CN105427307B - A kind of image partition method of cube Granule Computing - Google Patents
A kind of image partition method of cube Granule Computing Download PDFInfo
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Abstract
The present invention relates to the image partition methods of cube Granule Computing, it is slow image segmentation speed can effectively to be solved, the problem of effect difference, its solve technical solution be, the pixel rgb value of the colored image to be split of extraction first, further according to the rgb value of coloured image to be split, construct atom cube grain collection, build the combined operators and cube grain template between cube grain, the rgb value of each pixel of image to be split is expressed as cube grain, and it is matched with cube grain template, and determine the rgb value of each pixel of image after segmentation, finally preserve Segmentation of Color Images, to realize the image segmentation of cube Granule Computing.The method of the present invention splitting speed is fast, and effect is good, and segmentation result is made more to meet the vision of people, is the innovation on color image segmentation method, has very strong actual application value.
Description
Technical field
The present invention relates to image procossing, especially a kind of image partition method of cube Granule Computing.
Background technology
Image segmentation be a key problem in Digital Image Processing and computer vision field and image understanding with
The important research direction of analysis, its purpose are that the more interested region of people in image is come with other region disconnectings,
These regions are mutually mutually disjoint, and each region meets the consistency of specific region.Researcher for image segmentation into
A large amount of research is gone, it is proposed that many methods, Threshold sementation, differential operator edge detection, region growth technique and poly-
Many methods such as class cutting techniques, but due to the problem of technically existing, splitting speed is slow, and effect is poor, cannot meet at image
Actual needs in reason, therefore, it is imperative to improve and innovate.
Invention content
For the above situation, to overcome the defect of the prior art, the purpose of the present invention to be just to provide a kind of cube grain meter
The image partition method of calculation, can effectively solve that image segmentation speed is slow, the problem of effect difference.
The technical solution that the present invention solves is, first the pixel rgb value of the colored image to be split of extraction, further according to waiting for point
The rgb value of coloured image is cut, atom cube grain collection is constructed, builds combined operators and cube grain mould between cube grain
The rgb value of each pixel of image to be split is expressed as cube grain, and it is matched with cube grain template by plate,
And determine the rgb value of each pixel of image after segmentation, Segmentation of Color Images is finally preserved, to realize cube Granule Computing
Image segmentation.
The method of the present invention splitting speed is fast, and effect is good, and segmentation result is made more to meet the vision of people, is color images side
Innovation in method has very strong actual application value.
Description of the drawings
Fig. 1 is the flow chart element diagram of the present invention.
Fig. 2 is the amalgamation result figure between two cube grains of the invention.
Fig. 3 is the image after the segmentation of the present invention.
Specific implementation mode
It elaborates to the specific implementation mode of the present invention below in conjunction with attached drawing and concrete condition.
As shown in Figure 1, the present invention includes the following steps in the specific implementation：
(1), the pixel rgb value of the colored image to be split of extraction：
The pixel rgb value of the colored image to be split of extraction, R is red value, and G is green value, and B is blue valve, and (i, j) is
The coordinate of pixel, color are made of R, G, B, and the value range of R, G, B are [0,255]；
(2), according to the rgb value of coloured image to be split, atom cube grain collection is constructed：
