CN103234472A - Detecting method and detecting system for fiber fineness and density of Rex-rabbit clothing hair - Google Patents

Abstract

The invention provides a method and system for detecting the fiber fineness and density of a rex rabbit coat. The method includes the following steps: a. collecting coat image data; b. transmitting the coat image data to a computer for storage; c. processing by a computer The software completes the detection analysis. The system includes a coat image data acquisition device, a coat image data storage and detection analysis device. The invention can directly detect and analyze the fur quality, coat density and density results of the live rex rabbit by taking the coat of the live rex rabbit, and has convenient operation and high efficiency; the detection system is simple in structure, convenient to carry, fast in detection speed and high in precision.

Description

A method and system for detecting the fiber fineness and density of rex rabbit coat

technical field

The invention belongs to the technical field of precision measuring instruments, and particularly relates to a method and equipment for measuring the fineness of the fur quality of fur animals such as rabbit hair, wool, mink, mohair and other circular cross-section textile fibers, and is widely used in fur products and textiles Quality management in import, export and production industries. the

Background technique

Rex rabbit, also known as Rex rabbit, originally means "king of rabbits". And because the fur of the Lex rabbit is flat and upright, full of gorgeous luster, soft and comfortable to the touch, with fine and dense hair, just like the precious fur beast otter. Therefore, it is mostly called "rex rabbit". Rex rabbit is a typical fur rabbit. Rex rabbit fur quality standard requirements are summarized as "short, thin, dense, flat, beautiful, and firm". The so-called "short" means that the wool fiber is short, and the length of the wool fiber is between 1.3 cm and 2.2 cm. "Fine" means that the diameter of the wool fiber is 16 microns to 18 microns, and there are few coarse hairs. "Dense" means thick hair, with a coat density of 10,000 to 25,000 hairs/cm ² . "Ping" means that the fluff is uniform in length and smooth. "Beauty" means that the tone is beautiful and shiny. "Long" means that the coat is firmly grown and not easy to fall off. The sale of Rex Rabbit fur in the international market has always been favored.

The 2007.04.11 announcement number is that the patent application of CN1946335 relates to a coat quality detection device (100), which has an electromagnetic radiation source (80) and an imaging sensor (74), and has a radiation selection device (83,48). During use of the device, the selection means increases the access of the emission to the part of the skin (8), homogenized in the skin via multiple scattering and without absorption, and reaches the sensor (74) and provides an image of the skin The ratio of radiation to that portion of radiation that reaches the sensor by other means, such as reflection off the skin surface. By means of this option, the contrast of the image can be improved and can be less dependent on skin color and skin artifacts, thus making it easier to detect eg a white coat on light skin. The hair coat detection equipment is mainly used for the detection equipment of the fur quality of living fur animals. The existing methods for detecting the fineness and density of the coat of rex rabbits mainly include artificial shearing counting method, microscope projection method and hair follicle fiber counting method for skin section dyeing detection. On the one hand, it is necessary to take the skin, shear the fur, damage the fur or slaughter the rex rabbit; on the other hand, the detection instrument has a complex structure, and the detection can only be performed in the laboratory. The workload is large and the cycle is long, it is not convenient for data processing, it consumes manpower and material resources, and the detection cost is high. , It is not convenient for the quality management of the production industry.

Contents of the invention

A series of concepts in simplified form are introduced in the Summary of the Invention, which will be further detailed in the Detailed Description. The summary of the invention in the present invention does not mean to limit the key features and essential technical features of the claimed technical solution, nor does it mean to try to determine the protection scope of the claimed technical solution. the

One of the purposes of the present invention is to provide a method for detecting the fiber fineness and density of rex rabbit coat, which can be directly detected and analyzed by taking wool samples from living rex rabbits, and is easy to operate and high in efficiency.

The invention also provides a detection system for the fiber fineness and density of the rex rabbit coat, which is simple in structure, convenient to carry, fast in detection speed and high in precision.

The present invention is achieved through the following technical solutions,

A kind of rex rabbit is capillary, density fiber detection method, it is characterized in that comprising the steps:

a. Collect coat image data;

b. Transmit the coat image data to the computer for storage;

c. Complete the detection and analysis through computer processing software.

