JPH01281578A - Image processing system - Google Patents

Abstract

PURPOSE:To rapidly execute image data processing based upon Hough transformation at real time by approximately executing operation for expressing the coordinate of a point to be processed on an original image a Hough curve as operation based upon a rotating motion recurrence formula for drawing a circle. CONSTITUTION:Image data from a camera 11 are converted into edged data and the edged data are preprocessed by a preprocessing part 17 and applied to a digital differential analytical(DDA) operation part 18. In the operation part 18, the operation of the recurrence formula in each rotational angle is executed in each of arithmetic circuits 180-18n-1 and the operated result is temporarily stored in a histogram memory RAM together with density value data of each point to be processed. After ending the processing of one picture, the data of the RAM are sent to a near-by filter 19 to execute prescribed 8-near- by filtering processing. Then, prescribed sorting is executed by a sorting part 20. These operation is controlled by a CPU 22.

Description

DETAILED DESCRIPTION OF THE INVENTION [Field of Industrial Application] The present invention relates to an image processing method, and is used for controlling unmanned vehicles, real-time recognition of the motion of an observed object, and the like.

[Conventional technology]

For example, when controlling unmanned robots or autonomous vehicles, it is necessary to use a camera to capture images of identification lines displayed in advance on the road ahead, or the center line or shoulder line of the road, and process the images in real time.

Figure 10 is for explaining road recognition using images, where (a) is an image captured by a camera, and (b) is an image captured by a camera.
is the luminance (or its rate of change, etc.) in figure (a)
3 is a diagram showing pixels with high values as black dots. FIG.

As shown in Figure (a), the camera image 1 shows a road 3 extending toward infinity from the horizon 2. Road shoulder lines 4 are drawn on both sides of the road 3, and a center line 5 is drawn in the center. is depicted. Here, the shoulder line 4 and center line 5 of the road 3 have higher brightness than other parts,
Therefore, in the same figure (b), dots 4' and 5 are placed in these parts.
' will appear consecutively. In such a camera image 1, in order to recognize the traveling direction and curved shape of the road 3, an approximate straight line of the curve connecting dots 4', L2. It is sufficient to recognize L3 etc.

Traditionally, as a method for finding such an approximate straight line,
A technique called Ho,ugh (Hough) transformation is known (for example, US Pat. No. 3,069,654). This will be explained with reference to FIGS. 11 to 13. Figure 11(a)
As shown in , when a processing target point P (x, y) exists in the original image indicated by the x-y coordinate system, a p straight line flcf1. I, etc.) can be drawn infinitely. And the straight line that intersects perpendicularly to this straight line N, 1), ... and passes through b origin 0 (0,0) is also a straight line! I
, 1 , ... can be drawn one by one.

b Here, regarding the straight line passing through the origin 0 (0,0), the length to the straight line N (Ill, Ill, etc.) is ρ (ρ yu).

ρ, etc.), and the angle between it and the X axis is θ (θ, θbb

a), the above ρ of the straight line passing through this origin
.

θ is expressed as a sine curve, ie, a Hough curve, as shown in FIG.

Here, the origin 0 (0,0) and the processing target point P(x
, y) is the distance ρ from this processing target point 9
The above ρ(
ρ, ρ6. ...), which is 2 2 , 1/2 ρ - (x + y IaX p p,
When θ-〇, it becomes ρo-x.

Next, three points P lined up in a straight line as shown in Figure 12(a)
- When applying the Hough■ transformation in Figure 11 to P3, the above sine curve (
Hough curve) looks like the dotted line in Figure 12(b), the sine curve for point P2 looks like the -dotted chain line in Figure 12(b), and the sine curve for point P3 looks like the dashed line in Figure 12(b). It looks like the two-dot chain line. Here, the peaks (ρ , θ ) and (ρ , θ ) of the sine curve in FIG.
and l 1 2 2 (ρ, θ3) are respectively the origin O(0,
0) and the points p, p, p, pH ~ ρ3
and the angles θ1 to θ3 formed with the X axis.

