JP2005083776A - Grain classifier - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a grain classifier executing processing for separating accurately a grain area from a nongrain area to be cut out, even when a gradation difference that is a difference between the grain area and the nongrain area in an image data is varied by an imaging condition of grains. <P>SOLUTION: In this grain classifier for removing defective grains by judging the quality of the grains, based on the image data as to the grains acquired by image pick-up, a threshold value serving as a reference when binarization-processing the image data is determined in response to variation of the center picture element in a local area and 8 neighboring picture elements in the center picture element. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

  The present invention relates to a grain sorting device that removes defective grains by determining whether the grains are good or bad based on image data relating to the grains acquired by imaging.

2. Description of the Related Art Conventionally, there is a grain sorting device that removes defective grains by determining whether the grains are good or bad based on image data related to the grains acquired by imaging.
Such a conventional grain sorting device first performs binarization processing on the acquired image data with reference to a predetermined threshold.
Further, after the binarization process, the grain sorting apparatus executes a single grain image cut-out process by executing a process in the order of shrinkage process, expansion process, and cut-out process on the grain region in the image data. It was.

JP 2002-312762 A

However, it is known that the grain sorting apparatus as described above has the following problems.
In practice, it is generally known that the difference in density, which is the difference between the grain area and the non-grain area (that is, the background color) in the image data, changes depending on the imaging condition of the grain.
In this case, since the above-described conventional grain sorting apparatus has a threshold value serving as a reference for the binarization process fixed to a predetermined value, the grain area and the non-grain area are accurately separated and cut out. There is a problem that processing cannot be performed.
That is, for example, if the environment for imaging the grain is very bright or very dark, the difference between the grain area and the non-grain area in the acquired image data (that is, the difference in light and shade) ) Is small, there is a problem that it is impossible to accurately separate and cut out the grain region and the non-grain region.
In addition, when the difference between the grain area and the non-grain area is small for some reason, the threshold value is not set to an appropriate value (that is, an intermediate value between the grain area and the non-grain area). Therefore, the process of cutting out cannot be executed.
Even if such a threshold value is determined in advance by the user or the like, an appropriate threshold value is experienced while actually capturing the grain, performing a series of processes, and finally executing a cutout process. Because it is necessary to determine it manually, it is very time-consuming.
Furthermore, when the environment etc. which image a grain changes, since it is necessary to set the said threshold value again, there exists a problem of taking very much trouble.
Therefore, the present invention has been made in view of the above circumstances, and an object of the present invention is to execute an appropriate binarization process regardless of the situation of the grain area and the non-grain area in the image data. It is to provide a grain sorting device that makes it possible to automatically set a threshold for possible.

  The problems to be solved by the present invention are as described above. Next, means for solving the problems will be described.

That is, in the grain sorting apparatus for removing defective grains by making a judgment of good or bad of the grains based on image data relating to the grains obtained by imaging, in claim 1,
The grain sorting device is characterized in that a threshold value used as a reference when the image data is binarized is determined according to a change between a central pixel in a local region and eight neighboring pixels of the central pixel. ing.

  As effects of the present invention, the following effects can be obtained.

  According to the first aspect of the present invention, a threshold value used as a reference for the binarization process is calculated based on the image data according to the density of the pixels in the image data. Therefore, a threshold value suitable for the binarization process is automatically set. Therefore, more appropriate binarization processing can be executed as compared with the conventional case.

Hereinafter, the best mode for carrying out the present invention will be described with reference to the accompanying drawings for understanding of the present invention. The following best mode for carrying out the present invention is an example embodying the present invention, and is not intended to limit the technical scope of the present invention.
FIG. 1 is a schematic diagram schematically showing the grain sorting apparatus 1 of the present invention, FIG. 2 is a flowchart showing an example of a series of processing performed by the information processing means 4, and FIG. 3 is a local region and a peripheral region in image data FIG. 4 is a distribution diagram of pixel density in the local region.

First, the grain sorting apparatus 1 which is an example of the grain sorting apparatus of this invention is demonstrated using FIG.
Moreover, in the following description, although the case of rice is demonstrated as an example of the grain which the said grain selection apparatus 1 processes, a grain is not limited to the said rice.
Examples of grains other than the above-mentioned rice may be grains similar to rice (granular aggregates), for example, grains of wheat, soybeans, buckwheat, etc. good.
Of course, the present invention can also be applied to grains such as grains smaller than the grains etc. according to the resolution of the imaging means provided in the grain sorting apparatus 1.

  As shown in FIG. 1, the grain sorting apparatus 1 mainly includes a transport unit 2, an imaging unit 3, an information processing unit 4, and a sorting unit 5.

First, the transport unit 2 will be described in detail.
The conveying means 2 conveys the grains (grains) that are the selection target of the above-described grain sorting apparatus from the imaging means 3 toward the sorting means 5.
Here, the conveying means 2 includes a tank 11, a feeder 12 and a chute 13.

