JPS6319087A - Central point detecting method for circle and circular arc - Google Patents

Abstract

PURPOSE:To always detect the central point of a correct circle and a circular arc by counting a variable density image and an contour image, and detecting the center of the circle of the detected body having a circular outline based upon an inputted designated radius and brightness data. CONSTITUTION:An analog image from a television camera 1 is stored through an A/D converter 7 into an image memory 8. The contour of a material to be processed extracted by a contour extracting part 9 is stored into an image memory 10. An inclination counting part 11 counts the inclination of the contacting number on the line of the contour, a bright direction and a dark direction. The temporary central point of the material to be processed is counted by a central point candidate counting part 12 and stored into an image memory 13. A central point counting part 15 determined the central point of the processed material by the data of the image memory 13 and a threshold counting part and the result is outputted to an output part 16.

Description

DETAILED DESCRIPTION OF THE INVENTION (Field of Industrial Application) The present invention relates to a method for detecting the center point of a circle and an arc,
In particular, images of a workpiece having at least one circular or arcuate outline are input from a camera installed on an industrial robot, etc., the images are processed, and the center points of those circles or arcs are processed. Concerning how to detect.

(Prior technology) In recent years, with the amazing development of computers, industrial robots have been connected to cameras and given vision similar to humans, allowing them to measure the shape and position of workpieces, increasing flexibility. Industrial robots equipped with so-called artificial intelligence, which are capable of carrying out processing operations that require manual processing, are being actively developed.

By the way, as a method for processing visual images input from a camera connected to an industrial robot, binary image processing is currently most widely used in practical visual systems.

The binary image processing described above refers to the following processing.

As shown in FIG. 9(a), the workpiece 2 is photographed by the camera 1, this image is input into the computer, and this image is converted into MX.
Decompose the image into N pixels and calculate the density value of the image and its 8 degrees 1
Obtain a histogram of the number of pixels 3 for the surface.

The result of the histogram calculated by the computer is as shown in FIG. 9(d), in which a peak corresponding to the background and a peak corresponding to the workpiece 2 appear.

The density value at the valley between these two peaks is set as a threshold value t, and is used as a reference density for image processing.

Then, it is determined for each pixel whether the density is larger or smaller than the threshold value t, and if it is larger, the memory corresponding to pixel 3 is
If it is smaller than , set the memory to O.

That is, binary image processing is an image processing method in which a memory corresponding to each pixel is set to 1 or 0 depending on whether the density value of each pixel of the workpiece is larger or smaller than a threshold 1at.

The following method was used to detect the center point of the circle of the workpiece 2 using the image processing 21a as described above.

As shown in FIG. 9(a), a workpiece 2 is photographed by a camera 1, and the image is input to a combicoater having a digital image having a size of MXN pixels. The image input in this way is shown as shown in FIG. 2(b).

Next, the density value of each pixel 3 of the digital image is compared with the threshold t to create a digital image.

The calculation result is expressed as shown in FIG. 4(C).

Then, the cross-sectional moment of inertia S (x, y) of the figure shown in FIG.
, further determine the circumference of the figure, identify the workpiece that is considered to be a circle with the specified radius, and calculate S (X, V)/
By calculating A, the coordinates of the pixel corresponding to the center of gravity of the figure, that is, the center point of the circle of the workpiece 2, are determined.

(Problem to be Solved by the Invention) However, in this conventional method for detecting the center point of a circle, the process is performed using binary image processing that utilizes the contrast between the workpiece and its background. In order to accurately detect the center point of a circle, it is necessary to have a good contrast between the workpiece and its background, and the lighting conditions when photographing the workpiece with a camera must be as constant as possible. There were some limited conditions.

For example, consider the case of detecting the center point of a circle of a hollow cylindrical workpiece 4 that has been chamfered as shown in FIG. 10(a).

When the workpiece 4 is photographed with the camera 1, an image like that shown in FIG. 2(b) is input to the computer. In such an image, since the density value of the hollow part of the workpiece 4 is large,
The contrast is relatively poor, and the histogram of the density value and the number of pixels becomes as shown in FIG. 10(d), making it difficult to set a clear darkness value. Furthermore, if a plurality of workpieces 4 are input into the computer at the same time, it becomes even more difficult to set the threshold value t'.

In this way, when binary image processing is performed on the workpiece 4 with poor contrast 1, it is easily affected by the lighting conditions, so the computer may
) may be output as a calculation result, and in such a case, the center point of the circle of the workpiece 4 will be detected incorrectly. If a plurality of workpieces 4 are input into the computer at the same time, the possibility of erroneously detecting the center point as described above increases considerably.

