CN103489192B - Method for detecting number of Arabidopsis leaves and distance between cusp and center of mass of each leaf - Google Patents

Abstract

A method for detecting the number of Arabidopsis leaves and the distance between the cusp and the center of mass of each leaf particularly comprises the steps that a demarcating board is placed in a planting pot of Arabidopsis and RGB images of the Arabidopsis are collected through a camera; the collected images are pre-processed, so that the images are automatically corrected and demarcated, wherein the images are corrected so that distortion of the images can be corrected and the images are demarcated so that the real dimension of a unit pixel can be obtained; the pre-processed images are divided, the Arabidopsis is separated from backgrounds and extracted from the images; after Arabidopsis images are obtained through division, the number of the Arabidopsis leaves is extracted, and the distance between the cusp and the center of mass of each leaf is calculated. A phenotype parameter referring to the distance between the cusp and the center of mass of each leaf is used for reflecting the size of each leaf, and the growth situation can be described in a quantitative mode according to the number of the leaves of the Arabidopsis and the size of each leaf. Differences of the growth situations of the leaves of the Arabidopsis with different genes are described through the phenotype parameters so that the functions of the different genes and influence on the Arabidopsis by the genes can be inferred.

Description

A kind of Arabidopsis leaf number and blade cusp are to the detection method of the distance of barycenter

Technical field

The present invention relates to a kind of Arabidopsis leaf number and blade cusp to the detection method of the distance of barycenter, utilize camera Gathering the picture of arabidopsis, application image processing method extracts the blade number of arabidopsis and blade cusp to barycenter from image Distance, it is achieved the Non-Destructive Testing of arabidopsis growth course.

Background technology

Arabidopsis is a kind of important model plant in botany, genetics, hereditism.Arabidopsis phenotype is ground Study carefully, can illustrate the physiological function of arabidopsis comprehensively, up hill and dale, the particularly mutual relation between its phenotype and its gene, with And the impact that it is grown by different environmental conditions.The detection method of plant phenotypic characteristics includes that destructive measurement, contact are surveyed Amount and Computer Vision Detection method.Destructive measurement, i.e. for a collection of plant, extracts some randomly, with destructive Method measures its parameter.Contact is measured and is i.e. used touch sensor to measure the parameter of plant.Use computer vision technique Measuring i.e. by relevant device, including CCD camera, light source etc., it is thus achieved that the spectrum picture of measurand, utilization is correlated with Image is processed by software, algorithm, it is thus achieved that required data, thus obtains the phenotypic parameter of plant.Existing research work Realize individual blade is analyzed or the analysis of other crop mainly by computer vision technique.Li Xinguo etc. utilize and sweep Retouch instrument and obtain the image of rape leaf, and utilize Photoshop software to obtain blade pixel count, obtain blade by resolution Area (Li Xinguo, Cai Shengzhong, Li Shaopeng etc. Applied Digital image technique measures avocado leaf area [J]. tropical agricultural science, 2009,29 (2): 10-13.).Han Dianyuan etc. utilize color to carry out the calculation split for the blade proposition under white background is a kind of Method, so utilize reference rectangular slab in background calculate blade area (Han Dianyuan, gold zone deep pool, Fu Hui etc. based on color channel The plant leaf area of similarity image dividing method calculates [J]. Transactions of the Chinese Society of Agricultural Engineering, and 2012,28 (6): 179-183.).Lee is few Elder brothers etc. utilize image technique that Semen Maydis and Semen Tritici aestivi carry out image acquisition, and extract relevant parameter (Li Shaokun, Zhang Xian. crop plant type The research [J] of information multi-media image processing techniques. Acta Agronomica Sinica, 1998,24 (3): 265-271).Li Changying etc. utilize calculating Machine vision technique carries out non-destructive monitoring to hothouse plants growth, obtains the formalness feature of plant, including top projected leaf area With plant height (Li Changying, Teng Guanghui, Zhao Chunjiang etc. utilize computer vision technique realize to hothouse plants growth non-destructive monitoring [J]. Transactions of the Chinese Society of Agricultural Engineering, 2003,19 (3): 140-143.).

In sum, there is following defect in existing research:

1, use destructive measuring method that plant can be caused damage, and plant can not be grown into Line Continuity Measure.

2, use sensor measurement, directly contact with plant, the growth of plant can be produced certain impact, and Its cost is high, and development difficulty is the most relatively large.

3, existing computer vision technique is concentrated mainly on the Phenotypic examination of individual blade or other crop, and to plan The Phenotypic examination of south mustard relies primarily on artificial realization, and workload is big, inefficient.

