CN107945165A - Textile flaw detection method based on peak value coverage values and areal calculation - Google Patents

Textile flaw detection method based on peak value coverage values and areal calculation Download PDF

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CN107945165A
CN107945165A CN201711188164.1A CN201711188164A CN107945165A CN 107945165 A CN107945165 A CN 107945165A CN 201711188164 A CN201711188164 A CN 201711188164A CN 107945165 A CN107945165 A CN 107945165A
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map grid
mrow
peak value
value
calculate
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CN107945165B (en
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贾靓
庄丽华
颜榴红
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Changzhou University
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/194—Segmentation; Edge detection involving foreground-background segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30108—Industrial image inspection
    • G06T2207/30124—Fabrics; Textile; Paper

Abstract

The present invention provides a kind of textile flaw detection method based on peak value coverage values and areal calculation, this method analyzes the Pixel of Digital Image half-tone information based on flat textile surface under lighting source, divide the image into the grid for non-overlapping copies, the area of each grid is calculated, textile surface flaw is automatically positioned according to area distributions.The present invention is especially suitable for the textile surface flaw being automatically identified in the textile flat surfaces gray-scale image that is gathered under steady illumination light source.

Description

Textile flaw detection method based on peak value coverage values and areal calculation
Technical field
The present invention relates to textile Defect Detection technical field, and peak value coverage values and area meter are based on more particularly to one kind The textile flaw detection method of calculation.
Background technology
Traditional textile flaw manual identified accuracy rate only has 60-75% (referring to document：K.Srinivasan, P.H.Dastoor,P.Radhakrishnaiah,et al..FDAS:a knowledge-based framework for analysis of defects in woven textiles tructures,J.Text.Inst.83(1992)431– 448.), the method for machine automatic identification textile flaw has practical application request.The digital picture of flat textile surface is adopted Sample (hereinafter referred to as textile images) belongs to 2 d texture, and 2 d texture has been demonstrated can be according to 17 kinds of wallpaper group (wallpaper Group the pattern arrangement method) defined is generated (referring to document：H.Y.T.Ngan,G.K.H.Pang,N.H.C.Yung.Motif- based defect detection for patterned fabric,Pattern Recognit.(2008)1878– 1894.), for generate the pattern of 2 d texture be known as map grid (lattice) (referring to：https://en.wikipedia.org/ Wiki/Wallpaper_group), pattern is known as motif inside map grid.Most textile flaw automatic detection methods can only be located The textile images of p1 types in wallpaper group are managed (referring to document：H.Y.T.Ngan,G.K.H.Pang, N.H.C.Yung.Automated fabric defect detection—A review,Image and Vision Computing 29 (7) (2011) 442-458.), only a few methods can handle beyond p1 types textile images (referring to Document：H.Y.T.Ngan,G.K.H.Pang,N.H.C.Yung.Motif-based defect detection for Patterned fabric, Pattern Recognit. (2008) 1878-1894.), such as the benchmark based on wavelet pretreatment Image difference method (wavelet-pre-processed golden image subtraction, hereinafter referred to as WGIS) (ginseng See document：H.Y.T.Ngan,G.K.H.Pang,N.H.C.Yung,et al.,Wavelet based methods on Patterned fabric defect detection, Pattern Recognit.38 (4) (2005) 559-576.), symbiosis Matrix method is (referring to document：C.J.Kuo,T.Su,Gray relational analysis for recognizing Fabric defects, Text.Res.J.73 (5) (2003) 461-465.), cloth forest belt method (Bollinger bands, with Lower abbreviation BB) (referring to document：H.Y.T.Ngan,G.K.H.Pang,Novel method for patterned fabric Inspection using bollinger bands, Opt.Eng.45 (8) (2006) 087202-1-087202-15.), rule Band method (regular bands, hereinafter referred to as RB) is (referring to document：H.Y.T.Ngan,G.K.H.Pang,Regularity analysis for patterned texture inspection,IEEE Trans.Autom.Sci.Eng.6(1)(2009) 131-144.), Elo appraisal procedures (Elo rating method, hereinafter referred to as ER) are (referring to document：C.S.C.Tsang, H.Y.T.Ngan,G.K.H.Pang,Fabric inspection based on the Elo rating method, Pattern Recognit.51 (2016) 378-394.) etc..Although these methods can handle the textile images beyond p1, But their computational methods are built upon on the pattern (hereinafter referred to as map grid) of the similar lattice based on artificial selection more.Such as WGIS requires the size and texture of artificial selection map grid, and BB, RB and ER require the size of Manual definition's map grid.These prioris The degree of automation of machine recognition textile flaw is reduced to a certain extent.
