CN112710632A - Method and system for detecting high and low refractive indexes of glass beads - Google Patents

Abstract

The invention relates to a method and system for detecting the high and low refractive indices of glass microbeads. The detection method comprises: sequentially performing distortion correction, contrast enhancement, Fourier transform, contour optimization extraction and least squares fitting on a collected high refractive index image Circle-closing processing; perform distortion correction, grayscale enhancement, contrast enhancement, threshold segmentation, morphological processing, contour optimization and least squares fitting circle processing on the collected low-refractive index images in turn; according to the above processing, the radius of the contour circle and The coordinate value of the center of the circle, and then calculate the refractive index in the high-refractive index image and the low-refractive index image. The advantages of the present invention are: not only can the high refractive index and low refractive index of the glass microbeads be simultaneously detected, but also the detection efficiency and detection accuracy are greatly improved by the method of image analysis and processing.

Description

A kind of glass microbead high and low refractive index detection method and system

technical field

The invention relates to the field of refractive index detection of glass microbeads, in particular to a method and system for detecting high and low refractive indices of glass microbeads.

Background technique

Glass beads are a kind of silicate material with good chemical stability, mechanical strength and electrical insulation. Its unique feature is that it has retro-reflection characteristics to light, and is widely used in highways, railways, ports, marine transportation, Mines, tunnels, fire protection and urban construction are used as various signs, warning signs, vehicle photography and life-saving supplies, etc. With the rapid development of highway construction in my country, the amount of glass microspheres used in conjunction with road retroreflective materials has increased rapidly. Advertising signs, reflective films, reflective inks, reflective lines and other traffic safety products and facilities play an increasingly important role.

At present, the methods for measuring the refractive index of glass microspheres mainly include imaging method, primary rainbow method and secondary rainbow method, etc. At present, the above measurement technology, based on the imaging method, has complicated optical path adjustment, and it is difficult to realize the automatic quantitative measurement process. Low refractive index and poor imaging effect; based on the primary rainbow method, only glass beads with low refractive index can be measured, and based on the secondary rainbow method, only glass beads with high refractive index can be measured, and simultaneous measurement of high and low refractive index cannot be achieved. The disadvantage of single function, and the current measurement of the refractive index of glass beads is achieved by adjusting the optical path, but in general, the adjustment of the optical path is troublesome and complicated, and it needs to be measured with a ring scale, and the adjustment accuracy is not high, and the operability is poor. .

SUMMARY OF THE INVENTION

The purpose of the present invention is to overcome the shortcomings of the prior art, and to provide a method and system for detecting the high and low refractive index of glass microbeads, which solves the deficiencies in the existing methods for detecting the refractive index of glass microbeads.

The object of the present invention is achieved through the following technical solutions: a method for detecting the high and low refractive index of glass microbeads, the detection method comprises:

Distortion correction, contrast enhancement, Fourier transform, contour optimization extraction and least square fitting circle processing are performed on the collected high refractive index images in sequence; distortion correction, grayscale enhancement, and contrast enhancement are performed on the collected low refractive index images in sequence. , threshold segmentation, morphological processing, contour optimization and least squares fitting circle processing;

According to the above process, the radius of the contour circle and the coordinate value of the center of the circle are obtained, and then the refractive indices in the high-refractive index image and the low-refractive index image are calculated.

The distortion correction processing includes:

中的中心坐标设置为图像物理坐标系的原点，根据图像中每个像素在图像物理坐标系中的尺寸，得到图像像素坐标系和图像物理坐标系的转换矩阵，进而得到高折射率图像或者低折射率图像的图像物理坐标系

；Put the input high refractive index image or low refractive index image in the image pixel coordinate system

The center coordinate in the image is set as the origin of the image physical coordinate system. According to the size of each pixel in the image in the image physical coordinate system, the conversion matrix of the image pixel coordinate system and the image physical coordinate system is obtained, and then the high refractive index image or the low refractive index image is obtained. Image Physical Coordinate System for Refractive Index Images

;

转换为相机坐标系

，再将相机坐标系

转换为世界坐标系

；Convert the image to the physical coordinate system

Convert to camera coordinate system

, and then the camera coordinate system

Convert to world coordinate system

;

。Then, the camera internal parameter matrix, perspective projective matrix and scale factor are obtained, and the input high refractive index image and low refractive index image are corrected according to the camera internal parameter matrix, perspective projective matrix and scale factor.