The rgb value of each pixel of coloured image to be split is expressed as cube grain, cube grain has g=(C, r)
Form, wherein C=(R, G, B) be cube grain center, r be cube grain the length of side, indicate cube grain size, claim
For the granularity of cube grain；As r=0, cube grain is minimum and cannot divide again, referred to as atom cube grain, to a height of N1,
Width is the image of N2, and pixel number is N=N1 × N2, constructs the grain collection GS being made of N number of cube grain, the coordinate of pixel
(i, j) corresponding grain collection is N1+ 1 cube grain of × (j-1)；
(3), the combined operators between structure cube grain and cube grain template, method are：
Two cube grain g1=(C1, r1), wherein C1=(R1,G1,B1) be g1 center, r1 be g1 granularity, g2=
(C2, r2), wherein C2=(R2,G2,B2) be g2 center, r2 be g2 granularity, merge cube grain center and granularity, by
The center of g1 and g2 calculates the center (Ru, Gu, Bu) of cube grain according to formula 1, formula 2, formula 3：
Ru=0.5 (max { R1+0.5r1, R2+0.5r2 }+min { R1-0.5r1, R2-0.5r2 }) formula 1
Gu=0.5 (max { G1+0.5r1, G2+0.5r2 }+min { G1-0.5r1, G2-0.5r2 }) formula 2
Bu=0.5 (max { B1+0.5r1, B2+0.5r2 }+min { B1-0.5r1, B2-0.5r2 }) formula 3
Center by g1 and g2 and granularity merge the center of cube grain according to formula 4, and ru is the grain for merging cube grain
Degree：
Gu=g1∨g2=(Cu, ru) formulas 4；
Wherein Cu=(Ru, Gu, Bu) is the center for merging cube grain；
According to the following formula：
Ru=max { max { R1+0.5r1, R2+0.5r2 }-min { R1-0.5r1, R2-0.5r2 }, max { G1+0.5r1, G2+
0.5r2 }-min { G1-0.5r1, G2-0.5r2 }, max { B1+0.5r1, B2+0.5r2 }-min { B1-0.5r1, B2-0.5r2 } } formula 5
It obtains merging cube grain；
Granularity thresholds ρ is set, cube grain template is built, which is a set, is indicated with GB, cube grain template
In cube grain with gb=(Cb, rb) indicate, wherein Cb=(Rb, Gb, Bb) be template cube grain center, rb is template
The granularity of cube grain；First cube grain of cube grain collection GS is added in cube grain template, and in cube grain collection
GS leaves out first cube grain, the same all cube grains and cube grain calculated with formula 1 to formula 5 in cube grain collection GS
Merging cube grain gu in template GB between all cube grains；When the granularity of cube grain gu is less than or equal to granularity thresholds
When ρ, gb=gu, and leave out in cube grain collection GS and participate in combined cube grain, when the granularity of cube grain gu is more than
When granularity thresholds ρ, cube grain template GB is added in first cube grain for choosing cube grain collection GS, and in cube grain
Leave out first cube grain in collection GS, until cube grain all in cube grain collection GS is all left out, such cube
Element in body grain template GB is continuously increased, and cube grain integrates the element in GS and constantly reduces until as empty set, that is, constructs
Containing n cube grain cube grain template GB=gb1, gb2 ..., gbn)；
(4), the rgb value of each pixel of image to be split is expressed as cube grain collection GS, and by itself and cube grain
Template is matched, and matching formula is：
D (i, j)=max | Rj-Rbi |, | Gj-Gbi |, | Bj-Bbi | } formula 6
The center of i-th of cube grain wherein in (Rbi, Gbi, Bbi) cube grain template, (Rj, Gj, Bj) are cube
The center of grain collection j-th of cube grain of GS；
It is calculated between j-th of cube grain of cube grain collection GS and cube grain template i-th of cube grain of GB according to formula 6
Distance；
(5), the rgb value after color image pixel point segmentation to be split is determined：
According to the corresponding cube grain gj of the rgb value of image slices vegetarian refreshments to be split and cube grain template GB cube grains
The distance between gbi D (i, j) find out the number id=of the cube grain in the cube grain template corresponding to minimum distance
ArgminD (i, j), wherein 1<i<N, after the center of i-th d cube grain is pixel segmentation in cube grain template
Rgb value；
(6), Segmentation of Color Images is preserved, it is literary accordingly that the coloured image after segmentation with JPG formats is saved in computer
In part folder, to realize the image segmentation of cube Granule Computing.
The present invention in specific implementation, can also be provided by following embodiment.