Therefore, if a workpiece with uneven brightness inside a part with a circular outline is input to a computer as an image, or if the lighting conditions around this workpiece change,
There is a problem that the computer may perform incorrect processing, resulting in an error in detecting the center point of the workpiece.

The present invention has been made in view of the above-mentioned problems, and by calculating a gray scale image or a contour image of the workpiece, it is possible to determine whether the contrast of the workpiece is good or not and to input the workpiece simultaneously into a computer. It is an object of the present invention to detect the center point of a circle or arc of a specified radius of an object to be detected present in a workpiece without causing an error, regardless of the number of objects.

<Means for Solving the Problems> In order to achieve the above object, in the present invention, an object to be detected having a circular contour line having a different brightness with respect to the background is stored in an image memory; Calculate the tangent line passing through one point on the contour line of the detected object,
Calculate a straight line that is perpendicular to the tangent and passes through the point,
Calculate a center candidate point that is a specified radius away from the one point in either the bright direction or the dark direction relative to the background on the straight line, and calculate the center candidate point along the entire circumference on the contour line. The area where points are gathered is stored as a center candidate point area, and the point with the highest concentration of points in the center candidate point area is set as the center of a circle with a specified radius.

(Operation) According to the method described above, the center points of circles and arcs on the object to be detected are calculated based on the most densely packed point within the collection area of points obtained as a result of calculating the center candidate points. Therefore, the center point of a circle or arc can be detected regardless of the quality of the contour, and the center candidate point must always exist within the circular contour of the object to be detected. (Because the center candidate point is calculated only in one direction on the normal line of any contact point on the circular contour), even if there are multiple circles and arcs in the workpiece, The center candidate points of the respective circles and arcs do not interfere with each other, and it is possible to always accurately detect the center points of the circles and arcs.

(Example) Below, an example according to the present invention will be described in detail based on the drawings.

FIG. 1 is a block diagram of an apparatus for detecting the center point of a circle according to the present invention.

As shown in the figure, this device converts an analog image sent from a television camera 1 that captures a workpiece into a digital code using an A/l) inverter 7, and an image memory 8 that stores this digital code. , a contour extraction section 9 that calculates and extracts the contour of the JO workpiece 7 based on the data stored in the image memory 8, an image memory 1○ that stores the calculation results of the contour extraction section 9, and an image memory 1Q. An inclination calculation unit 11 that calculates the inclination of a tangent on the contour line and the bright direction and dark direction from the stored data regarding the contour of the workpiece, an image memory 8, a radius input unit 5, brightness data 6, etc. A center candidate point calculation unit 12 that calculates a temporary center point of the workpiece using human data; and an image memory 13 that stores data calculated by the center candidate point calculation unit 12.
a threshold calculation unit 14 that calculates a threshold based on the data in the image memory 13; a center point extraction unit 15 that determines the center point of the workpiece based on the data in the image memory 13 and the threshold calculation unit 14; The results calculated in section 15 are
Output unit 1 that outputs to RT, printer, or machine control device
It is composed of 6.

Next, with reference to FIGS. 3 and 4 and FIGS. 6 to 8, we will explain the process of calculating the center point of a workpiece having a circular outline using the apparatus configured as described above. This will be explained in detail based on the flowcharts in FIGS. 2 and 5.

Step 1 First, when the program starts, a video signal of the workpiece including a circular outline is sent from the television camera 1 to the A/D converter 7, where the video signal is converted into a digital signal for each pixel. The signals are converted and sequentially sent to designated addresses in the image memory 8.

For example, when a video signal of a workpiece having a cylindrical shape as shown in FIG. The data is digitized by an A/D converter 6 having a bit resolution (ability to decompose and digitize the degree of gradation from white to black into 256 steps from 0 to 255').

The density value of each pixel digitized by the A/D converter 6 is sequentially sent to the address corresponding to each pixel in the image memory 8.

Step 2 As explained in Step 1, the digitized density value of each pixel is stored in a predetermined location of the image memory 8 constituted by RAM. The image stored in the -]〇- image memory 8 is a grayscale image quantized with 8 bits.

Step 3: Data is extracted from the image memory 8, and this data is second-order differentiated using a spatial differential method to create a contour image in which only the contour of the workpiece is extracted. Here, the second-order differential, that is, the Laplacian, is a second-order differential operator that does not depend on the edge direction, and is often used in image processing. A detailed explanation of the Laplacian will be omitted here.