According to the method of the present invention, a detector is used to directly collect coat image data from a live rex rabbit.

According to the method of the present invention, in step c, a portable data processing device is used to complete data detection and analysis.

The detector according to the present invention is specifically a DinoLite handheld USB digital microscope.

The fiber fineness and density detection system of the rex rabbit is characterized in that it includes a coat image data acquisition device, a coat image data storage and detection analysis device.

According to the system of the present invention, the image data acquisition device is a DinoLite handheld USB digital microscope.

According to the system of the present invention, the coat image data storage, detection and analysis device is a portable data processing device.

According to the system of the present invention, the microscope is mainly composed of a CCD, a convex lens, and an illumination unit.

According to the system of the present invention, for more convenient use, the portable data processing device is provided with an input/output interface and a charging port.

The invention can directly detect and analyze hair samples taken from live rex rabbits, has simple structure, is convenient to carry, has fast detection speed, high precision, and is convenient to use and operate. Specifically, it has the following four notable features:

1. Fast: Instantly measure and obtain, image data analysis, statistics, quickly obtain the detection data results of the Rex Rabbit, easy to store and retrieve, and can also be analyzed and output afterwards.

2. Portable: The instrument is designed to be hand-held, easy to operate, does not require AC power, and is suitable for field and on-site testing.

3. Non-destructive: The test is carried out directly on the live animal, without damage to the fur of the tested fur animal, and the measurement can be repeated many times.

4. Different sampling heads can be replaced to detect the fur of various animals.

Description of drawings

Fig. 1 is a schematic structural diagram of the detection system in the present invention. the

Figure 2 is a 640X480 BMP format image.

Figure 3 is the unprocessed picture.

Figure 4 is the image after grayscale processing.

Fig. 5 is the chromatic order spectrogram that Fig. 4 is made.

Figure 6 is an example diagram of the color spectrum.

FIG. 7 is a schematic diagram of pixel information of a corresponding position in an image.

Fig. 8a and Fig. 8b are illustrations of two-dimensional adaptive denoising filtering processing respectively.

Figure 9a and Figure 9b are examples of creating predefined filters.

Figures 10a and 10b are examples of boundaries in the recognition intensity image, respectively.

Fig. 11 is a schematic diagram of the cross-free appearance of the coat of a rex rabbit under a microscope.

Fig. 12 is a cross or overlapping schematic diagram of the coat of a rex rabbit under a microscope.

FIG. 13 is a schematic diagram obtained by slicing an image from left to right.

Fig. 14 is a schematic diagram of data-gray value change.

Figure 15 is a schematic diagram of statistical results.

Figure 16 is a diagram of the denoising algorithm.

Among them, 1 is a coat image data acquisition device, 2 is a coat image data storage and detection analysis device, 3 is a CCD, 4 is a convex lens, 5 is a lighting unit, 6 is an input/output interface, and 7 is a charging port.

Detailed ways

In the following description, numerous specific details are given in order to provide a more thorough understanding of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced without one or more of these details. In other examples, some technical features known in the art are not described in order to avoid confusion with the present invention. the

In order to thoroughly understand the present invention, detailed details will be provided in the following description, so as to illustrate how the present invention solves the problems that the existing communication perception evaluation system cannot collect and classify network problem areas. Obviously, the practice of the invention is not limited to specific details familiar to those skilled in the communications arts. Preferred embodiments of the present invention are described in detail below, however, the present invention may have other embodiments besides these detailed descriptions.

A method for detecting the fiber fineness and density of a rex rabbit coat is characterized in that it comprises the following steps:

a. Collect coat image data;

b. Transmit the coat image data to the computer for storage;

c. Complete the detection and analysis through computer processing software.

In the present invention, a DinoLite handheld USB digital microscope is used to directly collect coat image data from a live rex rabbit. In step c, use portable data processing equipment to complete data detection and analysis.