The rgb value of the first step, extraction color image pixel point to be split, 3 × 4 coloured image, pixel (2,1)
Rgb value is (10,13,34)；
Second step, according to the rgb value of coloured image to be split, construct cube grain collection, rgb value be expressed as atom cube
Body grain, cube grain integrate as GS={ g1,g2,g3,g4,g5,g6,g7,g8,g9,g10,g11,g12, wherein
g1=(0,0,0,0), g2=(10,13,34,0), g3=(35,20,15,0), g4=(21,38,12,0),
g5=(151,151,155,0) g6=(101,155,98,0) g7=(155,100,95,0) g8=(102,99,
155,0)
g9=(255,198,197,0) g10=(195,199,255,0) g11=(199,255,200,0) g12=
(255,255,255,0)
Third step merges cube grain, constructs cube grain template, two cube grain g1=(0,0,0,0) and g2=(10,
13,34,0) it, by formula 1, formula 2, formula 3, formula 4 and formula 5, calculates and merges cube grain for gu=g1∨g2=(5,6.5,17,34),
Amalgamation result is shown in Fig. 2；
Granularity thresholds ρ is set, cube grain template is constructed, cube is added in first cube grain of cube grain collection GS
In grain template GB, leave out first cube grain of grain collection GS, second cube grain becomes first cube grain, by first
A cube grain merges respectively at the cube grain in cube grain template GB, when the granularity for merging cube grain is less than or equal to
When ρ, merge the cube grain that cube grain replaces cube grain template GB.This process is repeated, until GS is free of any cube
Body grain illustrates the generating process of cube grain template with 3 × 4 coloured images if granularity thresholds are ρ=200：
The cube grain that RGB is formed integrates as GS={ g1,g2,g3,g4,g5,g6,g7,g8,g9,g10,g11,g12, first by g1
It is added in cube grain template GB, g is left out from GS1, cube grain template GB has 1 cube grain GB={ gb1, and gb1=
g1, for first cube grain g of GS2, according to formula 2 and formula 3, g2With gb1Cube grain after merging is gu=g2∨gb1=
(5,6.5,17,34), since its granularity is 34 to be less than ρ, gb1=gu, i.e. gb1=(5,6.5,17,34), at this time cube grain mould
Plate has 1 cube grain, i.e. GB={ gb1}；
Gu=g3∨gb1=(11.5,9.425,16.5667,47), since its granularity is less than ρ, gb1=gu for 47,
Gu=g4∨gb1=(12.3436,11.9625,16.1611,52.075), since its granularity is less than ρ, gb1=gu,
Gu=g5∨gb1=(68.6887,68.4625,72.5804,165.075), since its granularity is less than ρ, gb1=
Gu,
Gu=g6 ∨ gb1=(69.4355,70.4625,73.1679,169.075), since its granularity is less than ρ, gb1=
Gu,
Gu=g7∨gb1=(69.9490,70.6398,73.2989,170.102), since its granularity is less than ρ, gb1=
Gu,
Gu=g8∨gb1=(69.9490,70.6398,73.2989,170.102), since its granularity is less than ρ, gb1=
Gu,
Gu=g9∨gb1=(119.9490,105.0520,106.7224,270.102), since its granularity is more than ρ, gb2
=g9,
Gu=g10∨gb1=(123.2963,109.2421,113.3357,283.3286), since its granularity is more than ρ, but
It is gu=g10∨gb2=(225,198.5,226,60), since its granularity is less than ρ, gb2=gu,
Gu=g11∨gb1=(123.8758,112.5005,111.3559,285), since its granularity is more than ρ, but gu
=g11∨gb2=(218.9027,211.75,219.9027,86.5), since its granularity is less than ρ, gb2=gu,
Gu=g12∨gb1=(126.6575,112.5005,114.0880 285), since its granularity is more than ρ for 255, but
It is gu=g12∨gb2=(218.9027,211.75,219.9027,86.5), since its granularity is less than ρ, gb2=gu,
Obtained cube grain template is GB={ gb1, gb2}；
The rgb value of 4th step, extraction color image pixel point to be split, 3 × 4 coloured image to be split its rgb value are successively
For
RGB (1,1)=(0,0,0) RGB (2,1)=(10,13,34) RGB (3,1)=(35,20,15)