In Figure 12(b), if you place the 6th mark at the point where the three Hough curves (sine curves) intersect, the coordinates here are (
ρ, e), which is the same as in Figure (a)! I
ρ, θ of the straight line passing through the origin 0 (0,0) perpendicular to L
It is equal to 1. Therefore, if we find the intersection point of such a sine curve, we can find the approximate straight line of the curve between the dots (black points) drawn in the x-y orthogonal coordinate system of the original image (however, in Fig. 12, this curve and the approximate straight line are Match)
can be found.

To explain this with reference to Fig. 13, first, in Fig. 13 (a), there are many dots (processing target points) to be Hough-transformed on the x-y coordinate plane (original image plane), and these are lined up on a curve. shall be.

Here, in the same figure (a), there are three approximate straight lines in the curve connecting the dots, L2. Can draw L3.

Therefore, if we convert all of these dots into sine curves (Hough transformation) as shown in Figure 11, we get
Intersection points of the sine curves as shown in FIG. 12(b) are obtained centered on three points. The coordinates of this intersection point are (ρ θ ), (ρ, 2゜t
l' tl θ ) and (ρ θ ), so this t2
If 13' t3 is expressed in the coordinate system of ρ, θ, H, where H is the frequency of appearance of the intersection point, the result will be as shown in FIG. 3(b).

Therefore, the approximate straight lines L1 to L3 of the curve corresponding to the shoulder line 4 of the road 3 as shown in FIG.
Histogram H in figure (b) (frequency of appearance of crossover points)
This can be determined by the values of ρ and θ at the peak of .

[Problem to be solved by the invention]

However, the Hough transformation method described above is
It has not been easy to apply it to high-speed and real-time image processing. This is because the coordinates fi (x, y
), to obtain the p Ho u gh curve (sine curve),
When trying to find the distance ρ from the origin to the straight line g, it is necessary to perform ρ噌X Φ sin θ + y "cos θ ... (1) p. For example, if θ is divided into 512, trigonometric function calculations will be performed 1024 times. Multiplication must be performed 1024 times and addition 512 times.If the original image to be calculated is composed of, for example, 512 x 512 pixels (processing target points), the total number of calculations is extremely large. Therefore, processing time would be significantly longer if processed using a normal processor.

Of course, the sin θ required in the above equation (1)
It is also possible to shorten the time required for calculation by storing the values of F.R.
"Hough conversion hardware using OM" 1986 IEICE 70th Anniversary General Conference, No.
.

1587). However, this method not only requires a large-capacity ROM, making the hardware large-scale, but also has a non-negligible access time. Furthermore, even if the trigonometric function data is stored in ROM in this way, (1
) formula remains the same as before, and considering that the multiplication time is considerably longer than the addition time,
This cannot be a fundamental solution. For this reason, it has been almost impossible to control a vehicle traveling at high speed in real time based on image data, or to recognize an observed object moving at high speed in real time based on image data.

Therefore, the purpose of the present invention is to provide an image processing method that can process image data to which Hough transform is applied at high speed in real time.

[Means for solving the three problems] The first aspect of the image processing method according to the present invention is to classify and extract a curve connecting a plurality of processing target points from the distribution characteristics of the plurality of processing target points on the original image. This image processing method includes the following two steps. In other words, the coordinates of a point on the circumference of an approximate circle drawn in the α-β orthogonal coordinate system are (α,
β1) and the coordinates of the next point on the circumference (α, β)
141 1+1 When the rotation angle is ε (where i is a positive integer), α1
゜1−f (α, β, ε) α 11 β −f (α, β, ε) Country β 11 The rotational motion recurrence formula is sequentially calculated for each rotation angle, for example, around one circumference of the above approximate circle. A first step of calculating and calculating H u gh convex curves corresponding to each of the plurality of processing target points on the original image, and approximation of a curve connecting the processing target points from the intersection of at least two Hough curves thus obtained. and a second step of deriving a straight line.

