JPS6234004A - Method for detecting center point of circle and circular arc - Google Patents

Abstract

PURPOSE:To detect the center point of a circle or circular arc having an indicated radius regardless of the quality of the contrast of an article to be processed, by calculating the light and shade image or contour line image of the article to be processed. CONSTITUTION:The digital code of an article to be processed sent from a television camera 1 and converted by an A/D converter 6 is stored in an image memory 7 and a contour extraction part 8 calculated and extracts the contour of the article to be processed on the basis of said stored data. A center candidate point calculation part 10 calculates the temporary center point of the article to be processed on the basis of the input data of the image memory 9 and memory 7 storing said calculated result and a radius input part 5. This calculated data is stored in an image memory 11 and a threshold calculation part 13 calculates a threshold value on the basis of said data. A center point extraction part 12 decides the center point of the article to be processed on the basis of said data and the data of the memory 11. The calculated result of the extraction part 12 is outputted to CRT, a printer or a machine control apparatus by an output part 14. By this method, the accurate center point of a circle or circular arc can be detected without being affected by the change in an illumination condition.

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, the present invention relates to a method of inputting an image of a workpiece having a circular or arcuate outline from a camera installed on an industrial robot, processing the image, and detecting the center point of the circle or arc.

(Conventional technology) In recent years, with the formative development of computers, industrial robots are connected to cameras and given vision similar to humans, allowing them to measure the shape and position of workpieces, increasing flexibility. Industrial robots with so-called artificial intelligence, which can perform certain processing tasks, 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.
The temperature value of the image is decomposed into N pixels, and a histogram of the number of pixels 3 with respect to the temperature value is obtained.

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 the reference density for single-plane 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 0, the memory is set 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 JJO object is larger or smaller than a threshold value.

The following method has been used to detect the center point of the circle of the workpiece 2 using the binary image processing described above.

As shown in FIG. 9(a), a workpiece 2 is photographed by a camera 1, and the image is manually input to a computer 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 measurement results are shown in the figure (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, y)/△
By measuring , the coordinates of a pixel corresponding to the small center of the figure, that is, the center point of the circle of the workpiece 2 are determined.

(Problems to be Solved by the Invention) However, in this conventional method for detecting the center point of a circle, binary image processing using the contrast between the workpiece and its back line is used. In order to accurately detect the center point of a circle, there are strict requirements such as the contrast between the workpiece and its background being good, and the illumination conditions when photographing the workpiece with a camera being as constant as possible. '1 was rare.

For example, consider the case of detecting the center point of a circle of a hollow cylindrical workpiece 4 that has been machined by J(1) as shown in FIG.

When the workpiece 4 is photographed by 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 threshold value t'.

In this way, when binary image processing is performed on the workpiece 4 with poor contrast, it is easily affected by the lighting conditions, so the computer may process the image as shown in FIG.
It is also conceivable that an image such as that shown in FIG.

In this way, if a workpiece with a circular outline whose interior brightness is not uniform is input into a computer as an image, or if the lighting conditions around this workpiece change, the computer will There is a possibility that processing may occur, and there is a problem that an error may occur 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 calculate the contrast of the workpiece, regardless of the quality of the contrast of the workpiece.
It is an object of this invention to be able to detect the center point of a circle or arc of a specified radius without causing any error.

(Means for Solving the Problems) In order to achieve the above object, in the present invention, a detected object having a circular contour is stored in an image memory, and the detected object having a circular contour is Calculate a tangent line passing through one point, calculate a straight line that is perpendicular to the tangent line and passing through the one point, find a center candidate point on the straight line and a specified radius away from the one point, and calculate the center candidate point of the center candidate point. The points obtained by performing the calculation for the entire circumference on the contour line are memorized and set as the center candidate point area, and the points F and the dense points are set as the center of a circle with a specified radius in the center candidate point area. It is characterized by