RGB (1,2)=(21,38,12) RGB (2,2)=(151,151,155) RGB (3,2)=(101,155,98)
RGB (1,3)=(155,100,95) RGB (2,3)=(102,99,155) RGB (3,3)=(255,198,197)
RGB (1,4)=(195,199,255) RGB (2,4)=(199,255,200) RGB (3,4)=(255,255,
255)
Its corresponding grain integrates as GS, and the matching distance between cube grain template GB is
D (1,1)=73.2989, D (1,2)=59.9490, D (1,3)=58.2989, D (1,4)=61.2989, D (1,
5)=81.7011, D (1,6)=84.3602, D (1,7)=85.0510, D (1,8)=81.7011, D (1,9)=185.0510,
D (1,10)=181.7011, D (1,11)=184.3602, D (1,12)=185.0510
D (2,1)=219.9027 D (2,2)=208.9027 D (2,3)=204.9027 D (2,4)=207.9027 D
(2,5)=67.9027D (2,6)=121.9027 D (2,7)=124.9027 D (2,8)=116.9027 D (2,9)=
36.0973 D (2,10)=35.0973D (2,11)=43.2500 D (2,12)=43.2500
5th step determines the number away from cube grain in the nearest cube grain template GB of each cube grain in GS, away from
The number of the cube grain of the nearest cube grain template GB of 12 cube grains is 1,1,1,1,2,1,1,1,2,2,2,2, segmentation
The rgb value of pixel afterwards is：
RGB (1,1)=(70,71,73) RGB (2,1)=(70,71,73) RGB (3,1)=(70,71,73)
RGB (1,2)=(70,71,73) RGB (2,2)=(219,222,220) RGB (3,2)=(70,71,73)
RGB (1,3)=(70,71,73) RGB (2,3)=(70,71,73) RGB (3,3)=(219,222,220)
RGB (1,4)=(219,222,220) RGB (2,4)=(219,222,220) RGB (3,4)=(219,222,
220)；
6th step preserves the image after segmentation, and it is corresponding that the coloured image after segmentation with JPG formats is saved in computer
In file.
And extraordinary technique effect is achieved through experiment, concrete condition is as follows：
Experiment one：For comparing the speed of the invention with the image segmentation of the image segmentation and Kmeans clusters of FCM clusters,
Experiment two is for comparing GCE, VI and RI of the invention divided with FCM and Kmeans.
In order to verify the present invention have faster speed, choose BSD300 in file entitled 3096 154401 (321 ×
481) pixel image is split.Granularity thresholds ρ drops to 100 with step-length 2 from 200, when table 1 lists the segmentation of operation 51 times
Between.The quantity of cube grain is as the cluster numbers of FCM and Kmeans methods in the cube grain template obtained using the present invention.This
Invention average sliced time be the average sliced time of 3.1595, Kmeans the average sliced time of 0.0272, FCM methods
It is 0.3515, splitting speed of the present invention is 116.1581 times of sides Kmeans (3.1595/0.0272=116.1581) of FCM methods
11.5846 times (0.3151/0.0272=11.5846) of method.
Experiment two, according to the module that GCE, VI be the smaller the better and RI is the bigger the better, choosing BSDS300 sizes is
The image of 154401 (321 × 481) pixel files entitled 3096, the verification present invention is relative to the superior of FCM and Kmeans methods
Property.Granularity thresholds ρ drops to 100 with step-length 2 from 200, and table 2 lists segmentation GCE, VI and RI of operation 51 times.When ρ=152,
GCE and VI reaches minimum, and respectively 0.0094 and 0.1154, RI, which reaches, is up to 0.9909, such as Fig. 3 of the image after segmentation.Fig. 3
(a) image cut for people's work point, Fig. 3 (b) are the corresponding segmentation images of minimum GCE, and Fig. 3 (c) is the corresponding segmentation figures of minimum VI
Picture, Fig. 3 (d) are the corresponding segmentation images of maximum RI.FCM methods, minimum GCE are 0.02, and minimum VI is 0.9641, and maximum RI is
0.6124.Kmeans methods, minimum GCE are 0.02, and minimum VI is 0.9004, and maximum RI is 0.6515.