.

通过灰度增强公式：

处理后得到灰度为s的输出图像

，实现输入图像灰度的线性扩展或压缩，将该步骤的作为输出图像

下一步骤的输入图像

。The grayscale enhancement processing includes: correcting the distortion of the input image with a grayscale of r

Through the grayscale enhancement formula:

After processing, the output image with grayscale s is obtained

, to achieve linear expansion or compression of the grayscale of the input image, and use this step as the output image

Input image for the next step

.

进行低通滤波，根据得到的灰度值和原始值进行计算得到结果值：

，得到输出图像

，实现对图像的高频区域进行强调，使图像更加清晰，将该步骤的作为输出图像

下一步骤的输入图像

。The contrast enhancement processing includes: processing the input image

Perform low-pass filtering, and calculate the resulting value according to the obtained gray value and the original value:

, get the output image

, to emphasize the high-frequency area of the image to make the image clearer, and use this step as the output image

Input image for the next step

.

灰度值满足在灰度区间[MinGray，MaxGray]的像素，将所有满足的像素点作为一个区域返回；The threshold segmentation process includes: selecting an input image

If the gray value satisfies the pixels in the gray interval [MinGray, MaxGray], return all the satisfied pixels as a region;

The morphological processing includes: sequentially performing dilation and erosion processing on the region obtained after the threshold segmentation processing, or sequentially performing erosion and dilation processing to filter out interference information in the image.

The Fourier transform process includes:

Fourier transform: Fourier transform is performed on the contrast-enhanced image to convert the time domain image into a frequency domain image;

Gaussian convolution: Convolve the Fourier-transformed image with the convolution kernel template. For a point on the image, let the origin of the convolution kernel template coincide with the point, and then convolve the point on the convolution kernel template with the convolution kernel template. Multiply the corresponding points on the image, and add the products of each point to obtain the convolution value of the point, and then process each point on the image in this way to obtain the convolution image;

Inverse Fourier Transform: Inverse Fourier transform the convolutional image to convert the frequency domain image to the time domain image.

The contour optimization extraction process includes:

Screen the contours in the image by the theoretical roundness value, and calculate the area and perimeter of the screened contours;

Calculate the actual roundness value of the circular contour through the area and perimeter, and compare the actual roundness value with the theoretical roundness value. If the actual roundness value is within the error range of the theoretical roundness value, judge the selected The contour is a circular contour. If the actual roundness value is no longer within the error range of the theoretical roundness value, re-screen and extract.

The least squares fitting circle processing includes:

和

，设样本集中的一点

到圆心的距离为

，则

；According to the equation of the circular curve

and

, let a point in the sample set

The distance to the center of the circle is

,but

;

到圆边缘的距离的平方与半径平方的差为：

；find the point

The difference between the square of the distance to the edge of the circle and the square of the radius is:

;

为

的平方和，

，

大于0，因此函数存在大于或等于0的极小值，

为对a，b，c求偏导，令偏导等于0，得到极值点：

，

，

；make

for

the sum of squares,

,

is greater than 0, so the function has a minimum value greater than or equal to 0,

In order to find the partial derivative with respect to a, b, and c, set the partial derivative equal to 0, and get the extreme point:

,

,

;

Calculate the values of a, b, c, and then get the fitted values of A, B and R.

A detection system based on the high and low refractive index detection method of glass microbeads, comprising:

Image acquisition module: acquires high and low refractive index images and transmits the acquired images to the image processing module;

Image processing module: perform distortion correction, grayscale enhancement, contrast enhancement, threshold segmentation, morphological processing, Fourier transform, contour optimization and least square fitting circle processing on the input high and low refractive index images, and then transmit the processing results to data processing module;

Data processing module: analyze and process the data to calculate the refractive index according to the radius of the circle and the imaging distance, and finally output and save the data information.

