices are provided. It is also possible. In the configuration shown in FIG. 3, since pixel extraction can be performed for each color, accurate extraction is possible even when the colors are mottled.

The concept of circle extraction will be described with reference to FIG. The characteristics of the circle in the main scanning direction do not change regardless of how the original is placed. Here, it is assumed that the inner frame (black pixel) of the circle is extracted while scanning in the main scanning direction to obtain the midpoint between the black pixels. When this is continuously processed in the sub-scanning direction, the middle point becomes a straight line in the sub-scanning direction in the circle, and its length is the maximum length (diameter) in the main scanning direction.
Becomes equal to A circle is extracted using these features.

Next, the method of extracting the middle point and the method of extracting the circle mainly for detecting the continuity of the middle points in the sub-scanning direction will be described. FIG. 5 is an explanatory diagram of the midpoint detection method. The one shown in the figure is a general example, and when there are four black pixel blocks in the main scanning direction, L1, L2 and L3 are respectively measured,
At the same time, the midpoint information is measured. A basic method for midpoint detection will be described with reference to FIG. A point from a black pixel to a white pixel is detected as the inner frame of the circle (S1), and it is set as a starting point. When the start point is detected, the counter is turned on (S
2). Then, a pixel that becomes a black pixel from a white pixel that is an end point is detected (S3), and the pixels up to the pixel are counted (S3).
4). Reset the counter after outputting the counter (S
5). Since the midpoint becomes half of the count value, the position of the midpoint can be immediately obtained by adding a binary counter. This configuration is an effective method when there is no noise in the target image.

On the other hand, the case where noise is taken into consideration will be described. FIG. 7 shows the case where noise is present in the circle. The length across the circle is actually L3, but because of the presence of noise, it is divided into L1 and L2, and the accurate midpoint of the circle cannot be extracted. However, to find the midpoint between all black pixels in the main scan direction to deal with noise,
As shown in FIG. 8, the increase in the number of midpoints makes real-time processing difficult. Further, the hardware cost for the midpoint processing also rises, which affects the feasibility / performance in the circle extraction. Although it is difficult to solve the problem of accurately extracting the midpoint when there is noise, it is possible to solve it by using the feature of the circle in the sub-scanning direction together, although it is difficult to solve it by processing only in the main scanning direction. .

Next, the method of midpoint processing will be described. FIG. 9 shows the state of the middle points of the distances th1 to th5 when scanning is performed in the raster direction with respect to a circle, a triangle, and a rectangle. In this case everything is in a straight line,
If it is distinguished from a circle only by this, misidentification will occur. Here, before extracting the midpoint, the distance information of the midpoint of the previous line is used. In the case of a circle, the initial distance gradually decreases as it progresses in the sub-scanning direction, which is close to 0, and then gradually decreases at a certain value (diameter). Shows the characteristic. On the other hand, in the case of the triangle, it increases only gradually, and in the case of the quadrangle, it only shows a constant value. Triangles and quadrilaterals are not always input at such positions, and thus actually show a more complicated appearance. On the other hand, the circle has the same characteristic regardless of the positional relationship, and therefore the midpoint is obtained using this characteristic. Therefore, it is possible to perform processing while limiting the target points in the circle. A block diagram in that case is shown in FIG. The detection of the start point, end point, and midpoint is the same as in FIG.

The circle is scanned to extract pixels (S10), the starting point is detected (S11), and the starting point is stored (S1).
2). Similarly, the end point is detected (S13) and stored (S13).
14). The middle point is detected from the stored start point and end point (S
15) Then, the distance between this midpoint pixel and the neighboring midpoint pixel one line before is compared (S16). The feature of the circle is determined from the comparison result (S17), the detected midpoint is stored when the feature of the circle is present (S18), and the detected midpoint is invalidated when there is no feature of the circle (S18). S19). And step 1
Returning to 1, the data of the next scan is processed.

Although the processing up to this point has been performed on the entire circle, it is also possible to process only a partial area of the circle as shown in FIG. 11 (a). Further, as shown in FIG. 11B, the sub-scanning direction may be separated. The midpoint detection method using the information in the sub-scanning direction has been described above. However, when the above-mentioned processing is performed, the midpoint becomes a straight line as shown in FIG. In addition to this condition, it can be determined whether or not it is a circle by determining whether or not the length of the straight line is equal to the diameter of the circle.

Here, as an example of the circle extraction method, 1
Description will be made regarding a case where it is realized by a memory for lines. FIG. 13 shows the concept of processing. FIG. 14 shows the flow. It should be noted that the method described so far is used as the method of extracting the midpoint, and the pixel detected as the midpoint is focused here.
When the pixel of interest is black and the same pixel (neighborhood) on the previous line is also a black pixel, addition (+1) is performed. By this processing, the length of the vertical straight line is stored in the memory. When the pixel of interest is white and the same pixel (neighborhood) on the previous line is a black pixel, the memory value up to this point is checked, and if it is close to the length of the diameter stored in advance, it is determined as a circle candidate. Otherwise, clear the value in memory. With the above processing, the circle candidate can be determined. The illustrated example shows that the number of pixels at the midpoint is "46".

In FIG. 14, when the pixel at the middle point is detected in a certain line scan (S20), if the detected pixel is a white pixel, it is determined whether the same pixel in the previous line is black (S2).
1) Similarly, when it is white, the process returns to step 20 to detect the next pixel. On the other hand, when the same pixel on the previous line is black, it is determined whether the midpoint length is equal to the threshold value (S22),
If they are not equal, the process returns to step 20. When the midpoint length is equal to the threshold value, a circle candidate signal is output as a circle candidate (S23), and the next pixel is detected. Returning to the beginning, when a black pixel is detected in step 20, it is determined whether the same pixel on the previous line is black (S24). From this determination result, when the same pixel on the previous line is black, the number of pixels is added (S25), and when it is white, the number of pixels is set to "1" (S2).
6) Return to step 20.

