KR19990011500A - Local binarization method of the imaging system - Google Patents

Abstract

According to the present invention, in an image system, a pixel of interest designation step of sequentially designating a pixel of interest to be binarized in a binarization target image, and masking adjacent pixels around the pixel of interest using a plurality of edge direction detection masks, respectively, An edge detection step of detecting a maximum edge size and an optimal edge direction corresponding to the pixel of interest by calculating a direction having a maximum edge size, and whether localization is performed to reflect characteristics of adjacent pixels positioned around the pixel of interest; And a binarization step of performing local binarization on the pixel of interest by determining a local threshold through a plurality of adjacent pixels selected according to the optimal edge direction after determining using a maximum edge size. .

According to the present invention, localization is performed by using edge orientation when binarizing a document image through a binarization mask, thereby minimizing false contouring and suppressing generation of isolated points due to noise. There is this.

Description

Local binarization method of the imaging system.

The present invention relates to a local binarization method of an image system, and more particularly, in an image input system, by performing local binarization using edge orientation when binarizing a document image through a binarization mask. The present invention relates to a localized binarization method of an image system that minimizes false contouring and suppresses generation of an isolated point due to noise.

One of the typical imaging systems, the scanner (ie, reading means) is the most common means for reading printed texts, photographs, human-written letters or pictures, and includes a multifunction device, a document translator, and CAD (Computer Aided Design). Essential components such as computer, facsimile, character recognizer and digital copier.

In recent years, as office electronics, which are at the crossroads of development, is rapidly increasing in demand for office automation devices such as printers, scanners, and facsimile machines, each office automation device is developed with high performance to expand its own functions. In addition, it is a trend to produce and provide a product that performs a complex document output function at the same time to reduce the economic burden and installation space for the user by developing each office automation equipment that was used independently as an integrated.

In particular, in devices having a scanner capable of reading a document therein, such as a facsimile machine or a multifunction printer, after reading an image having multiple levels of gradation through the scanner, the characteristics of the binarization method of the read data are characterized by In evaluating performance, it becomes a major evaluation item.

In general, "image binarization" refers to a gray level image represented by a multi-step gray level in an image system, where a quantization number is one bit, that is, a binary number of '1' and '0'. An image processing technique used to express a binary image or binary image. Image compression is performed based on a signal processing characteristic capable of preserving the characteristic information of a target image while significantly reducing the amount of processing data. It is widely used in the field of image output, image recognition, etc.

Due to this wide range of applications, various image binarization methods are known.

The most common binarization method is to obtain a binarized image by determining an arbitrary threshold and assigning 1 to a pixel value larger than the threshold and 0 to a pixel value smaller than the threshold. Thus, image binarization processing is a kind of threshold processing for classifying gray levels up and down based on a threshold value. The most important thing in the threshold processing is the determination of a threshold value. There are also ways to determine more precise thresholds through concentration histograms.

Meanwhile, in order to properly binarize an edge region of a visually important image, there is a method of performing binarization after emphasizing an edge of an original image through a predetermined edge enhancement filter. It is known that a good binarization image can be obtained compared to a method that does not include the emphasis process.

An example thereof will be described with reference to FIG. 1.

If the gradation value of the original image is given in Table 1,

78 77 76 93 152 176 227 222

When spatial filtering is performed using a 1 × 3 edge enhancement mask as shown in Table 2,

-One 3 -One

You can get the result with the edge shown in Table 3.

77 58 51 187 149 255

Fig. 1 shows the gradation value of the original image and the gradation value of which the edge is emphasized, where the solid line represents the gradation value of the original image, and the dotted line represents the gradation value of the image as a result of emphasizing the edge.

In this case, when the threshold is set to 150, 78, 58, and 51 in Table 3 are set to 0, 187 is set to 1, 149 is set to 0, and 255 is set to 1, so that the pixel set to 1 is displayed in black, and 0 When the pixel set to is white, the edges of 187, 149, and 255 where the edges are distributed have one edge on each side of 149, resulting in two lines of edges, resulting in deformation or distortion of the original image. .