1 present invention of table and the sliced time of Kmeans and FCM compare
2 present invention of table and GCE, VI and RI that FCM and Kmeans is divided compare
From the above, it is seen that the present invention is a kind of set partitioning method of Granule Computing, i.e., set is divided into its subset
The set of composition, grain is expressed as a kind of form of specification by it, since coloured image is by red (R), green (G) and blue
(B) three kinds of colouring informations merged and the image rgb value that generates, grain can be expressed as to the cubic form of three dimensions,
That is cube grain.The rgb value of image to be split is expressed as cube grain, using the combined operators between cube grain, by grain
The smaller cube grain of degree merges into the larger cube grain of granularity, makes the RGB of each pixel in a unique cube
In grain.Granule Computing is the effective ways for realizing different granular space conversion, and this method recognizes the fineness of image with people
It is consistent.So implementing segmentation to image with Granule Computing reflects the objective law that people recognize image.With prior art phase
Than having the advantages that following prominent：
1, splitting speed is fast, since the method for the present invention can complete image by the pixel value of single pass known image
Segmentation, computation complexity be O (N), wherein N be image size；
2, segmentation effect is good, GCE (Global Consistency Error), the M.Meil ǎ proposed using D.Martin
Three kinds of method evaluation present invention of RI (Rand Index) that the VI (Variant Information) and W.M.Rand of proposition are proposed
Image segmentation performance, and choose the picture appraisal foundation in the world in common image segmentation data set BSDS300, the number
Include the image after original image and artificial segmentation according to collection, is the innovation on image partition method, there is very strong practicability.
Claims (2)
1. a kind of image partition method of cube Granule Computing, which is characterized in that the pixel of the colored image to be split of extraction first
Point rgb value constructs atom cube grain collection further according to the rgb value of coloured image to be split, builds the merging between cube grain
The rgb value of each pixel of image to be split is expressed as cube grain by operator and cube grain template, and by itself and cube
Grain template is matched, and determines the rgb value of each pixel of image after segmentation, finally preserves Segmentation of Color Images, to real
The image segmentation of existing cube Granule Computing, specifically includes following steps：
(1), the pixel rgb value of the colored image to be split of extraction：
The pixel rgb value of the colored image to be split of extraction, R are red value, and G is green value, and B is blue valve, and (i, j) is pixel
The coordinate of point, color are made of R, G, B, and the value range of R, G, B are [0,255]；
(2), according to the rgb value of coloured image to be split, atom cube grain collection is constructed：
The rgb value of each pixel of coloured image to be split is expressed as cube grain, cube grain has the shape of g=(C, r)
Formula, wherein C=(R, G, B) are the center of cube grain, and r is the length of side of cube grain, indicates the size of cube grain, referred to as vertical
The granularity of cube grain；As r=0, cube grain is minimum and cannot divide again, referred to as atom cube grain, is to a height of N1, width
The image of N2, pixel number are N=N1 × N2, construct the grain collection GS being made of N number of cube grain, the coordinate (i, j) of pixel
Corresponding cube grain is N1+ 1 cube grain of × (j-1)；
(3), the combined operators between structure cube grain and cube grain template, method are：
Two cube grain g1=(C1, r1), wherein C1=(R1,G1,B1) be g1 center, r1 be g1 granularity, g2=(C2,
R2), wherein C2=(R2,G2,B2) be g2 center, r2 is the granularity of g2, merges center and the granularity of cube grain, by g1 and
The center of g2 calculates the center (Ru, Gu, Bu) of cube grain according to formula 1, formula 2, formula 3：