Application Example 1: When there are a plurality of circles as an object to be identified As shown in FIG. 15, an extraction method when a document has a plurality of circles will be described. Reliability is improved because it is determined whether or not the copy is a prohibited document using a plurality of pieces of information. It is also possible to have information by determining the arrangement relationship and size of a plurality of circles. In FIG. 16, the means (circle detection processing device) described in the above embodiment is used to identify the circle. The circle detection processing device 10 detects the information of the circle, for example, the position of the center, the diameter, etc., and sends the information of the circle to the multiple circle detection processing device 11. The multi-circle detection processing device comprehensively judges the layout relationship / size / number of circles based on the circle information, and outputs the result when the layout relationship is determined to be copy prohibited. To do.

Application Example 2: Case where Part of Line Constituting Circle Is Colored By adding a specific color to the circle as shown in FIG.
Since more information is available, the reliability of circle extraction is improved. Also,
At the same time, there is an advantage that the inclination of the original can be detected. This embodiment is used as the basic method of the circle extraction processing device. In FIG. 18, the image signal output from the sensor 1 is sent to the specific color detection processing device 7, is compared with the color stored in advance, and when it matches the specific color, the signal is sent to the circle extraction processing device 6. . If the pixel determined to be the specific color matches any of the pixels at both ends for obtaining the midpoint of the circle, the position where the specific color appears is stored. Since the specific color is added to the circle to be extracted in advance, it is determined by the midpoint information that it is a circle, and only when it is determined that the specific color has been added,
It is determined that this circle is a circle to be extracted. When the circle extraction processing device 6 determines that the circle is a circle, the information on the position of the center of the circle and the specific color is output. A combination of a color, an absolute position, a relative position, an angle from the center, and the like can be considered as the specific color information. Application Example 3: When a line forming a circle is given a specific color As shown in FIG. 19, a line forming a circle is given a specific color. The color scheme has a meaning when it is decomposed into histograms of each color, and as long as the rules are observed, there is a degree of freedom in which color scheme is used. Since more information is available, the reliability of circle extraction is improved.

The present embodiment is used as the basic method of circle extraction. However, in order to calculate the histogram, a buffer memory (line memory) 12 is required in the sub-scanning direction in FIG. If the circle detection processing device 10 extracts a circle candidate, the histogram calculation processing device 13
Then, the histogram of the area is calculated using the binary image of the circle as a mask image. When the distribution state of the histogram matches the pattern sent to the determination processing device 14 and stored in advance, the circle is determined to be the circle of the specific image. When a circle is detected, the histogram of each color of the circle is examined, and only when it is the same as that stored in advance, it is determined that the circle is a predetermined circle. Since there are two levels of identification, it is possible to reduce false positives. Further, by storing a plurality of pieces of histogram information, it is possible to embed other information. In this description, the histogram in the RGB space is obtained, but other color spaces may be used.

[0027]

As described above, according to the present invention, not only the image data on the main scanning line but also the connectivity in the sub-scanning direction of the midpoint position between pixels calculated from the image data is referred to. Even if it is included, it is possible to accurately detect the circle.

[Brief description of drawings]

FIG. 1 is a diagram showing an overall configuration.

FIG. 2 is a diagram showing a detailed configuration of input processing.

FIG. 3 is a diagram showing a configuration of pixel extraction processing.

FIG. 4 is a diagram showing a concept of circle detection.

FIG. 5 is a diagram illustrating a method of detecting a midpoint.

FIG. 6 is a block diagram of midpoint detection.

FIG. 7 is a diagram illustrating midpoint detection in the presence of noise.

FIG. 8 is a diagram showing a result of midpoint detection in the presence of noise.

FIG. 9 is a diagram showing characteristics of a middle point of a circle.

FIG. 10 is a diagram showing a configuration of a midpoint detection method using information in the sub-scanning direction.

FIG. 11 is a diagram illustrating another method of detecting the midpoint.

FIG. 12 is a diagram illustrating the concept of circle detection.

FIG. 13 is a diagram illustrating circle detection.

FIG. 14 is a diagram showing a circle detection process.

FIG. 15 is a diagram illustrating a case where there are a plurality of circles.

FIG. 16 is a diagram showing a configuration of circle detection when there are a plurality of circles.

FIG. 17 is a diagram illustrating a case where a part of a circle has a specific color.

FIG. 18 is a diagram showing a configuration of circle extraction in the case where a part of a circle has a specific color.

FIG. 19 is a diagram illustrating a case where a circle is colored with a specific histogram.

FIG. 20 is a diagram showing a configuration of circle detection when a circle is colored with a specific histogram.

[Explanation of symbols]

1 ... Sensor, 2 ... Specific image processing device, 3 ... Threshold processing device, 4 ... Pixel extraction processing device, 5 ... Midpoint extraction processing device,
6 ... Circle extraction processing device, 7 ... Specific color detection processing device, 10 ...
Circle detection processing device, 11 ... Plural circle detection processing device, 12 ... Line memory, 13 ... Histogram calculation processing device, 14 ...
Judgment processing device

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Claims (1)

[Claims] 1. An image processing apparatus for performing image processing on an original image read in pixel units and outputting the original image, and a position calculating means for calculating a midpoint position between pixels in a main scanning direction; Distance calculating means for calculating the distance to the position, connectivity detecting means for detecting the connectivity of the midpoint position in the sub-scanning direction, connectivity of the midpoint position and the distance from the pixel to the midpoint position An image processing apparatus, comprising: a circle extracting unit that extracts a circle based on the circle.