As described above, even when a binarization method including an edge enhancement process known to have superior binarization characteristics is used, an edge region and an edge boundary are generated in the edge region as described with reference to FIG. 1. It is expected that a localized binarization method capable of performing binarization by separating non-edge regions is expected. Accordingly, many localized binarization methods are currently known.

Applicants of the present invention have also proposed a number of localized binarization methods, including Korean Patent Application No. 91-8508, a binarization method using local maximum and minimum brightness values, to meet such technical expectations.

The Korean Patent Application No. 91-8508, a binarization method using local maximum and minimum brightness values, uses a binarization mask having a predetermined window as indicated by the name of the invention to focus adjacent pixels around the pixel of interest in the image. After masking, the pixel of interest is adaptively binarized using the local maximum and minimum values of the pixels masked in this binarization mask.

According to the prior art, the maximum-minimum localized localization method includes a scanning step of scanning a scanning target image in bands based on a shuttle scanning method in multi-level grayscales, and a predetermined window according to a window advance direction for shuttle scanning. A binarization masking step of masking adjacent pixels around the pixel of interest in the image by using a binarization mask having a maximum value, and after obtaining the maximum and minimum values of the pixels masked in the binarization mask, an absolute value of the difference between the maximum value and the minimum value And if the value is less than a predetermined reference value, binarizes the pixel of interest using a predetermined global threshold and otherwise binarizes the pixel of interest using a predetermined local threshold that is relatively smaller than the global threshold.

According to the conventional maximum-minimum localized localization method, there is an advantage that a clear image quality can be obtained without omitting edge enhancement filtering. However, the disadvantage of the maximum-minimum localized localized method is that even a single pixel among the adjacent pixels is noisy. With the component, the difference between the maximum and minimum values exceeds the reference value and is recognized as an edge even though it is not the edge (i.e., the edge) of the actual character or image, and the local threshold is applied, resulting in unnecessary use for the binarized image. There is a problem that an isolated pixel is generated, which causes a deterioration in image quality.

In other words, the maximum-minimum based local binarization method according to the prior art obtains the maximum and minimum values of adjacent pixels without considering the direction of the edges, so that a separate measure can be distinguished between noise components and edge components of an actual character or image. Is not sensitive to noise.

Accordingly, the present invention has been made in view of such a problem, and in the image input system, false contouring is performed by performing local binarization by using edge directionality when binarizing a document image through a binarization mask. It is an object of the present invention to provide a localized binarization method of an image system that can minimize and suppress the occurrence of an isolated point due to noise.

1 is a graph showing a gray value of an original image and a gray value highlighted by an edge according to the prior art;

2 is a flow chart of a preferred embodiment of a localized method of image system in accordance with the present invention;

3 is a block diagram illustrating a shuttle scanner module as an example of a shuttle scanning based imaging system;

4 is an exemplary view showing a shuttle block for scanning an A4 size original with a shuttle scanner module;

5 is an exemplary diagram for describing a method in which one shuttle block is scanned;

6 is an exemplary diagram showing each 3x3 edge direction detection mask for four directions;

7 is an exemplary diagram showing an example of selecting adjacent pixels in an edge region of the present invention;

<Description of the code used in the main part of the drawing>

21: shuttle scanner module 22: print head

23: horizontal moving shaft 24: carriage return motor

25: line feed motor 26: moving belt

In order to achieve the above object of the present invention, the localized binarization method of the image system according to the present invention includes a target pixel specifying step of sequentially specifying a pixel of interest to be binarized in a binarization target image in the image system, and a plurality of edges. An edge detection step of detecting a maximum edge size and an optimal edge direction corresponding to the pixel of interest by masking adjacent pixels around the pixel of interest using a direction detection mask, and then calculating a direction having a maximum edge size; Determining whether localization is performed by reflecting the characteristics of adjacent pixels located around the pixel of interest using the maximum edge size, and then determining a local threshold through a plurality of adjacent pixels selected according to the optimal edge direction. Characterized by a binarization step that performs localized binarization for All.