The image processing module includes a high refractive index image processing unit and a low refractive index image processing unit;

The high-refractive-index image processing unit sequentially processes the input high-refractive index image through distortion correction, contrast enhancement, Fourier transform, contour optimization extraction, and least-squares fitting circle to obtain the radius of the circular contour and the imaging distance;

The low-refractive-index image processing unit sequentially processes the input low-refractive index image through distortion correction, grayscale enhancement, contrast enhancement, threshold segmentation, morphological processing, contour optimization extraction, and least squares fitting circle to obtain a circular contour. Radius and imaging distance.

The invention has the following advantages: a method and system for detecting the high and low refractive indices of glass microbeads can not only simultaneously detect the high and low refractive indices of glass microbeads, but also greatly improve the detection rate through the method of image analysis and processing. efficiency and detection accuracy.

Description of drawings

Fig. 1 is the schematic flow chart of the method of the present invention;

Figure 2 is a schematic diagram 1 of the image pixel coordinate system to the image physical coordinate system;

Figure 3 is a schematic diagram 2 of the image pixel coordinate system to the image physical coordinate system;

Figure 4 is a schematic diagram of the camera coordinate system to the image physical coordinate system;

Figure 5 is a schematic diagram of the world coordinate system to the camera coordinate system.

Detailed ways

In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only It is a part of the embodiments of the present application, but not all of the embodiments. The components of the embodiments of the present application generally described and illustrated in the drawings herein may be arranged and designed in a variety of different configurations. Accordingly, the following detailed description of the embodiments of the present application provided in conjunction with the accompanying drawings is not intended to limit the scope of protection of the present application as claimed, but merely represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application. The present invention will be further described below with reference to the accompanying drawings.

It should be noted that in the present invention, f(x, y) represents the input image, g(x, y) represents the output image, and f(x, y) and g(x, y) in each step represent different images, That is, after the output image g(x, y) in a certain processing step goes to the next processing step, it represents the input image f(x, y) in the next processing step.

Example 1

As shown in Figure 1, the present invention relates to a method for detecting the high and low refractive index of glass microbeads, which mainly includes the following contents:

The program starts and initializes. After the software starts, it will automatically initialize, load the configuration file, detect the connection status of the camera, and enter the waiting state;

Manually adjust the position of the glass microbead sample. After the image reaches the best image acquisition effect, start the image acquisition of the high-refractive index camera or the low-refractive index camera; the high-refractive index camera and the low-refractive index camera use a separate camera and separate thread. Real-time image reading, processing, transmission and display can be realized;

After the image acquisition is successful, enter the image processing module, through a series of algorithm processing, obtain the information we want, and finally extract the radius of the circle; among them, the algorithms mainly used in the high refractive index image processing module are distortion correction, contrast enhancement, Liye transform, contour optimization extraction and least squares fitting circle; the algorithms mainly used in the low refractive index image processing module are distortion correction, grayscale enhancement, contrast enhancement, threshold segmentation, morphological processing, contour optimization extraction and least squares Multiply fit circle. This module performs a series of processing on the image through a unique algorithm, and transmits the calculation result to the data processing module;

Through the data processing module, the refractive index is calculated according to the circle radius and the imaging distance, and finally the data is output, and the measurement is over.

Further, distortion correction: the imaging process of the camera is essentially the transformation of the coordinate system. First, the points in the space are converted from the "world coordinate system" to the "camera coordinate system", then they are projected to the imaging plane (image physical coordinate system), and finally the data on the imaging plane is converted to the image pixel coordinate system. However, due to the lens manufacturing precision and the deviation of the assembly process, distortion will be introduced, resulting in the distortion of the original image. The distortion of the lens is divided into two categories: radial distortion and tangential distortion.