Ru=0.5 (max { R1+0.5r1, R2+0.5r2 }+min { R1-0.5r1, R2-0.5r2 }) formula 1
Gu=0.5 (max { G1+0.5r1, G2+0.5r2 }+min { G1-0.5r1, G2-0.5r2 }) formula 2
Bu=0.5 (max { B1+0.5r1, B2+0.5r2 }+min { B1-0.5r1, B2-0.5r2 }) formula 3
Center by g1 and g2 and granularity merge the center of cube grain according to formula 4, and ru is the granularity for merging cube grain：
Gu=g1∨g2=(Cu, ru) formulas 4；
Wherein Cu=(Ru, Gu, Bu) is the center for merging cube grain；
According to the following formula：
Ru=max { max { R1+0.5r1, R2+0.5r2 }-min { R1-0.5r1, R2-0.5r2 }, max { G1+0.5r1, G2+0.5r2 }-
Min { G1-0.5r1, G2-0.5r2 }, max { B1+0.5r1, B2+0.5r2 }-min { B1-0.5r1, B2-0.5r2 } } formula 5
It obtains merging cube grain；
Granularity thresholds ρ is set, cube grain template is built, which is a set, is indicated with GB, in cube grain template
Cube grain indicates that wherein Cb=(Rb, Gb, Bb) is the center of template cube grain with gb=(Cb, rb), and rb is template cube
The granularity of body grain；First cube grain of cube grain collection GS is added in cube grain template, and is deleted in cube grain collection GS
First cube grain is removed, the same all cube grains and cube grain template calculated with formula 1 to formula 5 in cube grain collection GS
Merging cube grain gu in GB between all cube grains；When the granularity of cube grain gu is less than or equal to granularity thresholds ρ,
Gb=gu, and leave out in cube grain collection GS and participate in combined cube grain, when the granularity of cube grain gu is more than granularity
When threshold value ρ, cube grain template GB is added in first cube grain for choosing cube grain collection GS, and in cube grain collection GS
In leave out first cube grain, until cube grain all in cube grain collection GS is all left out, such cube grain
Element in template GB is continuously increased, and cube grain integrates the element in GS and constantly reduces until as empty set, that is, constructs containing n
A cube grain cube grain template GB=gb1, gb2 ..., gbn)；
(4), the rgb value of each pixel of image to be split is expressed as cube grain collection GS, and by itself and cube grain template
It is matched, matching formula is：
D (i, j)=max | Rj-Rbi |, | Gj-Gbi |, | Bj-Bbi | } formula 6
The center of i-th of cube grain wherein in (Rbi, Gbi, Bbi) cube grain template, (Rj, Gj, Bj) are cube grain collection
The center of j-th of cube grain of GS；
According to formula 6 calculate between j-th of cube grain of cube grain collection GS and cube grain template i-th of cube grain of GB away from
From；
(5), the rgb value after color image pixel point segmentation to be split is determined：
According to the corresponding cube grain gj of the rgb value of image slices vegetarian refreshments to be split and cube grain template GB cube grains gbi it
Between distance D (i, j), find out the number id=of the cube grain in the cube grain template corresponding to minimum distance
ArgminD (i, j), wherein 1<i<N, after the center of i-th d cube grain is pixel segmentation in cube grain template
Rgb value；
(6), Segmentation of Color Images is preserved, the coloured image after segmentation is saved in the corresponding file of computer with JPG formats
In, to realize the image segmentation of cube Granule Computing.
2. the image partition method of cube Granule Computing according to claim 1, which is characterized in that include the following steps：
The rgb value of the first step, extraction color image pixel point to be split, 3 × 4 coloured image, the RGB of pixel (2,1)
Value is (10,13,34)；
Second step, according to the rgb value of coloured image to be split, construct cube grain collection, rgb value be expressed as atom cube
Grain, cube grain integrate as GS={ g1,g2,g3,g4,g5,g6,g7,g8,g9,g10,g11,g12, wherein
g1=(0,0,0,0), g2=(10,13,34,0), g3=(35,20,15,0), g4=(21,38,12,0),
g5=(151,151,155,0) g6=(101,155,98,0) g7=(155,100,95,0) g8=(102,99,155,
0)
g9=(255,198,197,0) g10=(195,199,255,0) g11=(199,255,200,0) g12=(255,255,
255,0)
Third step merges cube grain, constructs cube grain template, two cube grain g1=(0,0,0,0) and g2=(10,13,
34,0) it, by formula 1, formula 2, formula 3, formula 4 and formula 5, calculates and merges cube grain for gu=g1∨g2=(5,6.5,17,34)；