When image edges are determined using this Laplacian, in the case of sharp edges, a Laplacian curve as shown in Figure 3 (a) is obtained, and in the case of blurred edges, the Laplacian curve shown in Figure 3 (b) is obtained. ) is obtained.

In other words, the Laplacian curve has positive and negative peaks at the bottom and top of the edge, respectively. Therefore, to find the position of the edge, you just need to find the place where the Laplacian curve becomes ○ in the center between the positive and negative peaks of the Laplacian curve.

As described above, when the Laplacian of the grayscale image stored in the image memory 8 is calculated, a contour image as shown in FIG. 4(b) is obtained.

Step 4 The contour line extraction using the Laplacian processed in Step 3 has the property of being sensitive to noise and responding more strongly to thin lines and isolated points than to edges. Therefore, in order to extract an accurate contour of the workpiece, it is necessary to remove noise from the image.

In order to remove this noise, in the present invention, noise in the X and Y directions is removed using an operator that calculates the difference between the sums of concentrations in local regions.

Step 5 The contour line of the contour image, which has been made clearer by removing noise, is set to the maximum shading value of 255, and the background is set to O2.
Value.

That is, the contour image after noise removal is binarized by binary image processing to create a clearer contour image.

The processes from step 3 to step 5 described above are performed by the contour extraction section 9.

Step 6 After converting the noiseless grayscale image into a binary image in Step 5, convert the binarized data for each pixel to Image Memo 1/1.
Each is stored at a predetermined location of 0.

Step 7 The radius r of the detected object having a circular outline, which is the object of the television camera 1, is determined by the radius input unit 5, that is, a data input device such as a terminal, such as a keyboard, at the center candidate point. It is input to the calculation unit 12.

Step 8 The brightness data input unit 6, that is, the terminal A data input device such as a keyboard or the like is input to the center candidate point calculation unit]2.

Steps 9-13 = Calculated by the slope calculating section 11 from the image memory 8 in which the 8-bit quantized grayscale image is stored and the data regarding the outline of the workpiece stored in the image memory 10. Based on the data regarding the slope of the tangent on the contour line, the bright direction, and the dark direction, the data on the radius r of the workpiece inputted from the radius input section 5, and the brightness data inputted from the brightness data 6, The center candidate point calculation unit 12 creates an image of a center candidate point/group of the workpiece by processing a center candidate point detection program which is a subroutine of the center point detection program to be described later.

That is, for each pixel of the outline of the outline image stored in the image memory 10, the X and Y coordinates of the pixel considered as the center candidate point are determined based on the brightness data input by the brightness data input unit 6, and If all pixels on the line are calculated, an image as shown in FIG. 7 will be created.

Then, when creating the image, the density value of each pixel considered as a center candidate point is increased by 1 to form an image of a group of center candidate points.

Step 10 The data for each pixel of the contour image including the center candidate point group created in Step 9 is stored at a predetermined location in the image memory 13, respectively.

Step 11: The threshold calculation unit 14 inputs the preset darkness value t1 to the center point extraction unit 15.

Step 12 From the image memory 13 storing the image shown in FIG.
Data for each pixel is extracted, and the threshold value t1 output from the threshold value calculation unit 14 is compared with the data for each pixel, and pixels having a density value smaller than the threshold value t1 are set to a density value of ◯.

In other words, a pixel having a density value smaller than the threshold value t1 is
It will be deleted.

Step 13 The image processed in step 12 is transferred to the image memory 1 again.
The updated information is stored in the specified location in step 3.

= 15 - Step 14 The threshold value calculation unit 14 inputs the preset threshold value t2 to the center point extraction unit 15 again.

Step 15 Similar to step 12, in step 13 the image memory 13
The data of the image stored in is extracted, and the data for each pixel is compared with the threshold value t2 outputted from the threshold value calculation unit 14, and pixels having a density value smaller than the threshold value ↑2 are deleted.

Step 16 Find the coordinates of the pixel with the largest density value in the image of the collection of pixels having density values greater than the threshold value t2 processed in Step 15, set these coordinates as the center of the workpiece,
The coordinates are output to the output unit 15.

Next, the subroutine program processed in step 9 will be described in detail.

Step 20. Step 21 When data is input from the image memory 8, the inclination calculation section 11, the radius input section 5, and the brightness data input section 6, the step is initialized to the X and Y coordinates of the pixel x 1 and Yl or O.

Step 22 It is determined whether the density value at the X, Y coordinate position of the image memory 10 where the data of the contour image subjected to the binary image processing in Step 5 is stored is O. If this density value is O, step 29 is processed; otherwise, step 23 is processed.