At present around the Research on Computer Vision Detection of arabidopsis phenotype, document is at home and abroad rarely had to report.

Summary of the invention

The technical problem to be solved in the present invention is: how for the feature of arabidopsis, utilize computer vision technique to plan Mustard carries out Non-Destructive Testing in south, extracts the blade number in its growth course and the blade cusp distance parameter to barycenter.Each leaf Sheet cusp is used for reflecting the size of each blade to the distance of barycenter.The blade number of plant and the size of each blade both may be used To describe the growing state of arabidopsis quantitatively, it is also possible to for the research of arabidopsis gene function, i.e. joined by these phenotypes Number describe heterogeneic arabidopsis differences on leaf growth situation, such that it is able to infer heterogeneic function and Impact on arabidopsis thaliana.

(1) technical scheme

To achieve these goals, the invention provides Arabidopsis leaf number based on computer vision and blade cusp To the detection method of centroid distance, comprise the following steps:

S1., in the planting pot of arabidopsis, place scaling board, utilize the RGB image of collected by camera arabidopsis；

S2. to gather after image carry out pretreatment, it is achieved image from dynamic(al) correction and demarcation, wherein image rectification be for The distortion of correction chart picture, image calibration is the full-size(d) in order to obtain unit picture element；

S3. pretreated image is split, by arabidopsis and background segment, extract from image；

S4. extract the blade number of arabidopsis, and calculate each blade cusp distance to barycenter.

The scaling board gathered in image uses rim black and white gridiron pattern, by square group that 3 × 3 length of sides are 4mm Become.Place it in Arabidopsis plant side, therewith carry out image acquisition.

Step S2 specifically includes following steps:

S2.1 positions black and white gridiron pattern

S2.1.1, according to blue RGB feature, extracts blue border；

S2.1.2 carries out holes filling to the inside of image, then deducts original blue border image, obtains new images；

S2.1.3 carries out opening operation to new images, removes noise；Carry out closed operation again, connect breakpoint, obtain black and white chessboard The region of lattice；

S2.2 Corner Detection

S.2.2.1 the single order horizontally and vertically gone up calculating each point in black and white gridiron pattern region is led Number, obtains three width new images: horizontal first derivative square, the product of two first derivatives of quadratic sum of vertical first derivative；

S.2.2.2 with gaussian filtering, three width images are filtered, remove noise；

S.2.2.3 correlation matrix, calculation criterion function are formed by above-mentioned three width images, it is judged that whether pixel therein is Angle point；

S2.3 image rectification and image calibration

S.2.3.1 tessellated each foursquare summit coordinate in the picture is obtained by Corner Detection, and according to it Spatial relation in real world, obtains the transformation matrix of the two；

S.2.3.2 ask for the inverse of transformation matrix, act on image, it is achieved image rectification；

S2.3.3 obtains the tessellated total number of pixels of black and white by angular coordinate, and according to its full-size(d), obtains The full-size(d) of unit picture element；

Step S3 specifically includes following steps:

S3.1 image foremost segment

S3.1.1 is normalized acquisition rgb to the rgb value of each pixel, extracts the chromaticity difference diagram of 3g-2.4r-b, makees with 0 For threshold value, image is carried out binaryzation；

Result is deducted the blue border region originally detected by S3.1.2, it is judged that the ash of the G of obtained foreground area pixel Whether angle value more than 50, the most then retains, otherwise removes；

S3.1.3 extracts the region with most pixel in prospect connected region, is plant regional；

S3.2 removes noise

S3.2.1 carries out opening operation to gained image, obtains new image, and it will only retain big leaf area and plant Central area, and remove the detail section in image, including the noise around the stem of plant and blade；

S3.2.2 calculates its number of pixels to each connected region of detail section removed, straight less than 12 of number of pixels Connect removal；

The S3.2.3 number of pixels connected region more than or equal to 12, the new images individually obtained with opening operation superposes, Calculate the areal in image after superposition again, if areal reduces, illustrate that this connected region is stem, it is necessary to retain, no Then, if the areal after superposition in image increases or constant, then illustrate that this connected region is the noise around blade, must Must remove；

After S3.2.4 has judged all of detail section, all connected regions retained all obtained with opening operation is new Image overlay, obtains final plant regional；

Step S4 specifically includes following steps:

The hole of plant regional is filled with by S4.1, the barycenter of zoning；

S4.2 passes through profile lookup algorithm, extracts the outline point of the plant regional after filling；