The present invention also provides a rex rabbit coat fiber fineness and density detection system, which is characterized in that it includes a coat image data acquisition device 1 , and a coat image data storage and detection analysis device 2 .

Further, the image data acquisition device of the present invention is a DinoLite handheld USB digital microscope.

Further, the coat image data storage and detection analysis device 2 of the present invention is a portable data processing device.

The DinoLite handheld USB digital microscope is mainly composed of a CCD3, a convex lens 4, and an illumination unit 5.

For more convenient use, the portable data processing device is provided with an input/output interface 6 and a charging port 7 .

The software functions of the Rex Rabbit Coat Detector are generally as follows: a) Collect and store the image of the Rex Rabbit coat through the video head. b) Form the image of the coat of Rex Rabbit into a format that can be analyzed and calculated. c) Design a mathematical model based on the requirements for the detection of rex rabbit coat. d) Design the result statistics interface according to the requirements of the Rex Rabbit Coat Inspection Report.

The image collection of the Rex Rabbit Coat Detector is collected through a USB2.0 independent handheld micro-magnification video head. The image acquisition part of the software is programmed for the USB device (microscopic magnification video head) by using Directshow technology programming.

Image data is divided into 2 categories: dynamic video images (avi files), static image files (bmp or jpg format). For these two types of data obtained after capture, static image files (bmp or jpg format) are used as analysis data. BMP format was adopted as the final analysis data.

Collect 640X480 BMP format images, start from the lower left corner of the BMP image and end at the upper right corner, a total of 640X480 points. Declare an array of RGB types of PBits[640][480]. The data obtained by each matrix point is an RGB value. The RGB type is composed of R, G, and B data, which represent the red R of the point respectively. , Green G, blue B ternary color values. As shown in Figure 2, it can be seen that only the RGB values of the 640X480 point data can be obtained. Since the data to be processed is white or brown or black hair, it is necessary to perform grayscale processing on the data. The process is as follows:

The RGB gray value of each point obtains a (0-255) data value through the classic calculation formula of the gray value, thus obtaining a two-dimensional array P[640][480]. Each point of this data reflects the lightness and darkness of each position of the picture. This array of arrays is a basis for subsequent mathematical modeling. Let's take a look at the results of grayscale processing. Figure 3 is an unprocessed picture, and Figure 4 is a grayscale-processed picture, where you can see the difference between the grayscale value of the hair position and the background color.

Data model analysis process

What is obtained is only a data array of P[640][480].

The first step of processing color scale analysis:

For example, perform color scale analysis on the previous figure to obtain the color scale spectrum shown in Figure 5. From Figure 5, we can see a subsection of the color scale, which can be used to separate the background color from the useful data, and then analyze and process the data of the hair part. Of course, not every picture can be processed effectively. Taking Figure 6 as an example, it can be seen that when the color scale is defined as 109, the hair part cannot be displayed well, and the background color is mixed in it. This indicates that the base color and the hair color are too close, or that the hair color and the base color are seriously crossed. In other words, for example, the hair color scale is between (110-130), while the environment color and background color are between (80-150), which makes it impossible to remove useful data. Because of the grayscale processing, the 3-primary color becomes a 1-primary color. This process causes data to be lost, but grayscale processing is the simplest method for image level elimination. Therefore, the mathematical model cannot be divided simply by referring to the grayscale processing, and it is necessary to carry out another modeling method based on the 3-primary color basis. For example, the three colors of R color, G color and B color are segmented and refined. But there is a very difficult problem here. Most of the hair colors are white, gray, black, and brown. The RGB values of these colors are close to 3 equal parts. So even if the 3-color segmentation analysis is performed, the actual effect is the same as the gray-scale segmentation. Therefore, the data source needs to be able to effectively distinguish the hair from the environment color. This requires the addition of background color to distinguish. Imagine that after the background color and hair color can be effectively distinguished, the data coordinate position of the hair can be obtained. This coordinate position point corresponds to the array position (horizontal, column position) of the two-dimensional data of P[640][480]. With these positions, and then using an effective and feasible data model, the results such as the diameter, quantity, and number of hairs per unit area can be measured.