In the aspect of the present invention, the binarizing step compares the maximum edge size with a preset reference value and determines an area to which the pixel of interest belongs as an edge region if the maximum edge size is larger than the reference value. Determining a non-edge region, and when the region to which the pixel of interest belongs is determined as the edge region, pixels masked in an edge direction detection mask in the optimal edge direction. A local pixel selection step of selecting a plurality of adjacent pixels along the optimal edge direction among the plurality of pixels; determining the local threshold by averaging the selected neighbor pixels, and performing localization on the pixel of interest according to the local threshold; And if the region to which the pixel of interest belongs is determined to be a non-edge region, the preset global region. That by using a global consisting binarizing step of binarizing the pixel of interest it is preferred.

Hereinafter, a preferred embodiment of the method for localizing the image system according to the present invention will be described with reference to FIG.

Figure 2 shows a flowchart of a preferred embodiment of the localized method of image system according to the invention.

According to a preferred embodiment of the method of localized binarization of an image system according to the present invention, as shown in Fig. 2, a target pixel specifying step (S10) of sequentially specifying a pixel of interest to be binarized in a binarization target image (S10);

An edge for detecting the maximum edge size and the optimal edge direction corresponding to the pixel of interest by masking adjacent pixels around the pixel of interest using a plurality of edge direction detection masks, and then calculating the direction having the largest edge size. Detection step S20; And

After determining whether localization reflects characteristics of adjacent pixels located around the target pixel using the maximum edge size, the local threshold is determined through a plurality of direction-based adjacent pixels selected according to the optimal edge direction. And a binarization step S100 for performing local binarization of the pixels.

Here, in the binarizing step S100, when the maximum edge size is larger than the reference value, the maximum edge size is compared with a preset reference value, and the area to which the pixel of interest belongs is determined as an edge region. An area determining step S110 of determining a non-edge region;

When the region to which the pixel of interest belongs is determined as the edge region, adjacent pixels for selecting a plurality of direction-based adjacent pixels along the optimal edge direction among pixels masked by the edge direction detection mask in the optimal edge direction. Selection step (S120);

A local binarization step of determining the local threshold by averaging the selected direction-based neighboring pixels and performing binarization on the pixel of interest according to the local threshold; And

When the region to which the pixel of interest belongs is determined as a non-edge region, a global binarization step (S140) is performed to binarize the pixel of interest using a predetermined global threshold.

Referring to Fig. 2, the procedure of performing a localized method of an image system according to the present invention configured as described above will be described.

First of all, prior to explaining the execution procedure for the method of the present invention, the shuttle scanning based image system, which is one example of a system to which the present invention can be applied and described, will be briefly described as follows.

3 is a block diagram illustrating a shuttle scanner module as an example of a shuttle scanning-based imaging system. Referring to the configuration and operation of the shuttle scanner module in more detail, the shuttle scanner module 21 includes a CCD board, a lens, and a light source. Two modules are mounted on the same horizontal moving shaft 23 as the print head 22 and through one carriage return motor 24 and the moving belt 26. I can drive it.

4 is an exemplary view showing a shuttle block for scanning an A4 size document with a shuttle scanner module.

5 is an exemplary diagram for describing a method in which one shuttle block is scanned.

The CCD (Charge Coupled Device) used in the shuttle scanner module is a typical example of using a 128-dot CCD.

In the case of a shuttle scanning scanner employing a 128-dot CCD, when scanning at 300 dpi when the A4 (210 mm x 297 mm) format document is used, the number of dots is 2551 dots x 3507 dots. Techniques for scanning by dividing into shuttle blocks are known.

In detail, when an A4 size original is scanned with a shuttle scanner module 21 using a 128-dot CCD (2551 × 3507; based on 300 dpi), the original is divided into 27 shuttle blocks, and the shuttle scanner module 21 ) Is transported horizontally by the CR motor 24 while reading one shuttle block vertically one by one, and thus reads everything from the vertical one line to the 2551 line of the shuttle block. When the scanning of one shuttle block is completed, the shuttle scanner module 21 returns to the first line position by the CR motor 24 and the original is fed by the Line Feed Motor 25, The next shuttle block is located in the shuttle scanner module 21. When the above steps are repeated to scan all shuttle blocks from [BLOCK1] to [BLOCK 27], all the image data for one page of an A4 size document is read.