S4.3 calculates each outline point distance to plant regional barycenter, thus 2 dimension (2D) images are become 1 dimension (1D) Signal；

S4.4 carries out data point mirror image filling to the 1D signal described in S4.3 so that it is expand to the integral number power times of 2, To new 1D signal；

The 1D signal that S4.4 is obtained by S4.5 carries out 4 layers of Haar wavelet decomposition, extracts the 4th layer and believes with the 1D described in S4.3 Number wavelet coefficient that length is identical；

S4.6 searches the positive zero crossing in wavelet coefficient, the i.e. previous point of certain point, and less than 0, later point is more than 0, What these positive zero crossings represented is the local maximum of the 1D signal described in S4.3；For individual blade, its blade point The barycenter of some distance plant regional is farthest, i.e. the local maximum of 1D signal is the cusp of each blade, therefore believes by searching 1D Number local maximum be achieved with the blade number of plant；

S4.7 calculates the cusp of each blade in image and, to the length of barycenter, the number of pixels of length is walked with image calibration The actual length of the unit picture element suddenly obtained is multiplied, and obtains the cusp actual distance to barycenter of each blade.

(2) beneficial outcomes

The inventive method utilizes computer vision technique to gather the image of arabidopsis, utilizes image processing techniques to achieve plan The Non-Destructive Testing of south mustard phenotype.By the detection tessellated blue border of rim black and white and internal with Corner Detection Algorithm detection Angle point, finally achieve image from dynamic(al) correction and demarcation.Normalized rgb value linear combination is used to carry out threshold arabidopsis Value segmentation, and by again judging the details of plant regional, remove unnecessary noise, it is achieved thereby that do not affecting arabidopsis In the case of normal growth, arabidopsis is split from complicated natural growing environment.After being partitioned into plant, then extract The cusp of Arabidopsis leaf number and each blade is to the distance of barycenter.Observing relative to Traditional Man and measure, the method exists It is enhanced in efficiency.

Accompanying drawing explanation

By the detailed description non-limiting example made with reference to the following drawings of reading, the further feature of the present invention, Purpose and advantage will become more apparent upon:

Fig. 1 is the Arabidopsis leaf number according to one embodiment of the invention and the blade cusp distance detection side to barycenter The flow chart of method；

The collection transposition for gathering image used in the method that Fig. 2 provides for the present invention；

The image generated in the preprocess method processing procedure that Fig. 3 provides for the present invention, wherein, subgraph (1) be according to The RGB image of the arabidopsis that inventive method gathers, (2) are the black and white gridiron pattern region generated after pretreatment, and (3) are for passing through The Corner Detection image generated after pretreatment, (4) are the image after the correction generated after pretreatment；

The image generated in the image partition method processing procedure that Fig. 4 provides for the present invention, wherein, subgraph (1) is for passing through The bianry image generated after image foremost segment step, (2) are the figure of the final arabidopsis generated after noise removal step Picture；

The blade number of the arabidopsis that Fig. 5 provides for the present invention and blade cusp processed to the distance extracting method of barycenter The image generated in journey, wherein, subgraph (1) is the result after the holes filling in plant regional, and (2) are to search through profile to calculate The outline of the plant regional that method extracts, (3) are each outline point distance signal to barycenter, and (4) are distance signal Length is extended to the result that the integral number power of 2 obtains, and (5) are the 4th layer that this distance signal obtains after Haar wavelet decomposition Wavelet coefficient, and positive zero crossing, (6) are the local maximum of the former distance signal detected, (7) are the blade point detected Point, and each cusp is to the distance of barycenter.

Detailed description of the invention

A kind of Arabidopsis leaf number that the present invention proposes and blade cusp are to the distance detection method of barycenter, in conjunction with accompanying drawing Describe in detail as follows with embodiment.Following example will assist in those skilled in the art and are further appreciated by the present invention, but not Limit the present invention in any form.It should be pointed out that, to those skilled in the art, without departing from structure of the present invention On the premise of think of, it is also possible to make some deformation and change.These broadly fall into protection scope of the present invention.

For the phenotypic parameter of rapid extraction arabidopsis, the present invention a kind of Arabidopsis leaf number and blade cusp are to barycenter Distance detection method.After the method carries out pretreatment to the image gathered, from complicated background environment, it is partitioned into arabidopsis, On this basis the distance parameter of the blade number of arabidopsis and blade cusp to barycenter is extracted, improve arabidopsis Phenotypic parameter obtains efficiency.