Granularity thresholds ρ is set, cube grain template is constructed, cube grain mould is added in first cube grain of cube grain collection GS
In plate GB, leave out first cube grain of grain collection GS, second cube grain becomes first cube grain, vertical by first
Cube grain merges respectively at the cube grain in cube grain template GB, when the granularity for merging cube grain is less than or equal to ρ,
Merge the cube grain that cube grain replaces cube grain template GB, repeats this process, until GS is free of any cube grain,
If granularity thresholds are ρ=200, the generating process of cube grain template is illustrated with 3 × 4 coloured images：
The cube grain that RGB is formed integrates as GS={ g1,g2,g3,g4,g5,g6,g7,g8,g9,g10,g11,g12, first by g1It is added
In cube grain template GB, g is left out from GS1, cube grain template GB has 1 cube grain GB={ gb1, and gb1=g1,
For first cube grain g of GS2, according to formula 2 and formula 3, g2With gb1Cube grain after merging is gu=g2∨gb1=(5,
6.5,17,34), since its granularity is 34 to be less than ρ, gb1=gu, i.e. gb1=(5,6.5,17,34), at this time cube grain template
There are 1 cube grain, i.e. GB={ gb1}；
Gu=g3∨gb1=(11.5,9.425,16.5667,47), since its granularity is less than ρ, gb1=gu for 47,
Gu=g4∨gb1=(12.3436,11.9625,16.1611,52.075), since its granularity is less than ρ, gb1=gu,
Gu=g5∨gb1=(68.6887,68.4625,72.5804,165.075), since its granularity is less than ρ, gb1=gu,
Gu=g6 ∨ gb1=(69.4355,70.4625,73.1679,169.075), since its granularity is less than ρ, gb1=gu,
Gu=g7∨gb1=(69.9490,70.6398,73.2989,170.102), since its granularity is less than ρ, gb1=gu,
Gu=g8∨gb1=(69.9490,70.6398,73.2989,170.102), since its granularity is less than ρ, gb1=gu,
Gu=g9∨gb1=(119.9490,105.0520,106.7224,270.102), since its granularity is more than ρ, gb2=g9,
Gu=g10∨gb1=(123.2963,109.2421,113.3357,283.3286), since its granularity is more than ρ, but gu
=g10∨gb2=(225,198.5,226,60), since its granularity is less than ρ, gb2=gu,
Gu=g11∨gb1=(123.8758,112.5005,111.3559,285), since its granularity is more than ρ, but gu=g11
∨gb2=(218.9027,211.75,219.9027,86.5), since its granularity is less than ρ, gb2=gu,
Gu=g12∨gb1=(126.6575,112.5005,114.0880 285), since its granularity is 255 to be more than ρ, but gu
=g12∨gb2=(218.9027,211.75,219.9027,86.5), since its granularity is less than ρ, gb2=gu,
Obtained cube grain template is GB={ gb1, gb2}；
The rgb value of 4th step, extraction color image pixel point to be split, 3 × 4 coloured image to be split its rgb value are followed successively by：
RGB (1,1)=(0,0,0) RGB (2,1)=(10,13,34) RGB (3,1)=(35,20,15)
RGB (1,2)=(21,38,12) RGB (2,2)=(151,151,155) RGB (3,2)=(101,155,98)
RGB (1,3)=(155,100,95) RGB (2,3)=(102,99,155) RGB (3,3)=(255,198,197)
RGB (1,4)=(195,199,255) RGB (2,4)=(199,255,200) RGB (3,4)=(255,255,255)
Its corresponding grain integrates as GS, and the matching distance between cube grain template GB is：
D (1,1)=73.2989, D (1,2)=59.9490, D (1,3)=58.2989, D (1,4)=61.2989, D (1,
5)=81.7011, D (1,6)=84.3602, D (1,7)=85.0510, D (1,8)=81.7011, D (1,9)=
185.0510, D (1,10)=181.7011, D (1,11)=184.3602, D (1,12)=185.0510
D (2,1)=219.9027 D (2,2)=208.9027 D (2,3)=204.9027 D (2,4)=207.9027 D (2,
5)=67.9027 D (2,6)=121.9027 D (2,7)=124.9027 D (2,8)=116.9027 D (2,9)=
36.0973 D (2,10)=35.0973 D (2,11)=43.2500 D (2,12)=43.2500；
5th step determines the number away from cube grain in the nearest cube grain template GB of each cube grain in GS, vertical away from 12
The number of the cube grain of the nearest cube grain template GB of cube grain is 1,1,1,1,2,1,1,1,2,2,2,2, after segmentation
The rgb value of pixel is：
RGB (1,1)=(70,71,73) RGB (2,1)=(70,71,73) RGB (3,1)=(70,71,73)
RGB (1,2)=(70,71,73) RGB (2,2)=(219,222,220) RGB (3,2)=(70,71,73)
RGB (1,3)=(70,71,73) RGB (2,3)=(70,71,73) RGB (3,3)=(219,222,220)
RGB (1,4)=(219,222,220) RGB (2,4)=(219,222,220) RGB (3,4)=(219,222,220)；
6th step preserves the image after segmentation, and the coloured image after segmentation is saved in the corresponding file of computer with JPG formats
In folder, to realize the image segmentation of cube Granule Computing.
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