By doing so, it is possible to complete the entire data reading on the original to be scanned while overcoming the problem of low resolution CCD by using limited resources (ie, 128 dot CCD).

In an imaging system including such a shuttle scanning-based imaging system, the local thresholding method of the imaging system according to the present invention can be easily implemented in software by a central processing unit (not shown) of the imaging system, and also separately. Although it may be implemented and implemented by the hardware of the present invention, the embodiment performed by the central processing unit of the image system is a more general case and will be described based on this.

Therefore, in the following description, it is well known that the hardware subject of the present invention becomes the central processing unit of the image system.

First, in the pixel of interest specification step S10, pixels of interest to be binarized in the binarization target image are sequentially specified.

In this case, in the case of a normal image, the gray level of the binarized image is generally 256 grays representing a single pixel as 8 bits, and the bits allocated according to each application field can be added or subtracted. It is well known that while a pixel can be easily represented, a relatively large amount of resources must be allocated and a computational amount must be exponentially increased during signal processing.

Subsequently, in the edge detection step S20, after masking adjacent pixels around the pixel of interest using a plurality of edge direction detection masks, the maximum edge corresponding to the pixel of interest is calculated by calculating a direction having a maximum edge size. Detect magnitude and optimal edge direction.

6 is an exemplary diagram showing each 3x3 edge direction detection mask for four directions.

FIG. 6A is an exemplary diagram illustrating an edge direction detection mask in the 0 ° direction, and FIG. 6B is an exemplary diagram illustrating an edge direction detection mask in the 45 ° direction, and FIG. 6C. 6 is an exemplary diagram showing an edge direction detection mask in a 90 ° direction, and FIG. 6D is an exemplary diagram illustrating an edge direction detection mask in a 135 ° direction.

A to f of each edge direction detection mask with respect to the pixel of interest x are the gradation values of adjacent pixels located around the pixel of interest x selected based on the directionality of the edge.

Edge size determined by each edge direction detection mask ( Is calculated by Equation 1.

here, Represents the minimum value of Denotes an absolute value.

In a preferred embodiment according to the present invention, the output values of the edge direction detection masks in the four directions, that is, the value having the maximum value among the respective edge sizes are determined as the maximum edge size, and the edge direction at this time is determined as the optimum edge direction. Decide

To sum it up, the edge detection step S20 is an absolute value obtained by subtracting one row and one column pixel value from one row and three column pixel values masked by the edge direction detection mask in the 0 ° direction. A first step of selecting a minimum value as a first edge size from an absolute value obtained by subtracting two rows and one column pixel values from three column pixel values, and an absolute value obtained by subtracting three rows and one column pixel values from three rows and three column pixel values; Absolute value obtained by subtracting the 2-row and 1-column pixel values from the 1-row and 2-column pixel values masked by the 45 ° edge-direction detection mask, and subtracting the 3-row and 1-column pixel values from the 1-row and 3-column pixel values And a second step of selecting a minimum value as the second edge size from an absolute value obtained by subtracting the three-row and two-column pixel values from the two-row and three-column pixel values, and the first row 1 masked by the edge direction detection mask in the 90 ° direction. Absolute value by subtracting 3 rows, 1 columns of pixel values from column pixel values, 3 rows, 2 columns of pixel values from 1 row, 2 columns pixel value A third step of selecting a minimum value as the third edge size from the subtracted absolute value, the absolute value obtained by subtracting the three-row and three-column pixel values from the one-row and three-column pixel values, and masking by the edge direction detection mask in the 135 ° direction Absolute value by subtracting 3-row 2-column pixel value from 2-row 1-column pixel value, Absolute value by subtracting 3-row 3-column pixel value from 1-row 1-column pixel value, 2-row 3-column from 1-row 2-column pixel value A fourth step of selecting a minimum value as a fourth edge size among absolute values obtained by subtracting the pixel value, and a maximum value among the first edge size, the second edge size, the third edge size, and the fourth edge size; After selecting the maximum edge size, a fifth step of selecting the direction of the edge direction detection mask having the maximum edge size as the optimum edge direction is performed to determine the maximum edge size and the optimal edge direction.