As it is shown in figure 1, according to the distance of the Arabidopsis leaf number of one embodiment of the invention and blade cusp to barycenter Detection method includes step:

S1, in the planting pot of arabidopsis, places scaling board, utilizes CCD camera to gather the RGB image of arabidopsis；

In this example, the harvester that can use Fig. 2 carries out the collection of image.This harvester includes: CCD camera 1, support 2, illuminator 3 and arabidopsis 4, scaling board 5 is placed on the side of arabidopsis.Scaling board uses rim black and white chessboard Lattice, are that the square of 4mm forms by 3 × 3 length of sides.

S2 carries out pretreatment to the image after gathering, and specifically includes following sub-step:

S2.1 positions black and white gridiron pattern

S2.1.1 is according to blue RGB feature, and the gray value of R and G is both less than B, and the gray value of B is more than 150, from image In be partitioned into blue border.

S2.1.2 carries out holes filling to the inside of image, and its result deducts original blue border image, is newly schemed Picture.

S2.1.3 carries out opening operation to new images, removes noise；Carry out closed operation again, connect breakpoint, obtain black and white chessboard The region of lattice.

S2.2 Corner Detection

S.2.2.1 the single order horizontally and vertically gone up calculating each point in black and white gridiron pattern region is led Number.Use following Prewitt template to calculate, be i.e. approximately horizontal direction by the 3rd row in 3 × 3 regions and the difference of first row Derivative, be approximately the derivative of vertical direction by the difference of the third line and the first row.By the two subtemplate and image convolution, obtain Two matrixes identical with image size, are designated as I_x, I_y, and then calculate three width new images: I_x ², I_y ²And I_xI_y。

S.2.2.2 can be disturbed by noise in view of image, use the Gauss window of 101 × 101 that three width images are entered Row filtering, removes noise.

S.2.2.3 correlation matrix M is formed by three width new images:

$M=\left[\begin{array}{cc}{I}_x^2& I_x{I}_y\\ I_x{I}_y& I_y^2\end{array}\right]$

Utilize this matrix calculus criterion function R:

R=det (M)-k (trace(M))²

Wherein k typically takes 0.04.

For each pixel, a R value can be obtained, if the R value of certain point is more than 0.01R_max, and it is 3 × 3 The local maximum of neighborhood, then it will be judged as angle point.

S2.3 image rectification and image calibration

S.2.3.1 tessellated each foursquare summit coordinate in the picture is obtained by Corner Detection, in true generation In boundary, these summits form each square, according to its spatial relation, can arrange the coordinate on these summits.Order is wherein Certain point coordinate in the picture is (x ', y ')^T, coordinate in space be (x, y)^T, its homogeneous coordinates be respectively (x '₁, x ’₂, x '₃)^T(x₁, y, 1)^T, there is therebetween Projective Distortion conversion, transformation matrix is the linear change about homogeneous three-dimensional coordinate Change H, be expressed as:

$\left[\begin{array}{c}{x}_1^{\′}\\ x_2^{\′}\\ x_3^{\′}\end{array}\right]=\left[\begin{array}{ccc}{h}_{11}& h_{12}& h_{13}\\ h_{21}& h_{22}& h_{23}\\ h_{31}& h_{32}& h_{33}\end{array}\right]\left[\begin{array}{c}x\\ y\\ 1\end{array}\right]$

Because the inhomogeneous coordinate of image can be expressed as by homogeneous coordinates:

$x^{\′}=\frac{x_1^{\′}}{x_3^{\′}}=\frac{h_{11}x+h_{12}y+h_{13}}{h_{31}x+h_{32}y+h_{33}}$

$y^{\′}=\frac{x_2^{\′}}{x_3^{\′}}=\frac{h_{21}x+h_{22}y+h_{23}}{h_{31}x+h_{32}y+h_{33}}$

So each group of match point can get two equation below:

(h₃₁x+h₃₂y+h₃₃) x '=h₁₁x+h₁₂y+h₁₃

(h₃₁x+h₃₂y+h₃₃) y '=h₂₁x+h₂₂y+h₂₃

Transformation matrix H can be obtained with at least four pairs of match points.

S.2.3.2 ask for the inverse of transformation matrix H, remake for image, it is achieved the correction of image.