Further, the second aspect of the present invention includes the following four steps. That is, the first step is to obtain the initial value coordinates (α, β) from the processing target point on the original image, and the second step is to calculate the rotational motion recurrence formula described above sequentially for each rotation angle from the above initial value. Step 2, a third step of calculating a Hough curve corresponding to the processing point from the calculation result of the second step, and repeating the first to third steps for each processing point,
A fourth step of deriving an approximate straight line of a curve connecting the point to be processed from the intersection of the Hough curves of the book.

[Effect]

According to the present invention, the coordinates of the processing target point on the original image are
The calculation to represent ghlHIti can be approximately performed as a calculation of the rotational motion recurrence formula for drawing a circle, so
Multiplication and trigonometric function calculations are no longer necessary, and therefore, it is possible to quickly obtain an approximate straight line of a curve connecting the points to be processed on the original image with a small number of calculations.

〔Example〕

Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. In addition, in the description of the drawings, the same elements are given the same reference numerals, and redundant description will be omitted.

FIG. 1 is a flowchart showing an example of an image processing method according to an embodiment. First, input a pixel signal for each point to be processed on the original image captured by the camera (step 102).
, edge detection is performed (step 104), and the edged data is manually input to the preprocessing section (step 106). The above processing of steps 102 to 106 is repeated each time a pixel signal is input, and the results (edge data) are sequentially sent to a signal preprocessing section as, for example, digital data.

The preprocessing section performs predetermined (described later) preprocessing (step 108).
is executed, and the processed data is sent to the recurrence formula calculation section (step 110). This preprocessing is also repeated each time edged data is provided.

Next, the calculation of the rotational motion recurrence formula for obtaining the Hough curve (sine curve) is performed using, for example, D
D A (Dlgftal dll'f'erent[
al^nalysfs (digital differential decomposition Fr) operation (step 112), but this operation is performed on the pixel signals of one screen (original image plane) to be processed.
This continues until all processing is completed except for pixels that were excluded as outside the window or below the threshold in preprocessing (
When step 114) is completed, filtering processing (step 116) and sorting processing (118), which will be described later, are performed on the intersection points of the Hough curves, and as a final result, an approximate straight line of the curve connecting the processing target points on the original image is obtained. It turns out.

Next, the edge detection method and edged data will be explained with reference to FIG.

Now, let us assume that the original image captured by the camera is as shown in Figure 2 (a), and the line indicated by reference numeral 8 in the figure is X'
When taken out in terms of coordinates, the luminance S is shown in analog form as shown in FIG. 3(b). That is, the brightness of the outer part of the road 3 is low, and the brightness of the road 3 and the road shoulder is also low, but the brightness of the road shoulder line 4 is very high. Here, Fig. 2(b)
In the embodiment, the r4 degree distribution is recognized as, for example, 256-level digital data, but in order to accurately recognize the shape of a road, understanding the distribution of brightness itself is not sufficient.

Therefore, if we differentiate the brightness S with respect to the coordinate X'(dS/dx') and understand it as the rate of change in brightness, the edges become clear as shown in Figure 2 (C), and this can be expressed as an absolute value of 1dS/dx'l.
The result is as shown in FIG. 4(d), and edge data (digital data) for clearly recognizing the edge of the road shoulder line 4 can be obtained. Regarding this edge data,
Pretreatment is performed in the next step.

FIG. 3 is a flowchart for explaining preprocessing.

First, the edged data obtained as shown in FIG. 2 is input (step 122), and it is determined whether this is within a preset window (step 124). This window is set, for example, as shown by reference numeral 9 in FIG. 2(a). Here, whether or not the edged data is inside window 9 can be determined by the coordinate value of the data on the X-Y coordinate plane, and the next step 126 is executed only for the edged data inside window 9. Ru.

In step 126, it is determined digitally, for example, using a look-up table, whether or not the edged data within window 9 is equal to or greater than a predetermined threshold value (threshold level). If this threshold value is shown in analog form, it is set as shown by the dotted line in FIG. 2(d), for example.
The data processed in step 126 mainly becomes data (digital data) corresponding to the road shoulder line 4 and center line 5 in the original image. Therefore, only the main data (for example, data corresponding to the shoulder line and center line of the road) need to be subjected to signal processing, which will be described later, so that the overall processing speed can be increased. Note that this threshold value may be made variable. For example, if the road to be processed is bright throughout the day because it is daytime, the threshold value is set to a high level accordingly. On the other hand, when it is generally dark in the morning or evening, the threshold value may be set to a low level according to the average brightness of the entire screen.