(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 S position 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 a Δ/D converter 6, and includes an image memory 7 that stores this digital code, and an image memory 7 that stores the digital code. a contour extraction section 8 that calculates and extracts the contour of the workpiece based on the data stored in the memory 7; an image memory 9 that stores the calculation results of the contour extraction section 8; a center candidate point calculation section 10 that calculates a temporary center point of the workpiece based on human data from the radius input section 5; and a center candidate point calculation section 1
The image memory 11 stores the data calculated in 0, and the data in the image memory] 1, the threshold value calculation unit that calculates the threshold value] 3, and the data in the image memory 11 and the threshold value calculation unit 13. A center point extraction unit 12 that determines the center point and a result calculated by the center point extraction unit 12 are
and an output unit 14 for outputting to a printer or a control device of the machine.

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

TEP1 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 6, where the video signal is converted into a digital signal for each pixel. The signal is stored in the image memory 70.
It will be sent to the specified address in sequence.

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 for each pixel digitized by the A/D converter 6 is sequentially sent to an address corresponding to each pixel in the image memory 7.

As explained in TEP2 S T E P 1, the digitized density value of each pixel is stored in a predetermined location of the image memory 7 constituted by RAM. This image memory 7
The image stored in is an 8-bit quantized grayscale image.

TEP3 Data is taken out from the image memory 7, and this data is second-order differentiated using the 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 4 (a) is obtained, and in the case of blurred edges, the Laplacian curve shown in Figure 4 (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, in order to find the edge position, it is sufficient to find the place where the Laplacian becomes O at 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 7 is calculated, a contour image as shown in FIG. 5(b) is obtained.

Contour extraction using the Laplacian processed by PEP4 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.

TEP5 The contour line of the contour image, which has been made clearer by removing noise, is set to the maximum density 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.

As described above, the processes of STEP 3 to 5TEP5 are performed by the contour extraction section 8.

S T E P 6 After converting the noiseless grayscale image into a binary image in STEP 5, the binarized data for each pixel is stored in a predetermined location in the image memory 9, respectively.

TEP7 The radius r of the workpiece having a circular outline, which is a copy of the television camera 1, is input to the center candidate point calculation unit 10 by the radius input unit 5, that is, by the data input head such as a terminal device such as a keyboard. is input.

8TEP8 Memory data of the image memory 7 in which the 8-bit muntinated grayscale image is stored and the image memory 9 in which the contour image obtained by binarizing the data in the image memory 7 is stored; And the radius r of the workpiece input from the radius manual section 5
Based on the data, the center candidate point calculation unit 10 processes the center candidate point detection program, which is a subroutine of the center point detection program described later,
Create an image of the center candidate point group of the OT object.

In other words, for each pixel of the contour of the contour image stored in the sitting image memo 9, the X, Y
When the coordinates are calculated for all pixels on the contour line, the seventh
An image like the one shown in the figure 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.

TEP9 The data for each pixel of the contour image including the center candidate point group created in STEP8 is stored at a predetermined location in the image memory 11, respectively.

TEP10 The threshold calculation unit 13 inputs the preset darkness value t1 to the center point extraction unit 12.

5TEPi1 From the image memory 11 storing the image shown in FIG.
The data for each pixel is extracted, and the threshold value t1 output from the threshold value calculation unit 13 is compared with the data for each pixel, and pixels having a density value smaller than the threshold value t1 are set to the density value O.

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

5TEP12 The image processed in STEPI 1 is updated and stored in the image memory 110 at a predetermined location again.

TEP13 The threshold calculation unit 13 inputs the preset threshold t2 to the center point extraction unit 12 again.

STEPI4 Similar to STEPI 1, image memory 1 is set to 5TEP12.
1, and extracts the image data stored in the threshold value calculation unit 13.
The threshold value t2 outputted from the threshold value t2 is compared with the data for each pixel, and pixels having density values smaller than the threshold value t2 are erased.