Radial Distortion: Distortion distributed along the radius of the lens, caused by the fact that light is more curved farther from the center of the lens than near the center. The distortion in the center of the optical axis of the imager is 0, and it moves to the edge along the radius of the lens, and the distortion becomes more and more serious. The mathematical model of distortion can be described by the first few terms of the Taylor series expansion around the principal point. Usually, the first two terms are used, namely k1 and k2. For lenses with large distortion, such as fisheye lenses, you can The third item k3 is added to describe. According to the distribution position of a point on the imager in the radial direction, the adjustment formula is:

in:

In the formula (x0, y0) is the original position of the distortion point on the imager, (x, y) is the new position after the distortion is true;

Tangential distortion: It is caused by the lens itself being not parallel to the camera sensor plane (imaging plane) or the image plane. This situation is mostly caused by the installation deviation of the lens being pasted on the lens module. The distortion model can be described by two additional parameters p1 and p2:

，

，

为径向畸变参数，

，

为切向畸变参数。综上，我们需要5个参数（

，

，

，

，

）来描述镜头畸变，本发明通过相机标定来消除相机畸变，即畸变矫正。in

,

,

is the radial distortion parameter,

,

is the tangential distortion parameter. To sum up, we need 5 parameters (

,

,

,

,

) to describe lens distortion, the present invention eliminates camera distortion through camera calibration, that is, distortion correction.

The purpose of camera calibration is to obtain the internal parameters (distortion parameters) and external parameters of the camera, and obtain the relationship between the two-dimensional plane pixel coordinates and the three-dimensional world coordinates. Four coordinate systems: camera coordinate system, image physical coordinate system, image pixel coordinate system, and world coordinate system (reference coordinate system).

Image coordinate system: It is a coordinate system in pixels, its origin is at the upper left, and the position of each pixel is expressed in pixels, so such a coordinate system is called an image pixel coordinate system (u, v ), u and v respectively represent the number of columns and rows of pixels in the digital image, but the position of pixels is not represented by physical units, so it is necessary to establish an image coordinate system represented by physical units, called the image physical coordinate system (x , y), the coordinate system is based on the intersection of the optical axis and the image plane as the origin, which is generally located in the center of the image, but due to manufacturing reasons, it will be offset in many cases. in millimeters. The two coordinate axes are respectively parallel to the image pixel coordinate system. Namely: pixel coordinates (u, v), physical coordinates (x, y).

As shown in Figure 2, if the coordinates of the origin of the image physical coordinate system in the image pixel coordinate system are (u0, v0), and the size of each pixel in the image physical coordinate system is dx, dy, then the two coordinate systems are The relationship is:

The matrix form is:

But in general, the two axes are not perpendicular to each other:

As shown in Figure 3, at this time there are:

Written in matrix form as:

As shown in Figure 4, the camera coordinate system (Xc, Yc, Zc) to the image coordinate system (x, y);

According to the similar triangle principle, we can get:

As shown in Figure 5, the world coordinate system (Xw, Yw, Zw) to the camera coordinate system (Xc, Yc, Zc);

Combining the above equations, we get:

表示摄像机内参矩阵，

表示透视摄影矩阵，s = Zc 表示尺度因子。in,

represents the camera intrinsic parameter matrix,

represents the fluoroscopy matrix, and s = Zc represents the scale factor.

Grayscale enhancement: Indicates that the grayscale of the input image is linearly expanded or compressed. The mapping function is a linear equation, the input image is f(x,y), the grayscale is r, the output image is g(x,y), and the grayscale is s; its expression is as follows: Factor, k are parameter factors;

Contrast Enhancement: Emphasizes the high frequency areas (edges and corners) of the image, making the resulting image clearer. First perform low-pass filtering (mean_image) on the image g(x,y), and calculate the result value (res) according to the obtained gray value (mean) and original value (orig), and its expression is as follows: Factor is the parameter factor

Threshold segmentation: select the pixels whose gray value satisfies the gray value interval [MinGray, MaxGray] in the input image f(x, y), and return all satisfying points as an area, and the expression is as follows:

Among them, MinGray represents the minimum gray value of the optimal gray value range, and MaxGray represents the maximum gray value of the optimal gray value range. If we need a gray value of about 100 pixels, we can set the gray interval to [ 90,110].