S2.3.3 calculates total number of pixels N in black and white gridiron pattern region by angular coordinate_b, and according to its true chi Very little, i.e. 9 × (4mm)²=144mm², obtain the full-size(d) of unit picture element, it may be assumed that

$A_{\mathrm{dz}}=\frac{144}{N_b}$

Pretreated image is split by S3, is extracted by arabidopsis from image, specifically includes following sub-step Rapid:

S3.1 image foremost segment

S3.1.1 is normalized acquisition rgb to the rgb value of each pixel, and method is as follows:

$\left\{\begin{array}{c}r=\frac{R}{R+G+B}\\ g=\frac{G}{R+G+B}\\ b=\frac{B}{R+G+B}\end{array}\right.$

Extract the chromaticity difference diagram of 3g-2.4r-b, as threshold value, image is carried out binaryzation with 0, it may be assumed that if

I=3g-2.4r-b >=0

Then this pixel is judged as the pixel of prospect plant, is otherwise background pixel.

Result is deducted the blue border region originally detected by S3.1.2.Judge the G's of the pixel of obtained foreground area Whether gray value more than 50, the most then retains this pixel, otherwise this pixel is judged as background pixel, removes from region.

S3.1.3 extracts the region with most pixel in prospect connected region, is plant regional.

S3.2 removes noise

S3.2.1 carries out opening operation to gained image by the disk template of 7 × 7, obtains new image, and it will only retain big Leaf area and the central area of plant, and remove the detail section in image, including the noise around the stem of plant and blade.

S3.2.2 calculates its number of pixels to each connected region of detail section removed, straight less than 12 of number of pixels Connect removal.

The S3.2.3 number of pixels connected region more than or equal to 12, the new images individually obtained with opening operation superposes, Calculate the areal in image after superposition again, if areal reduces, illustrate that this connected region is stem, it is necessary to retain, no Then, if the areal after superposition in image increases or constant, then illustrate that this connected region is the noise around blade, must Must remove.

After S3.2.4 has judged all of detail section, all connected regions retained all obtained with opening operation is new Image overlay, obtains final plant regional.

S4 extracts the blade number of arabidopsis, and calculates each blade cusp distance to barycenter, specifically includes following son Step:

S4.1 holes filling

S4.1.1 gives a point in hole as starting point, and is set to 1, and other point is all set to 0, forms one Individual new region X₀。

S4.1.2 with following symmetrical structure unit to X₀Expand, and with original image region cover row occur simultaneously fortune Calculate, obtain a new region X₁。

S4.1.3 judges X₁Whether with X₀Equal, if equal, holes filling terminates, and otherwise makes X₀=X₁, and repeat second step.

S4.2 searches profile

The point in the upper left corner of the plant regional after S4.2.1 Selective filling hole is as starting point b₀, make c₀For b₀West side Background dot.

S4.2.2 is from c₀Start to detect b in a clockwise direction₀8 consecutive points, make b₁For detecting first is not 0 Point, and make c₀For b in detection sequence₁Point before, stores b₀, b₁。

S4.2.3 judges b₁Whether with b₀Equal, if equal, profile is searched and is terminated, and otherwise makes b₀=b₁, and repeat second step.

S4.3 extracts 1 dimensional signal

After S4.3.1 searches profile, it is thus achieved that profile sequence, the x coordinate and the y that extract each profile point in this profile sequence sit Mark.

S4.3.2 calculates each outline point distance to plant regional barycenter.Make certain outline point seat in the picture It is designated as (x_i, y_i), barycenter coordinate in the picture is (x_o, y_o), distance therebetween is:

$D_i=\sqrt{{(x_i-x_o)}^2+{(y_i-y_o)}^2}$

By calculating distance D of each point_i, obtain a 1D signal S, thus 2D image become 1D signal.

S4.4 extends signal

S4.4.1 makes a length of N of the 1D signal described in S4.3₀, calculate N₀Logarithm value with 2 as the end, the result obtained Round numbers also adds 1, obtains numerical value k.

If S4.4.2 2^kLess than 2N₀, then the 1D signal described in S4.3 is carried out mirror image switch, adds to described in S4.3 1D signal after, take front 2^kIndividual data point, obtains new signal.

If S4.4.3 2^kMore than 2N₀, then, after the 1D signal described in S4.3 is supplemented the signal of mirror image switch, it is supplemented with 1D signal described in S4.3, takes front 2 the most again^kIndividual data point, obtains new signal.

S4.5 wavelet transformation

The 1D signal that S4.4 is obtained by S4.5.1 carries out 4 layers of Haar wavelet decomposition, extracts the 4th layer of wavelet coefficient.

The S4.5.2 wavelet coefficient to obtaining extracts front N₀Individual data point, obtains new 1D signal S_CSo that it is described in S4.3 1D signal length identical.