St t: P'15 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 14, and set these coordinates to the center of the workpiece. year,
The coordinates are output to the output unit 14.

Next, the subroutine program processed in step 8 will be described in detail.

5TEP20, 5TEP21 When data is input from the image memory 7, image memory 9, and radius input section 5 in STEP 8, the X, Y of the pixel
Coordinates X1 and Yl are initialized to O.

S T E P 22 It is determined whether the density value provided at the X and Y coordinate positions of the image memory 9 where the data of the contour image subjected to the binary image processing is stored at STl=P5 must be O. . If this concentration value is O, 5TEP28 is
P23 is processed.

ST' E P 23 In S1EP25, if the density value of the pixel at the current coordinate is not O, as shown in FIG. 5(b), this coordinate A (
The pixels Xl, Yl) are on the contour line and are stored in the image memory 7.
Based on the data stored in the pixel,
The pixels are divided as shown in FIG. 5(a), and the density values of each pixel are extracted as a matrix as shown below.
As a general expression, it can be written as.

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 the operator for calculating the change in the Y direction.If this calculation is organized using the 5obel operator, the change in the concentration value in the X direction △X is = - (a 11 + 2 a 2 a 31) + (a
13+ 2 a 23+ a 33>...(1) Also, the change in density value in the Y direction ΔY is =-(a 11+ 2 a 12 + a 13),"
(a 31+ 2832+ 833>...(2) In this way, a certain pixel Δ(Xl.

Yl), the change in the density value around it in the X and Y directions △
X and △Y are found.

TEP24 A certain pixel A (Xl
, Yl) in the X and Y directions ΔX, Δ
Based on Y, the slope θ of a straight line perpendicular to the tangent to the pixel (indicated by [in FIG. 6) is determined.

The slope θ of the straight line is approximately θ−Ti1m− as shown in FIG.
1 It can be determined by ΔY/ΔX.

S T E P 25 Next, based on the radius r of the workpiece input in 5TEP7, from the center candidate point and the center candidate point, 2r fI! Find the X coordinate of the point that passed A.

As shown in Figure 6, the necessary X coordinate of pixel A on the contour line
1, on the straight line with the slope θ determined by 5TFP24, Xl
The X coordinates of a point r away from the center candidate point, that is, a point 2r away from the center candidate point, can be found as follows: X=X1r+cosθ (decoding similarity).

S T E P 26 Similar to S1EP25, find the Y coordinates of the center candidate point and a point 2r away from the center candidate point.

As shown in FIG. 6, the Y coordinates of the center candidate point and a point 2r smaller than the center candidate point can be determined as follows: Y=Y1±rsiηθ (same order of decoding).

8-cho1=P27 Coordinates X of the center candidate point for the pixel Δ on the contour calculated in S1EP25 and S-1-EP26.

Plot the pixels corresponding to Y. When plotting the pixel, the 9 degree value of this pixel is incremented (increased) by one rank compared to the density value of the pixel forming the contour line.

In TEP28 S T E l) 22, if it is determined that the density value of a pixel at a certain coordinate is O, or after the processing in 5TEP27, it is determined whether the density value of the pixel has become 0 and the X coordinate of the pixel has become 255.

If the X coordinate is 255, proceed to 5TEP29, and if not 255, proceed to 5TEP30.

TEP29 If it is determined in STEP28 that the pixel's X coordinate is not 255, the pixel's X coordinate is increased by one.

This formula can be expressed as X1=X1+1.

Then, when S T l:= P 29 is executed, S
-1- Returning to EP22, the above-described processing is performed from the pixel coordinates (0°0) to (255,0>).

TEP30 S T E l) When it is determined in step 28 that the pixel's X coordinate is 255, the pixel's X coordinate is forcibly initialized to O.

TEP31 After the processing of S1-P31, it is determined whether the Y coordinate of the pixel has reached 240 or not.

If the Y coordinate is 240, go to step 9 of the main routine.
If it is 240, proceed to 8TEP32.