Morphological processing: Mathematical morphology is composed of a group of morphological algebraic operators. It has four basic operations: dilation, erosion, opening operation and closing operation. The main function is to maintain the basic characteristics of the image and remove irrelevant structure; some interference information can be filtered out by morphological processing of the region obtained after threshold segmentation

Dilate: It is the process of merging all the background points in contact with the object into the object to expand the boundary to the outside. Can be used to fill holes in objects. Dilation can be regarded as a dual operation of erosion, and its definition is: Ba is obtained by translating the structuring element B by a. If Ba hits X, we record the point a. The set of all points a satisfying the above conditions is called the result of X being inflated by B.

Erode: It is a method of eliminating boundary points and shrinking the boundary inward. Small and meaningless objects can be eliminated. The result of X being eroded by S is the set of all x's that are still in X after S has been translated by x. In other words, the set obtained by corroding X with S is the set of the origin position of S when S is completely included in X, expressed by the formula:

Open operation (Close): first expansion and then corrosion is called close (close), that is, CLOSE(X)=E(D(X)). In general, the closing operation can fill in small lakes (ie, small holes) and close small cracks, while the overall position and shape remain unchanged.

Close operation (Open): Open and close are dual operations. Erosion first and then expansion is called open, that is, OPEN(X)=D(E(X)). Function: Eliminate small objects, separate objects at the slender point, and the position and shape are always unchanged.

Fourier Transform: The frequency of an image is an indicator that characterizes the intensity of grayscale changes in the image. Each point of the frequency domain image comes from the entire original image, and there is no one-to-one correspondence between the points on the spectrogram and the points on the image. When performing image processing, it is often necessary to obtain places where the grayscale changes drastically in the image. , or the area where the grayscale changes relatively slowly, the places where the grayscale changes sharply are those places where the grayscale value changes greatly, or the place where the gradient is large, the frequency is high; the place where the grayscale changes slowly is the place where the gradient is small, The frequency is low; the function of Fourier Transform (FT, Fourier Transform) is to transform a signal from the space domain or time domain to the frequency domain. In fact, it is to convert the data from the waveform format of the abscissa time and the ordinate sample value to the spectrum format of the abscissa frequency and the ordinate amplitude (or phase). The inverse Fourier transform is to restore the frequency domain to the space domain or time domain.

Further, the Fourier transform of the contrast-enhanced image is performed, and then Gaussian convolution is performed on the Fourier image, and finally the Fourier image is restored to a spatial domain image. The features have been enhanced, which is of great help to the next contour optimization extraction;

Fourier transform formula:

Gaussian convolution: Convolve with a template (convolution kernel) and an image. For a point on the image, let the origin of the template coincide with the point, and then multiply the point on the template with the corresponding point on the image, Then the product of each point is added to obtain the convolution value of the point, and each point on the image is processed in this way to finally obtain the convolution image. Convolution is an integral operation used to find the area of the overlapping area of two curves. The pixel value of a point is replaced by the weighted average of the pixel values of its surrounding points, which can be regarded as a weighted summation, which can be used to eliminate noise and feature enhancement.

Convolution formula:

为输出图像（傅里叶变换步骤中的输出图像），

为输入图像，

为模板（卷积核），一般卷积核选用二维高斯分布函数：in

is the output image (the output image in the Fourier transform step),

for the input image,

For the template (convolution kernel), the general convolution kernel uses a two-dimensional Gaussian distribution function:

Inverse Fourier transform formula:

Among them, f(x,y) image matrix, the row and column of the x/y image.

Contour optimization extraction: The process of screening contours by roundness. For a circular contour with radius r, area S, and perimeter L, we have:

It can be seen from the above formula that C is a fixed value (theoretical value) when the contour is a standard circle, and the contour processed by the previous algorithm often has many contours, including circular contours and non-circular contours, of which only circles are The shape is what we need, so after calculating the area and perimeter of the contour, the required circular contour can be filtered out through the above formula. When the calculated actual roundness value is within the error range of ±10% of the theoretical roundness value, it means that the screened contour is a circular contour.