Next, in step 128, a coordinate transformation is performed.

That is, the X-Y coordinate shown in FIG. 2(a) is converted into the X-y coordinate. By the above processing, Hough
Preprocessing for conversion ends.

Note that the order of steps 124 to 128 in FIG. 3 may be different. For example, the coordinate transformation in step 128 may be performed first, but in consideration of the time required for data processing, it is considered most preferable to perform the coordinate transformation in the order shown in FIG.

Next, application of the Hou gh transform in this embodiment will be specifically explained with reference to FIGS. 4 and 5.

P Hough song about point P (x, y) shown in Figure 4 (a)! As already explained in FIG. 11, when I (sine curve) is determined, it becomes as shown in FIG. 11(C). By the way, it is also easily understood from the theorem of trigonometric functions that the locus of such a sine curve can be replaced with the locus of circular motion as shown in FIG. In other words, Houg about point P (x, y) in figure (a)
Executing the h-transform p to obtain the Hough curve shown in FIG. 4(c) is equivalent to obtaining the circumferential locus of circular motion as shown in FIG. 2(b). Here, the radius R of the circle in FIG. ．． ．． (
2) 1 a ...(3
) laX p p .

The inventor focused on this fact and applied the recurrence formula for circular motion when drawing the circle shown in FIG. 4(b),
We have found a method that can easily perform Hough transformation of point P (x, y) to p (c) in the same figure. Here, according to the recurrence formula for circular motion above, α−
The coordinates (α , β
) is aI+l−f (a ・ β ・ ε) α 11 β −f (α , β , ε) ・・・(4) Country
It is determined as β 11 .

Several specific contents of this equation (4) have been known for a long time. For example, the rotation angle ε can be expressed as tm2-"(r
ad) (however, when m-0, 1, 2, -), al+1-"l-2βl β -2α1+β1 ... (5) it or β -2-1α +β, (1-2-") ...(6)
There are things like that. In addition, as something that the present inventor found to be more accurate and easier to calculate, or even more so, -3 厘β -α (2+ε /6) Ill 1 +β (1-2-"-1')... (8) etc. may also be used.

Therefore, this calculation will be specifically explained assuming that the recurrence formula of equation (7) above is applied.

FIG. 5 is a flow chart thereof. First, input the data that has been preprocessed according to FIG. 3 (step 132).
, find an initial value for the calculation (step 134). This initial value (α, β) is the position corresponding to the processing target point P(x,y)p in FIG. 4, that is, ρ. -X, when starting a circular motion from a position (angle) θ' = O (rad), α -y ・βo-xp ... (9) p, and starting a circular motion from an arbitrary angle θ' When starting, αom-xpsinθ'+ypcosθ'βo-xpC
O5θ' + y p s 'nθ' (10). In addition, in FIG. 4(C), the initial value (α.

β. ) is calculated as in the above equation (9), and a Hough curve is drawn. In this case, the initial values (α, β
. ) calculation becomes virtually unnecessary.

Next, the above β. After storing the values of , α and β are determined using equation (7). This is equation (9) or 10)
It can be obtained by substituting α and β determined by the formula into the formula (7) (step 136), and the result (
β , β2゜β3. ) are sequentially stored (step 138). Then, step 140) repeats this calculation for each rotation angle ε until it goes around the circle.
β the curve was memorized by above. .

β, β, β3. ...values and θ, θ, θ.

... (rotation angle ε). Below, if the processing shown in Figure 5 is executed for all processing target points on the original image, multiple Hough curves will be obtained in the ρ-θ coordinate system, and these will be as shown in Figure 12 (b). There will be a crossing point.

Here, the above recurrence formula (7) basically does not include the calculation of trigonometric functions or multiplication, and also does not require reference to the memory table (ROM), so
Calculations can be performed easily and quickly. These have sufficient accuracy (few errors) to perform circular motion.