In fact, the process of calculating the number of hair roots is the so-called data aggregation analysis.

The hair diameter calculation is a data slope statistical calculation plus geometric shape calculation modeling analysis.

4) Establishment of data model

First, the data structure for the processed data. As shown in Figure 7, the plastic two-dimensional array P[640][480] contains 640*480 points, and the value corresponding to (X, Y) of each point corresponds to the pixel information of the corresponding position of the image.

Since the data of each point of the processed image is no longer an RGB value, but a gray value, the information of each point has only one value. Furthermore, P[640][480] can be regarded as a matrix array data. When designing the model, design based on this data array. There is also a very important mathematical tool Matlab that needs to be used here. Matlab can perform a large number of complex mathematical calculations. Because the image cannot be completely processed according to the mathematical model. In order to better establish and design the model, it is necessary to blur and denoise the image data. The color scale transformation of the processed image is more stable, and such data is more conducive to analysis.

Use the powerful image data processing function of Matlab to denoise the image first: wiener2

Function:

Carry out two-dimensional adaptive denoising and filtering.

grammar:

J = wiener2(I,[m n],noise)

[J,noise] = wiener2(I,[m n])

example

I = imread('saturn. GIF');

J = imnoise(I,'gaussian',0,0.005);

K = wiener2(J,[5 5]);

imshow(J)

figure, imshow(K)

As shown in Fig. 8a and Fig. 8b, all processing operations must be performed around the initial image data format and the two-dimensional array after digitization. Sometimes the image effect of the rex rabbit coat is not ideal, and when the environmental noise is too large, it needs to do filtering and other operations.

freqspace

Function:

Determine the frequency space for the frequency response in 2D.

grammar:

[f1,f2] = freqspace(n)

[f1,f2] = freqspace([m n])

[x1,y1] = freqspace(...,'meshgrid')

f = freqspace(N)

f = freqspace(N,'whole')

Related commands:

fsamp2, fwind1, fwind2

freqz2

Function:

Computes the frequency response in 2D.

grammar:

[H,f1,f2] = freqz2(h,n1,n2)

[H,f1,f2] = freqz2(h,[n2 n1])

[H,f1,f2] = freqz2(h,f1,f2)

[H,f1,f2] = freqz2(h)

[...] = freqz2(h,...,[dx dy])

[...] = freqz2(h,...,dx)

freqz2(...)

example

Hd = zeros(16,16);

Hd(5:12,5:12) = 1;

Hd(7:10,7:10) = 0;

h = fwind1(Hd, bartlett(16));

colormap(jet(64))

freqz2(h,[32 32]); axis ([–1 1 –1 1 0 1])

fsamp2

Function:

Design two-dimensional FIR filter by frequency sampling method.

grammar:

h = fsamp2(Hd)

h = fsamp2(f1,f2,Hd,[m n])

example

[f1,f2] = freqspace(21,'meshgrid');

Hd = ones(21);

r = sqrt(f1.^2 + f2.^2);

Hd((r<0.1)|(r>0.5)) = 0;

colormap(jet(64))

mesh(f1,f2,Hd)

Related commands:

conv2, filter2, freqspace, ftrans2, fwind1, fwind2

fspecial

Function:

Create a predefined filter.

grammar:

h = fspecial(type)

h = fspecial(type,parameters)

example

I = imread('saturn. GIF');

h = fspecial('unsharp',0.5);

I2 = filter2(h,I)/255;

imshow(I)

figure, imshow(I2)

As shown in Figure 9a and Figure 9b.