Step 23 In step 22, if the density value of the pixel at the coordinate is not O, as shown in FIG.
The pixel 1, Yl) is located on the contour line, and based on the data stored in the image memory 7, the 8 pixels surrounding the pixel are divided as shown in FIG. 4(a), and each pixel is If the concentration values of are extracted as a matrix as shown below, the general formula can be written as follows.

Then, the changes in the density values of 8 pixels in the X and Y directions △X, △
In order to find Y, the following X is added to the matrix.
Multiply the direction change amount calculation operator and the Y direction change amount operator.

The operator for calculating the change in the X direction is: ■1 0 11 The operator for changing the cylinder in the Y direction is: Organizing this calculation using the 5obel operator, the change in the concentration value in the X direction △X is: --(a11+ 2 a21+a31) +(a 13+
2 a23+a33)...(1)...(2) In this way, pixel A(Xl) is placed on the contour line.

Yl), the amount of change Δ in the surrounding density values in the X and Y directions
X, △Y can be found.

Step 24 Pixel A (Xl
, Yl) in the X and Y directions △X, △
Based on Y, the slope θ of a straight line perpendicular to the tangent line (shown in FIG. 6) at the pixel is determined.

The slope θ of the straight line is approximately θ-1 books-1 as shown in Figure 6 using the values calculated using equations (1) and (2).
It can be determined by △Y/△X.

Step 25 Pixel A (X
i, Y'l) change in density value in the X and Y directions △X
, ΔY, and the data of the specified radius r input in step 7 described above, it is determined whether the area within the specified radius surrounding the object to be detected is brighter than its background. As a result of this judgment, if it is within the specified radius or bright, go to step 26.
Conversely, if it is dark, the process proceeds to step 27.

Step 26 Next, the X coordinate of the center candidate point is determined based on the radius r of the workpiece input in Step 7.

As shown in Figure 6, the X coordinate of a certain pixel on the contour line
1, on the straight line with the slope θ obtained in step 24,
The X coordinate of a point r away from the center candidate point, that is, the center candidate point, can be found as follows: X=X1+rcosθ.

Also, as shown in FIG. 6, a certain pixel A (7
) On the straight line passing through the X coordinate X1 and having the inclination θ obtained in step 24, a point r apart from Yl, that is, the center candidate point Y
The coordinates can be determined as follows: Y = Y l +r Si'nθ.

Step 27 Similarly to Step 26, on the straight line passing through the X coordinate X1 to a certain pixel on the contour line and at around 1 θ obtained in Step 24,
The X coordinate of a point r away from Xl, that is, the center candidate point, can be found as follows: X=X1-rcosθ.

Also, as shown in Figure 6, X to a certain pixel on the contour line
The Y coordinate of the center candidate point, which is a point r apart from Yl on the straight line passing through the coordinate X1 and having the inclination θ determined in step 24, can be determined as follows: Y=Y'1-rsaθ.

Step 2B Coordinates X of the center candidate point for a certain pixel on the contour calculated in step 26 or step 27.

Plot the pixels corresponding to Y. When plotting the pixel, the 8 degree value of this pixel is incremented (increased) by one rank compared to the density value of the pixel forming the contour line.

Step 29 If it is determined in step 22 that the 8 degree value of the pixel at the coordinates is O, or after the processing in step 2B,
It is determined whether the X coordinate of the pixel has reached 255.

If the X coordinate is 255, the process goes to step 30, and if it is not 255, the process goes to step 31.

Step 30 If it is determined in step 29 that the pixel's X coordinate is 255, the pixel's X coordinate is forcibly initialized to O.

Step 3] If it is determined in step 29 that the X coordinate of the pixel is not 255, the X coordinate of the pixel is increased by 1.

This equation can be expressed as X1=X1+'l.

Then, when step 31 is executed, the process returns to step 22 and the above-mentioned process is performed from the pixel coordinate (0°O) to (2
55, O).

Step 32 After the processing in step 30, it is determined whether the Y coordinate of the pixel has reached 240 or not.

If the Y coordinate is 240, the process goes to step 10 of the main routine; if the Y coordinate is 240, the process goes to step 33.

Step 33 If it is determined in step 32 that the Y coordinate of the pixel is not 240, the Y coordinate of the pixel is increased by 1.

This is expressed by the formula Y1=Y1+1.

When step 33 is executed, the process returns to step 22 and the previous process is performed on the pixel location! This will be done from (0°○) to (0,240>).