Next, neighborhood filtering after Hough transformation is completed will be described.

Now, when the intersection of the Hough curves is expressed on the ρ-θ plane, it is assumed that it becomes as shown in FIG. 6(a). In addition, the histogram H of the intersection points appearing for each unit of the ρ-θ plane is expressed by contour lines in FIG. It doesn't match. In addition, in order to make the explanation easier to understand, this histogram focuses only on the number of intersection points, and is weighted by the grayscale data value (rate of change in brightness) of the pixel (processing target point) in the original image. isn't it.

Concentrate and other points p2. It is assumed that a concentration of intersection points is also seen in p3. Here, if we focus on the vicinity of point p1,
There is a point p4. It can be seen that concentration also occurs in p5 and the like. However, what is particularly important in image processing is to find concentrated points p1 to p3 that are far apart from each other. For example, point p1 is on the shoulder line of the road, point p2 is on the center line, and point p3 is on the curved road ahead. Compatible with road shoulder lines. On the other hand, the point p4 near the maximum concentration point p1. P5 and the like often correspond to partial bends in road shoulder lines, and in terms of image processing, they mainly correspond to noise components.

Therefore, the influence of such noise components is reduced by, for example, 8-neighborhood filtering. That is, Fig. 6 (b
) is prepared, and the histograms of the intersection points of Hough song 11 are compared for the area F-F9. Then, when the following holds true for the center area F5, the data of this area F5 is set as the data to be detected. Specifically, for example, when one unit (element area) pixel on the ρ-θ plane is assigned to F1 to F9, the number of intersection points is F −6, F2-8, F3-4, F -2
, F5-14, FB-10, F7-7, F8-9,
When it becomes F9-8, F > F −F, F −F
Since the following holds true, the intersection point of F5 is set as the data to be detected. On the other hand, F -8, F2-4, F3-3, F -14
, F5-10, F8-7, F7-9, F8-
8, F9-2, since it is F5 minus F4, the area of F5 is not treated as data to be detected.

By performing the above filtering process, point p4. The second and third concentration points p2°F3 can be detected without being affected by the presence of p5. In other words, if the above filtering is not performed, the first concentration point (!&large-large-middle-middle point p
The next concentration point after l is the nearby point p4. p5, the second
and an independent concentration point p2 to be obtained as the third concentration point.
p3 becomes the fourth and fifth concentration points, which significantly complicates subsequent signal processing.

Next, the sorting process shown as step 118 in FIG. 1 will be explained with reference to FIG.

FIG. 7 is a flow chart thereof. First, for sorting processing, input memory M1 and comparison memory MML
-MMn"" 2.3.4-) is prepared and initialized (step 152).

Next, step 154 is to input data into the input memory field.
), and only when this input data is present (step 156), the comparisons of steps 158, 162 and 166 are executed. If the input memory MIO is larger, the contents are exchanged with the corresponding comparison memory MM (step 1).
6o.

164.168). Then, finally the comparison memory MM
l~MM. will hold n pieces of input data in descending order of size.

A concrete example of this is shown in FIG.

First, four memories MM1 to MM4 are prepared as comparison memories. And the input data is '5, 7.2,
Assume that there are 10 pieces: 8, 4.9, 3.1.6.8'.

Then, the data stored in the memories MM□ to MM4 changes as shown by the arrows in FIG. will be stored. Note that this sorting process may be executed by software. By executing the above series of processes, all steps of the image processing method according to the present invention are completed. , the approximate straight line of the curve connecting multiple processing target points of the original image is the above ρ,
It is determined by the value of θ.

The present invention is not limited to the above embodiments, and various modifications are possible.

For example, the window setting shown in step 124 in FIG. 3 may be performed for a plurality of windows, and furthermore, when the windows overlap, one of the windows may be processed preferentially.

Also, the window settings themselves are not necessarily necessary,
(ough conversion may be performed on the entire original image. However, in this case, since the peripheral area of the original image indicated by the dotted line Q in FIG. 2(a) contains many noise components, When performing accurate image processing, this portion must be removed in advance.