Related commands:

conv2, edge, filter2, fsamp2, fwind1, fwind2

ftrans2

Function:

Design 2-D FIR Filters by Frequency Transformation.

grammar:

h = ftrans2(b,t)

h = ftrans2(b)

example

colormap(jet(64))

b = remez(10,[0 0.05 0.15 0.55 0.65 1],[0 0 1 1 0 0]);

[H,w] = freqz(b,1,128,'whole');

plot(w/pi–1,fftshift(abs(H)))

Related commands:

conv2, filter2, fsamp2, fwind1, fwind2

fwind1

Function:

Design a 2D FIR filter with a 1D window method.

grammar:

h = fwind1(Hd,win)

h = fwind1(Hd,win1,win2)

h = fwind1(f1,f2,Hd,...)

example

[f1,f2] = freqspace(21,'meshgrid');

Hd = ones(21);

r = sqrt(f1.^2 + f2.^2);

Hd((r<0.1)|(r>0.5)) = 0;

colormap(jet(64))

mesh(f1,f2,Hd)

Related commands:

Conv2, filter2, fsamp2, freqspace, ftrans2, fwind2

fwind2

Function:

Design a 2D FIR filter with a 2D window method.

grammar:

h = fwind2(Hd,win)

h = fwind2(f1,f2,Hd,win)

example

[f1,f2] = freqspace(21,'meshgrid');

Hd = ones(21);

r = sqrt(f1.^2 + f2.^2);

Hd((r<0.1)|(r>0.5)) = 0;

colormap(jet(64))

mesh(f1,f2,Hd)

Related commands:

conv2, filter2, fsamp2, freqspace, ftrans2, fwind1

After these complex operations the image data is ready for analysis. A very important operation will be used at this time, that is, edge finding. edge

Function:

Identify boundaries in an intensity image.

grammar:

BW = edge(I,'sobel')

BW = edge(I,'sobel',thresh)

BW = edge(I,'sobel',thresh,direction)

[BW,thresh] = edge(I,'sobel',...)

BW = edge(I,'prewitt')

BW = edge(I,'prewitt',thresh)

BW = edge(I,'prewitt',thresh,direction)

[BW,thresh] = edge(I,'prewitt',...)

BW = edge(I,'roberts')

BW = edge(I,'roberts',thresh)

[BW,thresh] = edge(I,'roberts',...)

BW = edge(I,'log')

BW = edge(I,'log',thresh)

BW = edge(I,'log',thresh,sigma)

[BW,threshold] = edge(I,'log',...)

BW = edge(I,'zerocross',thresh,h)

[BW,thresh] = edge(I,'zerocross',...)

BW = edge(I,'canny')

BW = edge(I,'canny',thresh)

BW = edge(I,'canny',thresh,sigma)

[BW,threshold] = edge(I,'canny',...)

example

I = imread('rice. GIF');

BW1 = edge(I,'prewitt');

BW2 = edge(I,'canny');

imshow(BW1);

figure, imshow(BW2)

As shown in Figure 10a and Figure 10b.

Using the interface between VC and Matlab, use VC to call a large number of useful image data processing functions in the Matlab kernel to complete the above image processing operations. The final data is the raw data for the formal establishment of the algorithm. Here it is defined as PCAL [640] [480] The calculation data will be modeled below.

First look at the original data prototype. When the data prototype is ideal, it looks like this:

Situation 1: As shown in Figure 11, all the coats of Rex rabbits show no cross under the microscope. But this is impossible. ideally like this

Situation 2: As shown in Figure 12, the coat is crossed or overlapped, which is very common and happens a lot.

Situation 3: The coat image is not clear and the edges are fuzzy.

This situation cannot be judged well even after denoising, blurring, filtering, edge recognition and other operations. ``

To sum up, even with a complex human brain, it is difficult to identify. On the one hand, it is required to start from the results of the Rex Rabbit Coat Tester, so that the direction of the coat is as consistent as possible and the overlap is reduced. On the other hand, it is necessary to find a feasible statistical method based on the theory of probability and statistics.

Starting from the signal analysis and processing theory, a feasible mathematical model algorithm is designed, coupled with the calculation of probability and statistics, which can basically meet the error requirements of the rex rabbit coat detection results. ``

Signal analysis generally deals with signals that are point-to-point data and serialized data. Assuming that all hairs "grow" downward from the top of the image, this requirement can be achieved by adjusting the structure of the data acquisition head. Then assuming that all the hairs grow downward from the top of the image, it is certain that if the image is sliced from left to right, as shown in Figure 13.