Subsequently, in the binarization step (S100), it is determined whether the local binarization reflecting the characteristics of adjacent pixels located around the pixel of interest is determined using the maximum edge size, and then localized through a plurality of adjacent pixels selected according to the optimal edge direction. The threshold value is determined to perform local binarization of the pixel of interest, which will be described in more detail as follows.

First, in the region determining step (S110), when the maximum edge size is larger than the reference value, the maximum edge size is compared with a predetermined reference value, and the area to which the pixel of interest belongs is determined as an edge region. Decide on a non-edge region.

In order to clarify this, the case of the text will be described as an example. In the case of performing the region determination step S110, if the maximum edge size is larger than the reference value, the pixel of interest belongs to the boundary portion (ie, the edge region) of the text. Otherwise, it means that the pixel of interest is the inside or background of the character, not the boundary portion.

Subsequently, in an adjacent pixel selection step (S120), when the region to which the pixel of interest belongs is determined as the edge region, a plurality of pixels masked in the edge direction detection mask in the optimal edge direction are arranged along the optimal edge direction. Select adjacent pixels.

FIG. 7 is a diagram illustrating an example of selecting adjacent pixels in an edge region of the present invention. The positions of the selected adjacent pixels are pixels indicated by hatching in FIG. 7, and a of each edge direction detection mask shown in FIG. 6 is shown. It is selected in the same way as ~ f.

In operation S130, the local threshold is determined by averaging the selected adjacent pixels (S311), and then binarization is performed on the pixel of interest according to the local threshold.

That is, if the pixel of interest belonging to the edge region is larger than the local threshold, the pixel of interest is binarized to white, otherwise it is binarized to black.

On the other hand, when the region to which the pixel of interest belongs is determined as a non-edge region, the pixel of interest is binarized using a global threshold in a global binarization step S140.

That is, if the pixel of interest belonging to the non-edge region is larger than the global threshold, the pixel of interest is binarized to white, otherwise it is binarized to black.

In this case, the global threshold may be determined as half of the maximum gray scale value. Alternatively, the global threshold may be determined by multiplying all the gray scale values of the binarization target image by averaging them and multiplying the overall average by a proportional constant. .

In this case, the overall average value is for reflecting the statistical characteristics of the image to the edge determination threshold value, and the proportional constant is a parameter that can add or subtract the ratio of the classification to the edge block. In addition to using only the overall mean to reflect the statistical properties of the image in the determination of the global threshold, the standard deviation or variance of the data is compared to the overall mean to reflect more accurate statistical properties. Considering together may be considered.

Meanwhile, although the kernel size of the edge direction detection mask is 3 × 3 in the preferred embodiment of the present invention, another separate edge direction detection mask may be used, and any edge direction may be used without departing from the spirit of the present invention. You may use a detection mask.

In other words, as many convolution masks capable of detecting the direction of the edges are known through specialized books or patent applications related to image processing, any convolution mask for detecting edge direction may be used.

Since the present invention has been described through the preferred embodiments, in view of the technical difficulty aspects of the present invention, a person having ordinary skill in the art may easily change other embodiments of the present invention. It is apparent that all embodiments and modifications cited in the above description belong to the claims of the present invention.

As described in detail above, in the image system, a pixel of interest designation step of sequentially designating a pixel of interest to be binarized in a binarization target image, and adjacent pixels around the pixel of interest using a plurality of edge direction detection masks, respectively. After masking, the edge detection step of detecting the maximum edge size and the optimal edge direction corresponding to the pixel of interest by calculating the direction having the largest edge size, reflecting the characteristics of adjacent pixels located around the pixel of interest According to an embodiment of the present invention, there is provided a localization method for determining local localization using the maximum edge size, and then performing a localized value for the pixel of interest by determining a local threshold value through a plurality of adjacent pixels selected according to the optimal edge direction. According to the localized binarization method of the imaging system, binarized mas The via has the advantage that can be performed by the local binarized by using the direction of the edge at the time of binarizing a document image to minimize the Pentateuch globalization (false contouring), and suppress the occurrence of the isolated points caused by noise.