When obtaining the histogram of the intersection point of the Hough curve, gray value data regarding the rate of change in brightness (value of the differentiated edged data) can be superimposed. For example, the Hou corresponding to the processing target point with a high rate of change in brightness
The weighting is increased for the gh curve, and the weighting is decreased for curves where the rate of change in brightness is low. If the intersection points of the Hough curves are weighted accordingly, more accurate image processing becomes possible. moreover,
The data corresponding to the luminance is digitally processed as it is without converting it into edge data by differentiation, and then Houg
Luminance weighting may be applied to the intersection point of the h curve.

It is not necessary to calculate the rotational motion recurrence formula for the entire circumference (-circumference) of the approximate circle, but for 1/2 circumference, 1/2 circumference, etc.
It may be 4 rounds or 1/8 round. For example 1/4
Calculate the circumference by O≦θ and π/2 and π≦θ<3 x /
2, other circumferences (π/2≦θ×π,
3π/2≦θ×2π) The above values can be immediately obtained from these.

Further, it is not necessarily necessary that the rotation angles are always the same, and it is not impossible to make them partially different.

Next, an example of an image processing apparatus to which the above embodiment is applied will be briefly described.

FIG. 9 is a block diagram illustrating the overall configuration. As shown in the figure, the image data obtained by the camera 11 is supplied as a pixel signal (luminance signal) of, for example, 256 gradations from the signal human power section 12.
The data is sent to the edge detection section 13, where edged data as shown in FIG. 2 is generated. This edged data corresponds to the rate of change in brightness in the pixel, and is stored in the multilevel memory 14, converted to analog data in the D/A converter 15, and displayed on the CRT display 16.

On the other hand, the edged data from the edge detection unit 13 is given to the preprocessing unit 17, where the preprocessing shown in FIG.
I F O (First-In First-Out
) method is sequentially applied to the DDA calculation unit 18.

The DDA calculation unit 18 is configured by n (n: natural number) DDA calculation circuits 18 to 18, and the above-mentioned recurrence formula is calculated according to the Vibration processing method as shown in FIG. executed. That is, D.D.
For each of the A calculation circuits 18 to 18, the calculation of the recurrence formula is executed every n-1 rotation angles. The calculation results (the aforementioned β, β, β, ...) are temporarily stored in a histogram memory (RAM) not shown, but at this time, the gray value data for each processing target point is also stored. fc! be remembered. After the processing for one screen is completed, such histogram data is sent from the histogram memory to the neighborhood filter 19, where the 8-neighborhood filtering process as shown in FIG. Sorting processing as shown in FIGS. 7 and 8 is performed.

Note that the above circuit elements are interconnected by a VME bus 21, and are controlled by a CPU 22 according to a predetermined procedure. In addition, the DDA calculation circuit 18~
18 is connected to the neighborhood filter 19 by another VME bus n-1 23, so that calculation results (histogram data consisting of address data, gray value data, etc.) are transferred.

〔Effect of the invention〕

As explained above, according to the present invention, the calculation for expressing the coordinates of the processing target point of the original image as a H u gh convex curve is as follows:
For example, as a calculation of the rotational motion recurrence formula for drawing a circle, D
Since it can be performed approximately by DA calculation, multiplication and trigonometric function calculations are not required, and therefore an approximate straight line of a curve connecting the processing target points on the original image can be obtained with a small number of calculations and without referring to a memory table. becomes possible.

Therefore, it becomes possible to process image data to which Hough transformation is applied at high speed and in real time.