Least Squares Fitting Circles: Least squares analysis is a mathematical optimization technique that finds the best functional fit for a set of data by minimizing the sum of squared errors. The least squares method is a calculation method that uses the simplest method to find some absolutely unknowable true values, and minimizes the sum of squared errors to find the best matching function for a set of data. The least squares method is usually used for curve fitting. (least squares fitting). The least squares circle fitting method is a statistical-based detection method that can achieve accurate fitting localization at the sub-pixel level.

The formula for the circle:

,

,

where A and B are the coordinates of the center of the circle, and R is the radius

,

,

make

,

,

Another equation for the circular curve can be derived:

The radius parameter of the circle can be obtained by only asking for a, b, and c:

到圆心的距离为

:Set a point in the sample set (here the sample set is the set of all points on the extracted contour)

The distance to the center of the circle is

:

到圆边缘的距离的平方与半径平方的差为：point

The difference between the square of the distance to the edge of the circle and the square of the radius is:

为

的平方和make

for

sum of squares

大于0，因此函数存在大于或等于0的极小值，

为对a，b，c求偏导，令偏导等于0，得到极值点：

is greater than 0, so the function has a minimum value greater than or equal to 0,

In order to find the partial derivative with respect to a, b, and c, set the partial derivative equal to 0, and get the extreme point:

The values of a, b, and c can be obtained by solving this system of equations, and then the fitted values of A, B, and R can be obtained through the above formula.

Example 2

The invention also relates to a glass microbead high and low refractive index detection system, which comprises an initialization module, an image acquisition module, an image processing module and a data processing module.

Further, the initialization module: load system parameters, configuration files, and initialize the camera;

Image acquisition module: acquires high and low refractive index images and transmits the acquired images to the image processing module;

Image processing module: It is divided into a high-refractive index image processing module and a low-refractive index processing module, which use different algorithms respectively. The main algorithms used in the high-refractive index image processing module are distortion correction, contrast enhancement, Fourier transform, and contour optimization. Extraction and least squares fitting circle; the algorithms mainly used in the low refractive index image processing module are distortion correction, grayscale enhancement, contrast enhancement, threshold segmentation, morphological processing, contour optimization extraction and least squares fitting circle. This module performs a series of processing on the image through a unique algorithm, and transmits the calculation result to the data processing module;

Data processing module: analyze and process the data, and finally output and save the data.

The foregoing are only preferred embodiments of the present invention, and it should be understood that the present invention is not limited to the forms disclosed herein, and should not be construed as an exclusion of other embodiments, but may be used in various other combinations, modifications, and environments, and Modifications can be made within the scope of the concepts described herein, from the above teachings or from skill or knowledge in the relevant field. However, modifications and changes made by those skilled in the art do not depart from the spirit and scope of the present invention, and should all fall within the protection scope of the appended claims of the present invention.

Claims (9)

1. A method for detecting the high and low refractive indexes of glass beads is characterized by comprising the following steps: the detection method comprises the following steps:

sequentially carrying out distortion correction, contrast enhancement, Fourier transform, contour optimization extraction and least square method fitting circle processing on the acquired high-refractive-index image; sequentially carrying out distortion correction, gray level enhancement, contrast enhancement, threshold segmentation, morphological processing, contour optimization and least square method circle fitting processing on the collected low-refractive-index image;

and obtaining the radius of the contour circle and the coordinate value of the center of the circle according to the processing, and further calculating the refractive indexes in the high refractive index image and the low refractive index image.

2. The method for detecting the high and low refractive indexes of the glass microspheres according to claim 1, wherein the method comprises the following steps: the aberration correction process includes:

inputting a high refractive index image or a low refractive index image in an image pixel coordinate system

The central coordinate in the image is set as the origin of the image physical coordinate system, and the conversion matrix of the image pixel coordinate system and the image physical coordinate system is obtained according to the size of each pixel in the image physical coordinate system, so as to obtain the image physical coordinate system of the high refractive index image or the low refractive index image

；

Physical coordinate system of image

Conversion to camera coordinate system

Then the camera coordinate system

Conversion to world coordinate system

；

Further obtaining a camera internal reference matrix, a perspective projection matrix and a scale factor, and correcting the input high refractive index image and low refractive index image according to the camera internal reference matrix, the perspective projection matrix and the scale factor to obtain an image