You can see the position of the red line, and the transformation of the pixel point can be represented by a continuous serialized signal:

For example: black, black, black, white, white, white, black, black, black, white, white. This serialized data.

Of course, it is impossible to obtain such clear data from the actual image through the above operations. The gray value is from 0-255, 0 represents extremely black, 255 represents extremely white, here is a set of actual data after serialization.

It can be seen from Figure 14 that in fact, there are a total of 640 data from the left to right of the image gray value of a certain row, and the change of this data reflects the change of the gray value. The established data model requires the identification of the average gray level of the background color, and based on this value, a threshold line is defined. Part of the pixel position is sorted out. In this way, the continuous coat value can be partially obtained. The point length of this continuous coat copy is the "false diameter" of the coat in this slice group. The reason why it is defined as "false diameter" is because this diameter is not related to hair. The growth direction is vertical and needs to be corrected. It can be seen from here that a statistical result is shown in Figure 15.

As shown in the figure above, if 4 groups of copies above the threshold line are found in the slice group, then it can be seen that the continuous coat color part contains 4 groups, which can be considered as the hair of this slice part Through the number of roots. If N slices are made on a picture, then after the N slices are subjected to probability statistics, a credible hair root value can be obtained, and the error of this value should be accepted within the statistical error. 

But there will be another situation here, the hairs cross and overlap, especially when 2 or more hairs overlap and cross, the amount of data in a certain hair group in a single slice group will greatly exceed that of a single coat through the slice group normal pixel count. So it is necessary to design a mathematical processing formula to divide the overlapping hairs. After the division, the number of hair groups in a single slice group is the number of hair roots. Carry out the hair root values of N slice groups, remove the largest and smallest values, and the value after stacking and averaging is the credible number of hair roots. ``

As for the hair diameter, it is necessary to compare the diameters of different slice groups and design a slope group. This slope group represents that for each hair slice group, the information of the hair collection position forms a set group and generates a straight line formula. The slope of the straight line formula is the correction value of the hair diameter. After correction, the true diameter of the hair can be obtained. ``

Here is a detailed description of the design of the data threshold line and the method of classification statistics:

From the above, we know that each slice group is actually the data sequence of each row corresponding to the image data. If the hair passes through this column, only the part where the hair passes is the color of the hair, and the rest is the background color. Since the background color is defined as black, even if there is light interference, the color of the hair will not appear. So it's easy to judge. ``

For example, you can look at the direction of the color in this way. It starts with the background color and moves from left to right. When the fur part starts to appear, the color starts to become lighter, that is, the gray value starts to increase from a small value. When it reaches a certain value, It is the jitter of the part behind the edge of the coat, but the jitter of this value is within the range of the coat color, and then slowly leaves the coat part, and when it leaves, it changes from large to small again and enters the background color part , and then cycle, the data that needs to be counted is when the coat starts to appear and when it leaves the coat part. The maintained coat part is the "false length" of the coat. The number of hairs on this slice group.

The array defining this slice group is B [640]

First calculate the maximum gray value Bmax and the minimum gray value Bmin of the array, and define BY=(Bmax+Bmin)/2 as the threshold line. Then the threshold line statistics part program can be designed as follows. Due to the problem of small pixel jitter, a threshold line jitter noise processing method must be designed to define that the color jitter pixel value does not exceed 5, then quantize the values of the upper and lower 5 pixels of BY, if a pixel value is greater than BY- 5, less than BY+5, then this value is regarded as BY-5, then these values are always less than BY+5, then only when a value above BY+5 is found, the pixel value will be considered to have crossed the threshold line BY, so that Small jitter can be removed.

Design of statistical results report

The design of the statistical result report must be implemented in accordance with the design requirements of the general analysis software. It generally needs to include the following contents:

1) Single image statistics, even after clicking "collect", the image starts to be collected, and then the current image needs to be analyzed and counted.

2) After a single image is counted, it must be added to the backup database. For example, after counting multiple parts of a Rex rabbit, it is necessary to integrate and average the data of several counts. ``

3) Form reports and histograms, which are convenient for users to observe. 

4) The measured results of each rex rabbit can be saved and loaded at any time.