The tank 11 constitutes the most upstream part of the transport means 2 and plays the role of a container for temporarily storing grains to be sorted.
The internal shape of the tank 11 is substantially bowl-shaped, and a grain inlet 11a is formed on the upper surface.
Since it is comprised in this way, it becomes possible to throw in into the tank 11 the grain which is going to sort out from the said grain inlet 11a.
Furthermore, since the opening part 11b is formed in the lower part of the tank 11, the grain thrown in in the tank 11 is discharged | emitted from this opening part 11b.

The feeder 12 discharges the grain stored in the tank 11 from the tank 11 to the chute 13 while keeping the amount of grain discharged per unit time substantially constant.
Here, the feeder 12 is an electromagnetic type, and a feeder trough 12b is provided on the feeder base 12a via a leaf spring.
The feeder trough 12 b is a substantially rectangular parallelepiped box with one of the upper surface and the four side surfaces opened, and is located below the opening 11 b of the tank 11.
The opened side surface of the feeder trough 12b faces the upstream end of the chute 13, and the feeder trough 12b vibrates with respect to the feeder base 12a by an electromagnet provided on the feeder base 12a.

  The chute 13 is a slope having side walls. The grains discharged from the tank 11 by the feeder 12 slide down on the chute 13.

The imaging means 3 is for acquiring image data relating to the grains conveyed by the conveying means 2, and is arranged in the middle part of the conveying means 2.
Specific examples of the image pickup means 3 include cameras such as a CCD camera, other line sensors, optical sensors, and the like. Any image data may be used as long as image data of grains flowing down from the chute 13 can be acquired. The invention is not limited to the above specific example.
Here, the imaging means 3 is composed of a total of two imaging means, the upper surface imaging means 3 a and the lower surface imaging means 3 b, but image information relating to the upper and lower surfaces of the grain passing through each of the imaging means 3. Can be obtained.
Furthermore, image data of only one side of the grain may be acquired according to the type and use of the grain (grain) to be selected, and on the other hand, by providing three or more imaging means, it is more versatile. Alternatively, the image data related to the grain may be acquired.

The information processing means 4 has a function for judging goodness or badness of the grain based on the image data relating to the grain acquired by imaging by the imaging means 3, calculation, and control of the grain sorting device 1. It is what you have.
As used herein, “judgment of good or bad grain” specifically refers to so-called good rice (good grain) as “good”, and on the other hand, damaged rice, dead rice, foreign matter, etc. (defective) (Grain) is judged as “bad”.
As a specific configuration example of the information processing means 4 for making such a determination, it may be configured by an electronic computer such as a computer.
As a specific configuration of an electronic computer such as the computer, an input means such as a keyboard and a mouse, a CPU that performs calculation / judgment / image processing, and an operation program for operating the grain sorting apparatus 1 are stored and image data. For example, a storage element (RAM, ROM, hard disk drive, etc.) for expanding
The information processing means 4 is connected to a feeder base 12a, an image pickup means 3, a selection means 5, and the like, and controls each connected portion.

The sorting means 5 is a means for sorting and removing grains (damaged rice, dead rice, foreign matter, etc.) that are judged to be defective in the judgment of the quality of the grains by the information processing means 4 Located downstream of the means 2.
As a specific example of the selecting means 5 shown in FIG. 1, for example, an air gun or the like may be used.
In this case, the air gun sprays compressed air on the grain determined to be defective by the information processing means 4, and the grain determined to be defective by this spraying is converted to non-defective rice (good grain). ) Can be separated and removed from the above.
Specifically, as shown in FIG. 1, the grain determined to be defective is collected in the defective product collection unit 15 by the spraying of the sorting means 5, and on the other hand, nothing is processed by the sorting means 5 (air gun). The non-defective rice that has not been collected is collected by the non-defective product collecting unit 14.

  Regarding the grain sorting apparatus 1 having the above-described configuration, the information processing unit 4 calculates a threshold value serving as a reference when the image data is binarized based on the image data acquired from the imaging unit 3. A series of processing for doing so will be described with reference to FIG.

<Terms used below>
First, terms in the following description will be described.
FIG. 3A shows one grain region 100 in the image data.
In the grain region 100, the hatched portion indicates the grain region 100 itself, and the portions other than the hatched portion are non-grain regions serving as backgrounds other than the grain region 100.
Both the grain region and the non-grain region, that is, the region of the entire image data is referred to as a “peripheral region”.
In addition, a region having a shape with a predetermined size, such as a region indicated by a dotted rectangle in FIG.
Further, the local region is a set of 3 × 3 pixels as shown in FIG. 3B, for example.
In the set of 3 × 3 pixels, the pixel o is a “center pixel”, and the eight pixels h, i, j, k, l, m, n, and p surrounding the pixel o are “8 neighboring pixels”. Respectively.
Further, a diagram showing such a set of 3 × 3 pixels is also referred to as an “8 neighborhood diagram”. This is named because there are eight neighboring pixels around the center pixel as described above.
Further, the above-described local region can be set at any location in the image data. By setting the local region while sequentially moving in units of one pixel as described below, It becomes possible to acquire data relating to all the local areas that can be set by.