Claims (6)

In the local binarization method of the imaging system, A target pixel specifying step of sequentially specifying a pixel of interest to binarize in the binarization target image; An edge for detecting the maximum edge size and the optimal edge direction corresponding to the pixel of interest by masking adjacent pixels around the pixel of interest using a plurality of edge direction detection masks, and then calculating the direction having the largest edge size. Detection step; After determining whether localization reflects characteristics of adjacent pixels located around the target pixel using the maximum edge size, the local threshold is determined through a plurality of direction-based adjacent pixels selected according to the optimal edge direction. And a binarization step of performing local binarization on the pixels. The method of claim 1, wherein the binarization step, The maximum edge size is compared with a predetermined reference value, and when the maximum edge size is larger than the reference value, an area to which the pixel of interest belongs is determined as an edge region, otherwise, as a non-edge region. Determining an area to determine; When the region to which the pixel of interest belongs is determined as the edge region, the neighboring pixels that select the plurality of direction-based adjacent pixels along the optimal edge direction among pixels masked by the edge direction detection mask in the optimal edge direction Pixel selection step; A local binarization step of determining the local threshold using the plurality of direction-based neighboring pixels and then binarizing the pixel of interest according to the local threshold; And And a local binarization step of binarizing the pixel of interest using a predetermined global threshold when the region to which the pixel of interest belongs is determined as the non-edge region. . 3. The method of claim 2, wherein the local threshold is a value obtained by averaging the plurality of direction-based neighboring pixels. The method of claim 1, wherein the plurality of edge direction detection masks, A localized binarization method of an image system, comprising an edge direction detection mask in a 0 ° direction, an edge direction detection mask in a 45 ° direction, an edge direction detection mask in a 90 ° direction, and an edge direction detection mask in a 135 ° direction. The method of claim 4, wherein the edge detection step, Absolute value by subtracting one-row, one-column pixel value from one-row, three-column pixel value masked by the edge direction detection mask in the 0 ° direction, and subtracting two-row, one-column pixel value from two-row, three-column pixel value A first step of selecting a minimum value as a first edge size from an absolute value obtained by subtracting a 3 row 1 column pixel value from a 3 row 3 column pixel value; Absolute value obtained by subtracting the 2-row and 1-column pixel values from the 1-row and 2-column pixel values masked by the 45 ° edge-direction detection mask, and subtracting the 3-row and 1-column pixel values from the 1-row and 3-column pixel values A second step of selecting a minimum value as a second edge size from an absolute value obtained by subtracting the third row second column pixel value from the second row third column pixel value; An absolute value obtained by subtracting a 3-row one-column pixel value from a one-row, one-column pixel value masked by the 90 ° edge-direction detection mask, and an absolute value obtained by subtracting the three-row, two-column pixel value from a one-row, two-column pixel value A third step of selecting a minimum value as a third edge size among absolute values obtained by subtracting the third row and three column pixel values from the first row and three column pixel values; An absolute value obtained by subtracting a 3-row 2-column pixel value from a 2-row, 1-column pixel value masked by the edge direction detection mask in the 135 ° direction, and an absolute value obtained by subtracting a 3-row, 3-column pixel value from a 1-row, 1-column pixel value A fourth step of selecting a minimum value as a fourth edge size from an absolute value obtained by subtracting the second row and third column pixel values from the first row and second column pixel values; And After selecting the maximum value among the first edge size, the second edge size, the third edge size, the fourth edge size as the maximum edge size, the direction of the edge direction detection mask having the maximum edge size is the optimal And a fifth step of selecting in the edge direction. The method of claim 1, wherein the plurality of direction-based neighboring pixels include: And pixels masked to an edge direction detection mask having said maximum edge size.