5) Printing function, because the most advanced UMPC embedded operating system technology is used, the handheld portable system supports Windows printing function, so it only needs to be connected to a general-purpose printer, and only need to write the required printing data format. ``

The entire software design process uses DirectShow, GDI image programming, and uses VC++ programming tools to write software.

The most critical steps in the realization of the entire software are:

1. Establishment of mathematical model

The quality of the mathematical model directly affects the reliability and repeatability of the data results, and even directly affects whether the results can be measured. ``

2. The collection of data sources is good or bad

The quality of the so-called data source has also been mentioned before. This process is the most difficult step besides the establishment of the data model. After all, no matter how well the computer software is designed, no matter how intelligent the model is established, the data source is not ideal, and it cannot be realized. Measurement. The analysis function of computer programming design cannot be compared with that of the human brain. Perhaps the things that can be distinguished by the human naked eye cannot be realized by the computer. Therefore, it is necessary to design data models within the scope of computer capabilities, and collect, collect, and process data sources. ``

3. The most ideal data source

The most ideal data source is that the background color and hair color have obvious color gradations, and there are few intersections. These all require the clarity of the camera, background color (environmental interference) and other factors to be processed. The problem of hair crossing can be solved, and these can be analyzed and processed through the effective data range. ``

The data model is the key to the entire software design. The analysis methods used include image grayscale, corrosion, filtering and other methods. These methods are effective methods to eliminate environmental color interference. As for hair diameter and root number statistics, these involve aggregation statistics , geometry, statistics, probability statistics (credibility) and other analysis.

So far, the results of the diameter of the hair of the rex rabbit and the number of roots of the hair of the rex rabbit have been obtained, but these values are still unreliable, because such data results have not been verified and post-processed. Because the algorithm is dead, the signal (collected image) is ever-changing, and the results obtained also have true and false values. ``

The so-called true value is within the range of valid data. For example, the diameter of a rex rabbit’s hair cannot be as small as 1um, nor can it be as large as 100 or even 200um, so the data still needs to be verified. An ideal processing module needs to be designed to exclude false values and leave true values. ``

The first step is to improve the algorithm for obtaining hair diameter by threshold line segmentation. The data to be processed is a string array, which is simply a string of numbers, 640 integers (ranging from 0 to 255). Through signal processing mathematical method, an algorithm for calculating the diameter is designed. (MAX+MIN)/2 is used as the value of the threshold line, and sometimes it may be wrong. If the deviation of MAX or MIN is particularly large, this will cause the threshold line to look too large or too small, resulting in a larger or smaller diameter calculation result. Therefore, the designed model is as follows:

1. Calculate the maximum value MAX and the minimum value MIN. ``

2. Remove the values corresponding to MAX and MIN, return to step 1, and remove the maximum and minimum of the remaining data. After 5 cycles, the difference between the MAX removed in the previous round and the MAX removed in the next round is less than Within 5 is fine. 

3. The remaining data string will be the last calculated array. Calculate the average value of the data string and use this average value as the threshold line. ``

4. Add noise removal algorithm. ``

The so-called denoising algorithm: For example, the data appears as shown in Figure 16.

It can be seen that there is a small noise at the first position of the threshold line in the figure. If no judgment is made, a small diameter value will appear in the noise part, which will directly cause the resulting data to be unusable. Image data is extremely rich and heavily influenced by the environment, so it must be excluded. ``

The exclusion algorithm is as follows, threshold line V, design threshold line V1, threshold line V2, V1=V-T, V2=V+T. The size of T is based on human needs. If the range of noise to be excluded is larger, T should be larger, and if the range of noise to be excluded is smaller, T should be smaller. Then calculate as follows when judging: The data value of the rising edge is one point before R1 and one point after R2. If R1<=V1 and R2>=V2, such a result is considered to have crossed the threshold line, otherwise set R2 as the next judgment R1 = V1. Similarly, the data value of the falling edge is R1 at the previous point and R2 at the next point. If R1>=V2 and R2<=V1, the result is considered to have crossed the threshold line. Otherwise, set R2 to R1=V2 for the next judgment. A full rise and a full fall are the index difference of a range point of one diameter. This difference is the number of pixels occupied by the diameter, and the number can be converted into a diameter. ``