STE: P32 If it is determined in P31 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=Yl+1.

Then, when S T E P 32 is executed, 5TE
Return to P22 and repeat the process described above with the pixel coordinates (0°0)
This will be performed from (0,240).

To summarize the subroutine program explained above,
For all pixels of a screen composed of 255 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 are plotted.

During this plotting, the G degree value of the pixel is incremented by one rank compared to the density value of the pixel forming the contour line, and an image of the center candidate point group is created.

In addition, 5TEP22(7)V2 (Xi, Yl) is
Indicates the pixel at the coordinates (Xi, Yl) of the image memory 9, 5
Vl of TEP23 is the image memory 7, Vl of 5TEP27 is
3 represents the image memory 11, respectively.

Briefly, FIG. 8 shows the process of finding 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 6, and stored in an image memory 7. Then, this image is subjected to spatial differentiation processing by the contour line extraction section 8, and only the contour line is extracted as shown in FIG.

Next, based on the images (a) and (b) and the radius r of the cylinder input in the radius input unit 5, the center candidate point numbing area 1
An image as shown in (C) including the center candidate point calculated as 0 is created, and this image is stored in the image memory 11. Then, the image shown in (d) is created by the threshold calculation unit 13 erasing pixels having density values below the threshold input to the center point extraction unit 12. Roughly speaking, the threshold calculation unit 13 again erases pixels having a density value that is less than the threshold value input to the center point extraction unit 12, and calculates the coordinates of the pixel with the largest density value among the remaining pixels. 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 7.

This method performs the same processing as the program shown in the above-mentioned embodiment for all pixels of a grayscale image,
This method is effective 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 9.

In this method, when determining a tangent to a circle, the slope of the tangent is approximated from the slope of the contour line, and the center point is determined from the tangent.

In the three embodiments described above, only the center point extraction of the circle has been described, but the threshold value t2 output from the threshold value calculation unit 13 to the center point extraction unit 12 is the threshold value for extracting the center point of the circle. By setting a value smaller than , 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, the center of a circle of a detected object having a circular contour is detected based on a grayscale image and a contour image. Even in images with poor contrast between the contours of the body and its background, it is no longer affected by changes in lighting conditions, etc., and it is now possible to always accurately detect the center point of a circle or arc. Ta.

[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, and FIG. 2 is a flowchart of a computer program built in the device shown in FIG.
1 to 3 are flowcharts of the program processed after calculating the center candidate point of the apparatus shown in FIG. 1, and FIG.
), (b) are diagrams showing the curves when the -T Tsuji cross section is processed with Laplacian, Figures 5 and 6 are explanatory diagrams when the center candidate point is not output, and Figure 7 is the center candidate point Figure 8 shows the image after calculation. Figure 8 is an explanatory diagram showing the process by which an actual image is processed by the center point detection method of the present invention. Figures 9 and 10 show the center point detected by conventional binary image processing. It is necessary for explanatory diagram when squeezing out. 1... TV camera, 2, 4... Workpiece, 3.
...pixel. Patent Applicant: Nissan Motor Co., Ltd. 4th Drawing 7th 1st Variation Rough End @ 5 (a) (b
) Figure 6 Figure 7 Figure 8 (a) (d) (b) (e) (C) . ) Fig. 9 (b) (d) (su(c) Fig. 1.. (d) Large (,!, t' -J and value

Claims (1)

[Claims] storing a detected object having a circular contour in an image memory;
Calculate a tangent that passes through a point on the outline of the image stored in the image memory, calculate a straight line that is perpendicular to the tangent and passes through the point, and find a center on the straight line that is a specified radius away from the point. A candidate point is calculated, and the points obtained by calculating the center candidate point for the entire circumference on the contour line are stored and set as a center candidate point area, and the most dense point of points within the center candidate point area is stored. A method for detecting the center point of circles and arcs whose center is a circle with a specified radius.