To summarize the subroutine program explained above, 2
For all pixels of a screen composed of 55 x 240 pixels, pixels whose density value is not O are extracted, and based on the coordinates of the pixel, the pixels whose coordinates are considered to be the center candidate point in the designated direction are plotted.

At the time of plotting, the density value of the pixel is incremented by one rank from the a degree value of the pixel forming the contour line by -23= to create an image of the center candidate point group.

Note that V2 (Xi, Yl) in step 22 indicates the pixel at the coordinates (Xl, Yl) of the image memory 10, Vl in step 23 indicates the image memory 8,
3 represents the image memory 13, respectively.

Further, FIG. 8 shows the process of determining the center of the cylinder of the cylinder head using the method for detecting the center point of circles and arcs of the present invention. The image shown in (a) is
This image is a cylinder head photographed by a television camera 1, digitally converted by an A/D converter 7, and stored in an image memory 8. Then, this image is subjected to spatial differentiation processing by the contour line extracting section 9, and only the contour line is extracted as shown in FIG.

Next, the center candidate point calculation unit 12 calculates the center candidate point based on the images (a> and (b), the radius r of the cylinder inputted in the radius input unit 5, and the brightness data inputted in the brightness data input unit 6. An image as shown in (C) including the center candidate point is created, and this image is stored in the image memory 13.Then, the threshold calculation section 14 calculates the density value below the threshold input to the center point extraction section 15. Was it created by erasing pixels that have (
This is the image shown in d). Furthermore, the pixel having the density value below the threshold value inputted to the center point extracting part 15 is deleted again in the interval 11fl calculating part 14, and the coordinates of the pixel having the largest density value among the remaining pixels are determined. The image that has undergone this final processing is the image shown in (e).

Next, as another example, without extracting the contour image,
There is a method of detecting the center point of the workpiece only from the gray scale image stored in the image memory 8.

This method performs the same processing as the program shown in the previous embodiment on all pixels of a grayscale image, and is an effective method when the capacity of the image memory is small.

Further, as another example, there is a method of detecting only from the contour image stored in the image memory 10.

−25= In this method, when finding a tangent to a circle, the inclination of the tangent is approximated from the inclination of the contour line, and the center point is found from the tangent.

In the three embodiments described above, only the calculation of the center point of the circle has been described, but the threshold value 12 outputted from the threshold value calculation unit 14 to the center point extraction unit 15 is set to be higher than the threshold value when calculating the center point of the circle. By setting a small value, it becomes possible to also find the center of an arc having a circular arc shape.

(Effects of the Invention) As is clear from the above description, in the present invention, a grayscale image and a contour image are calculated, and a circle of a detected object having a circular contour is calculated based on the input specified radius and brightness data. Since the center is detected, when the contrast between the outline of the detected object and its background is poor, or when multiple detected objects are present in the same image, even smells can be detected.
It is no longer affected by changes in lighting conditions, etc., and the center point of a circle or arc can always be accurately detected.

[Brief explanation of the drawing]

FIG. 1 is a block diagram of a device for detecting the center points of circles and arcs according to the present invention, FIG. 2 is a flowchart of a computer program built into the device shown in FIG. 1, and FIG. Flowchart of the program processed by the center candidate point calculation unit of the device shown in FIG. 1, FIG. 4 (a>
, (b) is a diagram showing the curve when the edge section is processed by Laplacian, Figures 5 and 6 are explanatory diagrams when calculating the center candidate point, and Figure 7 is the image after calculating the center candidate point. , FIG. 8 is an explanatory diagram showing the process in which an actual image is processed by the center point detection method of the present invention, and FIGS. 9 and 1Q are diagrams showing the case where the center is calculated by conventional binary image processing. FIG. 1... TV camera, 2, 4... Workpiece, 3.
...pixel. Figure 1 Figure 8 (a) (d) (b)
(e) (C) Figure 9 (a) (b) Figure 10 (a) (c) Figure 1 (b) (d) Okutomo

Claims (1)

[Claims] A detected object having a circular contour line with different brightness relative to the background is stored in an image memory, a tangent passing through a point on the contour line of the detected object stored in the image memory is calculated, and the tangent line and Calculate a straight line that is perpendicular to the point and passes through the point, calculate a center candidate point that is away from the point by a specified radius in either the bright direction or the dark direction relative to the background on the straight line, and By calculating the center candidate points for the entire circumference on the contour line, the area where the points are gathered is stored and set as the center candidate point area, and the point with the highest concentration of points in the center candidate point area is set at the specified radius. A method for detecting the center point of circles and arcs with the center of the circle as the center.