When the image processing unit 4 performs binarization processing on the image data obtained by scanning by the imaging unit 3, the information processing device 4 calculates and obtains the transition (change) of the image data value in the vicinity of 8 pixels to be processed, In accordance with this change, the threshold value used as the reference for the binarization process is also changed.
Specifically, simultaneously with scanning, an average value of shades of eight neighboring pixels with respect to the central pixel of the local region is obtained, and if the average value changes, the threshold value is also changed according to the change rate.
Further, not only the rate of change, but also a variance value of shade values of the center pixel and 8 neighboring pixels may be calculated, and the threshold value may be calculated in view of the variance value.
For example, first, the sizes of the image data acquired by the imaging means 3 in the vertical axis direction and the horizontal axis direction are calculated (S10).
The information processing means 4 initializes the average density value of the pixels in the local area (pixels in the entire 8 neighborhood diagram) and the average density value of the pixels in the peripheral area (S20).

Subsequently, the information processing means 4 calculates the average density value of the pixels in the local region and the pixels in the peripheral region (S30).
Regarding the calculation of the average density value of the local region in this case, the information processing means 4 moves all the pixels that can be set in the image data, for example, by moving the central pixel of the local region by one pixel unit in the image data. The average density value of the local area is calculated.

Next, the information processing means 4 initializes the variance value of the pixel density in the local region (S40), and calculates the variance value of the pixel density in the local region (S50).
Regarding the calculation of the variance value of the local area in this case, the information processing means 4 moves all the local pixels that can be set in the image data, for example, by moving the central pixel of the local area on a pixel-by-pixel basis. The variance value of the pixel density in the region is calculated.

  Subsequently, the information processing unit 4 performs binary processing based on the average density value of the pixels in the local area, the average density value of the pixels in the peripheral area, and the variance value of the density of the pixels in the local area, which are calculated above. A threshold value that serves as a reference for the conversion processing is calculated (S60).

An example of a specific calculation method in step S60 will be described with reference to FIG.
FIG. 4 illustrates two pixel density distributions in the local region (the horizontal axis is density and the vertical axis is the number of pixels).
FIG. 4A shows a case where the variance value V1 of the pixel in the local region is larger than a reference variance value determined in advance in the information processing means 4, while FIG. 4B shows the pixel variance value in the local region. The case where the variance value V2 is smaller than the reference variance value is shown.

<When dispersion value V1 is larger than the reference dispersion value>
As a specific example in the case where the variance value V1 is larger than the reference variance value, as shown in FIG. 4A, the interval between the average density value H1 of the pixels in the peripheral region and the density value T1 at which the number of pixels is maximized. A large case is considered.
In this case, it can be said that there is a variation in the density of pixels in the local region, and the pixels are not clearly distinguished.
In such a case, the information processing means 4 may use the average density value H1 as a threshold value serving as a reference for binarization processing.

<When dispersion value V2 is smaller than reference dispersion value>
As a specific example when the variance value V2 is smaller than the reference variance value, as shown in FIG. 4B, there are two density values where the number of pixels is maximum (density values T2 and T3), and A case where the density values T2 and T3 are close to each other can be considered.
In this case, there is not much variation in density between pixels in the local area, but the difference between the pixel with the density value T2 and the pixel with the density value T3 may not be clear.
Therefore, in such a case, in order to clarify the difference between the pixel having the density value T2 and the pixel having the density value T3, the density value S1, which is the density at the midpoint between the density value T2 and the density value T3, is set to 2. It is good also as a threshold value used as the standard of value processing.

As described above, it is possible to automatically determine an appropriate threshold value when the variance value relating to the pixel density in the image data is large and when the variance value is small. Can be executed.
That is, a threshold value used as a reference when performing binarization processing can be automatically calculated based on the image data in accordance with the density of pixels in the image data. Appropriate binarization processing can be executed.

The schematic diagram which showed typically the grain selection apparatus 1 of this invention. 7 is a flowchart showing an example of a series of processing performed by the information processing means 4. Explanatory drawing of the local area | region and peripheral area | region in image data. The pixel density distribution map in the local region.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Grain sorting apparatus 2 Conveying means 3 Imaging means 4 Information processing means 5 Sorting means 11 Tank 12 Feeder 13 Chute

Claims (1)

In the grain sorting device that removes defective grains by determining whether the grains are good or bad based on image data related to the grains obtained by imaging,
A grain sorting apparatus, wherein a threshold value used as a reference when the image data is binarized is determined according to a change between a central pixel in a local region and eight neighboring pixels of the central pixel.