Get the hair diameter data string of a picture, there should be at least 30 groups, list the array lengths of these 30 groups of data separately, exclude the largest number of 5 groups, the smallest number of 5 groups, and the remaining 20 groups as data calculate. ``

The 20 sets of data are 20 sets of diameter values, and the number of arrays in each set is the number of diameters calculated by each set of data, that is, the number of hairs. The smaller number of them must be the part where the hair sticks to the image, which will lead to the reduction of the hair root. Design the following algorithm to exclude:

1. Check each group of data. After the hair diameter is proportionally converted [(value × actual width of the screen)/640] if it is greater than 40, divide it by 2 to generate 2 new values. If it is less than 10, directly exclude it and put it in The backup array, the so-called backup array is the false value table, and these false values have to be put into the statistical result array. ``

2. Calculate the root value of each group of data, and classify the arrays with the same number of roots. For example, 5 groups of 20 roots, 6 groups of 21 roots, 7 groups of 22 roots... 

3. Use the number of the array with the most same number of roots as the basic number of hairs M, and then remove the smallest or largest hair value for the arrays that are within 2 greater than M and smaller than 2, and put the number of hairs into the basic number of hairs After M with the same number of hair roots, they are also classified into the array with the same number of hairs at most. For example: 5 groups of 20 roots, 6 groups of 21 roots, 7 groups of 22 roots, and 2 groups of 25 roots. Then the 2 groups with 25 roots are removed. Each group of data of 21 roots removes a minimum value and belongs to the group of 20 roots, and each group of data of 22 roots removes a minimum value and also belongs to the group of 20 roots, so the number of groups of 20 roots is 18 groups. Then these 18 groups of data are sequentially stacked from 1 to 20, and then the number of groups is removed to obtain the actual average diameter of each hair. G[18][20]; In fact, the final result is such a two-dimensional array. for(I=0,I<20,I++) { for(j=0;j<18;j++) { AVG[I]=AVG+G[j][I]; } AVG[I]=AVG[I]/ 18; }

The following 18-element array AVG[18] can be obtained, then the number of hairs is 18, and the array value corresponds to the hair diameter. ``

This array is the final truth value data, fill in the list and enter the later analysis.

The present invention has been described through the above-mentioned embodiments, but it should be understood that the above-mentioned embodiments are only for the purpose of illustration and description, and are not intended to limit the present invention to the scope of the described embodiments. In addition, those skilled in the art can understand that the present invention is not limited to the above-mentioned embodiments, and more variations and modifications can be made according to the teachings of the present invention, and these variations and modifications all fall within the scope of the claimed invention. within the range. The protection scope of the present invention is defined by the appended claims and their equivalent scope.

Claims (9)

1. an otter rabbit be is characterized in that comprising the steps: by wool fibre fineness, Density Detection method

A, collection are by a hair view data;

B, will in computing machine, be stored by the hair image data transmission;

C, finish by computer-processing software and detect to analyze.

2. method according to claim 1 is characterized in that adopting detector directly to gather by the hair view data from live body otter rabbit on one's body.

3. method according to claim 1 is characterized in that adopting in step c the portable data treatment facility to finish the Data Detection analysis.

4. detector according to claim 2 is DinoLite hand-held USB digit microscope.

5. the otter rabbit be is characterized in that comprising by hair image data acquiring device (1), by hair image data storage and detection analytical equipment (2) by wool fibre fineness, Density Detection system.

6. system according to claim 5 is characterized in that described image data acquiring device is (1) DinoLite hand-held USB digit microscope.

7. system according to claim 5 is characterized in that described is the portable data treatment facility by hair image data storage and detection analytical equipment (2).

8. system according to claim 6 is characterized in that described microscope is mainly by CCD(3), convex lens (4) and lighting unit (5) constitute.

9. system according to claim 7 is characterized in that described portable data treatment facility arranges promising input/output interface (6) and charge port (7).

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