S4.6 searches positive zero crossing

S4.6.1 is to 1D signal S_CMove to right one respectively and move to left one, obtaining S_C1And S_C2。

S4.6.2 searches and meets the point of following condition:

(S_C=0I S_C2>0)∪(S_C1<0I S_C<0I S_C2>0)

The point meeting above-mentioned condition is S_CThe positive zero crossing of signal, the i.e. previous point of certain point be later less than 0 Point is more than 0, and what these positive zero crossings represented is the local maximum of the 1D signal S described in S4.3.For individual blade, The barycenter of its blade cusp distance plant regional is farthest, i.e. the local maximum of 1D signal is the cusp of each blade.Therefore pass through The local maximum searching 1D signal is achieved with the blade number of plant.

S4.7 length of blade

S4.7.1 calculates the cusp of each blade in image and, to the length of barycenter, makes certain blade cusp seat in the picture It is designated as (x_i, y_i), barycenter coordinate in the picture is (x_o, y_o), distance therebetween is:

$d_i=\sqrt{{(x_i-x_o)}^2+{(y_i-y_o)}^2}$

The evolution phase of the true area of the unit picture element that the number of pixels of length is obtained by S4.7.2 with image calibration step Take advantage of, obtain the cusp actual distance to barycenter of each blade, it may be assumed that

$d_{\mathrm{zi}}=d_i\sqrt{A_{\mathrm{dz}}}$

The method further illustrating the present invention below in conjunction with examples of implementation, the method comprises the following steps:

S1, in the planting pot of arabidopsis, places scaling board, utilizes the RGB image of collected by camera arabidopsis.Scaling board is adopted Be rim black and white gridiron pattern, be made up of the square that 3 × 3 length of sides are 4mm.Place it in arabidopsis side, with one Rise and carry out image acquisition, shown in the picture of collection such as Fig. 3 (1).

S2 to gather after image carry out pretreatment, it is achieved image from dynamic(al) correction and demarcation.

S2.1 positions black and white gridiron pattern

S2.1.1 is according to blue RGB feature, and the gray value of R and G is both less than B, and the gray value of B is more than 150, from image In be partitioned into blue border.

S2.1.2 carries out holes filling to the inside of image, and its result deducts original blue border image, is newly schemed Picture.

S2.1.3 carries out opening operation to new images, removes noise；Carry out closed operation again, connect breakpoint, obtain black and white chessboard The region of lattice, as shown in Fig. 3 (2).

S2.2 Corner Detection

S.2.2.1 by Prewitt template and image convolution, the level side that each in black and white gridiron pattern region is put is calculated To with the first derivative in vertical direction, obtain three width new images: horizontal first derivative square, vertical first derivative square Product with two first derivatives.

Three width images are filtered by the Gauss window S.2.2.2 using 101 × 101, remove noise.

S.2.2.3 formed correlation matrix, calculation criterion function by above-mentioned three width images, find out angle point.As shown in Fig. 3 (3), Each angle point marks with red point.

S2.3 image rectification and image calibration

S.2.3.1 tessellated 16 foursquare summits are obtained according to from left to right by Corner Detection, from top to bottom Order coordinate in the picture as follows:

(163,370) (224,362) (286,354) (347,347)

(171,430) (233,423) (294,415) (356,407)

(179,491) (241,484) (302,476) (346,468)

(187,553) (249,545) (311,537) (372,529)

It is strict square in real world according to it, by each coordinate convention put is:

(164,347) (225,347) (286,347) (347,347)

(164,408) (225,408) (286,408) (347,408)

(164,469) (225,469) (286,469) (347,469)

(164,530) (225,530) (286,530) (347,530)

Calculating these two groups of transformation of coordinates matrixes is:

$\left[\begin{array}{ccc}0.9820& 0.1202& -1.2519e-05\\ -0.1232& 1.0126& 3.8417e-05\\ 51.0888& -43.5533& 0.9977\end{array}\right]$

S.2.3.2 the inverse of this transformation matrix is:

$\left[\begin{array}{ccc}1.0026& -0.1183& 0.0000\\ 0.1237& 0.9713& 0.0000\\ -45.9428& 48.4665& 1.0000\end{array}\right]$

Acted on image, it is achieved image rectification, recovered the metric characteristic of image, shown in result such as Fig. 3 (4).

It is 35721 that S2.3.3 obtains the tessellated total number of pixels of black and white by angular coordinate, and according to its true chi Very little, i.e. 9 × (4mm)²=144mm², obtain the full-size(d) of unit picture element, it may be assumed that

$A_{\mathrm{dz}}=\frac{144}{35721}$

Pretreated image is split by S3, is extracted by arabidopsis from image, specifically includes following sub-step Rapid:

S3.1 image foremost segment

S3.1.1 is normalized acquisition rgb to the rgb value of each pixel, extracts the chromaticity difference diagram of 3g-2.4r-b, makees with 0 For threshold value, image is carried out binaryzation.