[Brief explanation of the drawing]

FIG. 1 is a flowchart showing the overall configuration of an image processing method according to an embodiment of the present invention, FIG. 2 is a diagram explaining an example of an original image and edge detection of an input pixel signal, and FIG. 3 is an edge conversion diagram. Flowchart explaining data preprocessing, Part 4
5 is a flowchart illustrating calculation of the rotational motion recurrence formula in the embodiment, FIG. 6 is a diagram illustrating 8-neighborhood filtering processing, and FIG. Figure 8 is a flowchart explaining sorting processing, Figure 8 is a diagram specifically explaining sorting processing, Figure 9 is a block diagram showing the overall configuration of an image processing device to which the embodiment is applied, and Figure 10 is road recognition. Diagrams explaining Hough, Figures 11 to 13 are conventional Hough
It is a figure explaining conversion. 1... Camera image, 2... Horizon, 3... Road,
4... Road shoulder line, 5... Center line, 11.
. . . Camera, 12 . Pre-processing section, 18-DDAag section, 18-18-DDA
n-1 arithmetic circuit, 19... neighborhood filter, 20... sorting section, 21.23... VME bus, 22... C
P.U. Patent Applicant: Honda Motor Co., Ltd. Representative Patent Attorney Yoshiki Hase Flowchart of pre-processing Figure 3 Figure 5 Figure 8 Neighborhood filtering Lz Road knowledge Figure 10 Explanation of Hough transformation (I) Figure 11 of Hough transformation Explanation (■)

Claims (6)

[Claims] 1. From the characteristics of the distribution of multiple processing target points on the original image,
In an image processing method that separates and extracts a curve connecting these points to be processed, the coordinates of one point on the circumference of an approximate circle drawn in the α-β orthogonal coordinate system are (α_i, β_i), and the next point on the circumference is When the rotation angle to the coordinates (α_i_+_1, β_i_+_1) of the point is ε (where i is a positive integer), α_i_+_1=f_α(α_i, β_i, ε)β_i
The rotational motion recurrence formula ＿＋＿1=f_β(α_i, β_i, ε) is calculated sequentially for one or more rotation angles of the approximate circle or one rotation of a natural number, and a first step of obtaining Hough curves corresponding to each of the processing target points; and at least two Hough curves obtained from the first step.
an image processing method, comprising: a second step of deriving an approximate straight line of a curve connecting the processing target point from the intersection of the eight curves; 2. From the characteristics of the distribution of multiple processing target points on the original image,
In an image processing method that separates and extracts a curve connecting these processing target points, a first step of calculating initial value coordinates (α_0, β_0) in an α-β orthogonal coordinate system from the processing target point on the original image; The coordinates of one point on the circumference of the approximate circle drawn in the α-β orthogonal coordinate system are (α_i, β_i), and the rotation angle to the coordinates (α_i_+_1, β_i_+_1) of the next point on the circumference is ε. (where i is a positive integer), α_i_
+_1=f_α(α_i, β_i, ε)β_i_+_1
= f_β(α_i, β_i, ε), a second step of sequentially calculating a rotational motion recurrence formula for each rotation angle from the initial value of the first step; and a calculation result of the second step. a third step of obtaining a Hough curve corresponding to the processing point from
and a fourth step of deriving an approximate straight line of a curve connecting the processing target point from the intersection of the ugh curve. 3. In the first step, x-y of the processing target point is
When the coordinates in the orthogonal coordinate system are (x_p, y_p), α_0=-x_psinθ'+y_pcosθ'β_0
The coordinates of the initial value in the α-β orthogonal coordinate system (α_0, β_0) as =x_pcosθ′+y_psinθ′
3. The image processing method according to claim 2, wherein the step is to obtain . 4. In the first step, x-y of the processing target point is
When the coordinates in the orthogonal coordinate system are (x_p, y_p), the coordinates of the initial value in the α-β orthogonal coordinate system (α
_0, β_0). 3. The image processing method according to claim 2. 5. In the second step, the rotation angle is set to ε=2^-
When ^m, α_i_+_1=α_1(1-2^-^2^m^-^1
)-2^-^mβ_iβ_i_+_1=2^-^mα_
3. The image processing method according to claim 2, wherein the step is to calculate a recurrence formula i+β_i(1-2^-^2^m^-^1). 6. The third step is a step of obtaining a Hough curve weighted by gradation value data based on the luminance of the processing target point in the original image or the rate of change thereof, and the fourth step is a step of obtaining a Hough curve weighted by the gradation value data based on the luminance of the processing target point in the original image or the rate of change thereof. 3. The image processing method according to claim 2, further comprising the step of deriving the weighted approximate straight line.