。

3. The method for detecting the high and low refractive indexes of the glass microspheres according to claim 2, wherein the method comprises the following steps: the grayscale enhancement includes: for the gray scale of corrected distortionrIs inputted to the image

By the gray enhancement formula:

processed to obtain a gray scale ofsOutput image of (2)

Linear expansion or compression of the input image gray scale is realized, and the gray scale is enhanced

Input image as next step

。

4. The method for detecting the high and low refractive indexes of the glass microspheres according to claim 2, wherein the method comprises the following steps: the contrast enhancement includes:for input image

Low-pass filtering is carried out, and calculation is carried out according to the obtained gray value and the original value to obtain a result value:

to obtain an output image

The method can emphasize the high-frequency region of the image, make the image clearer, and enhance the contrast of the output image in the step

Input image as next step

Whereinresthe value of the result is represented by,Factorthe parameter factors are represented by a number of parameters,meanwhich represents a gray-scale value of the image,origrepresenting the original value.

5. The method for detecting the high and low refractive indexes of the glass microspheres according to claim 2, wherein the method comprises the following steps: the threshold segmentation process includes: selecting an input image

The gray value satisfies the gray range [ MinGray, MaxGray]Returning all satisfied pixel points as a region, wherein MinGray represents the minimum gray value of the optimal gray value range, and MaxGray represents the maximum gray value of the optimal gray value range;

the morphological treatment comprises the following steps: and sequentially performing expansion and corrosion treatment on the region obtained after the threshold segmentation treatment, or sequentially performing corrosion and expansion treatment to filter out interference information in the image.

6. The method for detecting the high and low refractive indexes of the glass microspheres according to claim 2, wherein the method comprises the following steps: the Fourier transform processing includes:

fourier transform: carrying out Fourier transform on the image subjected to contrast enhancement to convert the time domain image into a frequency domain image;

gaussian convolution: convolving the image subjected to Fourier transform with a convolution kernel template, enabling the origin of the convolution kernel template to coincide with a point on the image, multiplying the point on the convolution kernel template with the corresponding point on the image, adding the products of the points to obtain the convolution value of the point, and processing each point on the image in such a way to obtain a convolution image;

inverse Fourier transform: and carrying out inverse Fourier transform on the convolution image to convert the frequency domain image into a time domain image.

7. The method for detecting the high and low refractive indexes of the glass microspheres according to claim 2, wherein the method comprises the following steps: the contour optimization extraction process includes:

screening the contours in the image through a theoretical roundness value, and calculating the area and the perimeter of the screened contours;

and calculating an actual circularity value of the circular contour according to the area and the perimeter, comparing the actual circularity value with the theoretical circularity value, judging that the screened contour is the circular contour if the actual circularity value is within the error range of the theoretical circularity value, and screening and extracting again if the actual circularity value is not within the error range of the theoretical circularity value.

8. A detection system based on a glass bead high and low refractive index detection method is characterized in that: it includes:

an image acquisition module: collecting high and low refractive index images and transmitting the collected images to an image processing module;

an image processing module: carrying out distortion correction, gray level enhancement, contrast enhancement, threshold segmentation, morphological processing, Fourier transform, contour optimization and least square method circle fitting processing on the input high and low refractive index images, and then transmitting the processing result to a data processing module;

a data processing module: and analyzing and processing the data, calculating according to the circle radius and the imaging distance to obtain the refractive index, and finally outputting and storing data information.

9. The detection system based on the glass bead high and low refractive index detection method according to claim 8, characterized in that: the image processing module comprises a high-refractive-index image processing unit and a low-refractive-index image processing unit;

the high-refractive-index image processing unit processes the input high-refractive-index image through distortion correction, contrast enhancement, Fourier transform, contour optimization extraction and least square fitting circle in sequence to obtain the radius and the imaging distance of the circular contour;

and the low-refractive-index image processing unit is used for processing the input low-refractive-index image through distortion correction, gray level enhancement, contrast enhancement, threshold segmentation, morphological processing, contour optimization extraction and least square method fitting circle in sequence to obtain the radius and the imaging distance of the circular contour.

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