Result is deducted the blue border region originally detected by S3.1.2.Judge the G's of the pixel of obtained foreground area Whether gray value more than 50, the most then retains this pixel, otherwise this pixel is judged as background pixel, removes from region.

S3.1.3 extracts the region with most pixel in prospect connected region, is plant regional, result such as Fig. 4 (1) shown in.

S3.2 removes noise

S3.2.1 carries out opening operation to gained image by the disk template of 7 × 7, obtains new image, and it will only retain big Leaf area and the central area of plant, and remove the detail section in image, including the noise around the stem of plant and blade.

S3.2.2 calculates its number of pixels to each connected region of detail section removed, straight less than 12 of number of pixels Connect removal.

The S3.2.3 number of pixels connected region more than or equal to 12, the new images individually obtained with opening operation superposes, Calculate the areal in image after superposition again, if areal reduces, illustrate that this connected region is stem, it is necessary to retain, no Then, if the areal after superposition in image increases or constant, then illustrate that this connected region is the noise around blade, must Must remove.

After S3.2.4 has judged all of detail section, all connected regions retained all obtained with opening operation is new Image overlay, obtains final plant regional, shown in result such as Fig. 4 (2).

S4. extract the blade number of arabidopsis, and calculate each blade cusp distance to barycenter, specifically include following son Step:

The hole of plant regional is filled with by S4.1, shown in its result such as Fig. 5 (1).

S4.2 passes through profile lookup algorithm, extracts shown in the outline point such as Fig. 5 (2) of the plant regional after filling.

S4.3 extracts 1 dimensional signal

After S4.3.1 searches profile, it is thus achieved that profile sequence, the x coordinate and the y that extract each profile point in this profile sequence sit Mark.

S4.3.2 calculates each outline point distance to plant regional barycenter, as shown in Fig. 5 (3).

S4.4 extends signal

A length of 1163 of 1D signal described in S4.4.1S4.3, calculate 1163 logarithm value with 2 as the end, the knot obtained Really round numbers add 1, obtains numerical value 11.

S4.4.2 because 2048 less than 2326, then carries out mirror image switch to the 1D signal described in S4.3, adds to S4.3 Described in 1D signal after, take front 2048 data points, obtain new signal, as shown in Fig. 5 (4).

S4.5 wavelet transformation

The 1D signal that S4.4 is obtained by S4.5.1 carries out 4 layers of Haar wavelet decomposition, extracts the 4th layer of wavelet coefficient.

S4.5.2 to obtain wavelet coefficient extract front 1163 data points, obtain new 1D signal so that it is with in S4.3 Described 1D signal length is identical.

S4.6 searches positive zero crossing

S4.6.1 is to 1D signal S_CMove to right one respectively and move to left one, obtaining S_C1And S_C2。

S4.6.2 searches the positive zero crossing of this signal, shown in result such as Fig. 5 (5).That these positive zero crossings represent is this 1D The local maximum of signal, as shown in Fig. 5 (6).For individual blade, the barycenter of its blade cusp distance plant regional is Far, i.e. the local maximum of 1D signal is the cusp of each blade.Therefore by just searching the number of the local maximum of 1D signal The blade number of plant can be obtained.

S4.7 length of blade

The coordinate of S4.7.1 image Leaf cusp is:

(497,360) (485,232) (590,256) (607,324) (673,397) (600,480) (507,506) (412,409)

The coordinate of barycenter is: (542,373)

In calculating image, the cusp of each blade is to the distance of barycenter:

d_i=[47.02 152.18 126.43 81.28 133.00 121.60 137.54 135.05]

The evolution phase of the true area of the unit picture element that the number of pixels of length is obtained by S4.7.2 with image calibration step Take advantage of, obtain the cusp actual distance to barycenter of each blade, it may be assumed that

$d_{\mathrm{zi}}=d_i\&CenterDot;\sqrt{\frac{144}{35721}}=\left[\begin{array}{cccccccc}3.0& 9.7& 8.0& 5.2& 8.4& 7.7& 8.7& 8.6\end{array}\right]$

Unit is millimeter.Shown in result such as Fig. 5 (7).

Claims (2)

1. Arabidopsis leaf number and blade cusp are to a detection method for the distance of barycenter, comprise the following steps:

S1., in the planting pot of arabidopsis, place scaling board, utilize the RGB image of collected by camera arabidopsis；

S2. the image after gathering is carried out pretreatment, it is achieved image from dynamic(al) correction and demarcation, wherein image calibration is precisely in order to school The distortion of positive image, image calibration is the full-size(d) in order to obtain unit picture element；

Step S2 includes positioning black and white gridiron pattern, Corner Detection, image rectification and image calibration；

Described location black and white gridiron pattern comprises the following steps: according to the blue component in RGB feature, extracts blue border；To figure The inside of picture carries out holes filling, then deducts original blue border image, obtains new images；New images is carried out opening operation, Remove noise；Carry out closed operation again, connect breakpoint, obtain the tessellated region of black and white；

Described Corner Detection comprises the following steps: calculate horizontal direction and Vertical Square that each in black and white gridiron pattern region is put First derivative upwards, obtains three width new images: horizontal first derivative square, two single orders of the quadratic sum of vertical first derivative The product of derivative；With gaussian filtering, three width images are filtered, remove noise；Correlation matrix is formed by above-mentioned three width images, Calculation criterion function, it is judged that whether pixel therein is angle point；

Described image rectification and image calibration comprise the following steps: obtain tessellated each foursquare top by Corner Detection Point coordinate in the picture, and according to its spatial relation in real world, obtain the transformation matrix of the two；Ask for conversion Inverse of a matrix, acts on image, it is achieved image rectification；The tessellated total number of pixels of black and white, and root is obtained by angular coordinate According to its full-size(d), obtain the full-size(d) of unit picture element；

S3. pretreated image is split, by arabidopsis and background segment, extract from image；

Step S3 includes image foremost segment and removes noise；

Described image foremost segment comprises the following steps: the rgb value of each pixel is normalized acquisition rgb, extracts 3g- The chromaticity difference diagram of 2.4r-b, carries out binaryzation as threshold value to image with 0；Result is deducted the blue border district originally detected Territory, it is judged that whether the gray value of the G of obtained foreground area pixel more than 50, the most then retains, otherwise remove；Extraction prospect is even The region with most pixel in logical region, is plant regional；

Described removal noise comprises the following steps: gained image is carried out opening operation, obtains new image, and it will only retain great Ye Panel region and the central area of plant, and remove the detail section in image, including the noise around the stem of plant and blade；Right Each connected region of the detail section removed calculates its number of pixels, number of pixels directly removing less than 12, number of pixels Connected region more than or equal to 12, the new images individually obtained with opening operation superposes, then the district calculated after superposition in image Territory number, if areal reduces, illustrates that this connected region is stem, it is necessary to retain, otherwise, if the district after superposition in image Territory number increases or constant, then illustrate that this connected region is the noise around blade, it is necessary to remove；Judge all of details After part, the new images that all connected regions retained all obtain with opening operation is superposed, obtains final plant regional；

S4. after being partitioned into arabidopsis image, extract arabidopsis blade number, and calculate each blade cusp to barycenter away from From；

Step S4 specifically includes following steps:

The hole of plant regional is filled with by S4.1, the barycenter of zoning；

S4.2 passes through profile lookup algorithm, extracts the outline point of the plant regional after filling；

S4.3 calculates each outline point distance to plant regional barycenter, thus 2 dimension (2D) images are become 1 1D signal；

S4.4 carries out data point mirror image filling to the 1D signal described in S4.3 so that it is expand to the integral number power times of 2, obtains new 1D signal；

The 1D signal that S4.4 is obtained by S4.5 carries out 4 layers of Haar wavelet decomposition, extract the 4th layer with the 1D Chief Signal Boatswain described in S4.3 Spend identical wavelet coefficient；

S4.6 searches the positive zero crossing in wavelet coefficient, the i.e. previous point of certain point, and less than 0, later point is more than 0, these What positive zero crossing represented is the local maximum of the 1D signal described in S4.3；For individual blade, its blade cusp away from Barycenter from plant regional is farthest, i.e. the local maximum of 1D signal is the cusp of each blade；Therefore by searching 1D signal Local maximum is achieved with the blade number of plant；

S4.7 calculates the cusp of each blade in image and, to the length of barycenter, the number of pixels of length is obtained with image calibration step To the actual length of unit picture element be multiplied, obtain the cusp actual distance to barycenter of each blade.

Arabidopsis leaf number the most according to claim 1 and blade cusp are to the detection method of the distance of barycenter, wherein Described scaling board uses rim black and white gridiron pattern, 3 × 3 length of sides be that the square of 4mm forms.