The content of the invention
The technical problems to be solved by the invention are：In order to improve the degree of automation of machine recognition textile flaw, this Invention provides a kind of textile flaw detection method based on peak value coverage values and areal calculation, main a kind of automatic including design Split textile images for the method for map grid and a kind of flaw recognition methods based on map grid and map grid areal calculation of design.
To make statement cheer and bright, existing centralized definition partial symbols according to the present invention and concept.
Represent Positive Integer Set.
Expression includes zero integer set.
Expression includes zero arithmetic number set.
Expression includes zero real number set.
Represent that element number isReal vector.
Represent plural number set.
Represent that element number isComplex vector.
T representing matrixes or vectorial transposition.
Represent the real matrix of n × m sizes, wherein
Represent the real matrix of k × n × m sizes, wherein
IfAndThen Ai,:The i-th row of representing matrix A, A:,jThe jth row of representing matrix A.
IfAndThen Al,:,:Represent the l layer matrixes that size is n × m in A, Al,i,:Represent the i-th row of the l layer matrixes that size is n × m in A, Al,:,jRepresent the of the l layer matrixes that size is n × m in A J is arranged.
Represent ratioSmall maximum integer, such as
{aiRepresent by index i determine by element aiThe set or multiset of composition.
| S | represent the element number in set S, if S is vector, | S | represent element number contained by vector, | S | it is known as Vector length.
Avg (S) or mean (S)：The average of set of computations or multiset S, the element of S is real number.
std(S)：The standard deviation of set of computations or multiset S, the element of S is real number.
med(S)：The median of set of computations or multiset S, the element of S is real number.
mod(S)：The mode of multiset S is calculated, the element of S is real number.
Max (S) represents to find out set or the element maximum of multiset S, such as max (Ic) represent IcThe maximum of middle pixel Gray value.
max(sCondition) represent find out it is qualifiedMaximum.
Min (S) represents to find out set or the element minimum value of multiset S, such as min (Ic) represent IcThe minimum of middle pixel Gray value.
args∈S(C (s)) represents the value of s when condition C is true.
arg maxsF (s) is represented in the value range of the domain internal variable s of function f so that function f (s) takes maximum The s of value.
arg minsF (s) is represented in the value range of the domain internal variable s of function f so that function f (s) takes minimum The s of value.
arg maxsf1(s),f2(s) represent in function f1And f2Domain intersection internal variable s value range in so that Function f1(s) and f2(s) s being maximized.
argRepresent the domain internal variable s in function f (s)1And s2Value range in so that function f (s) s being maximized1And s2。
arg modi({ai) represent corresponding multiset { aiMode mod ({ ai) index.
dimx(I) total line number of two-dimensional image I, dim are representedy(I) total columns of I is represented.
Image origin：The position that pixel column column index starts in image, the hypothesis on location are in the image upper left corner and value (1,1)。
I (x, y) represents the pixel value in two-dimensional image I with ranks index (x, y).Line indexIt is former by image Point starts for step-length to be incremented by downwards with 1,1≤x≤dimx(I)；Column indexBy image origin with 1 for step-length to the right It is incremented by, 1≤y≤dimy(I)。
Image boundary：With line index dimx(I) row and column index dimy(I) row.
Textile images cartoon component Ic：To the textile images I of a width gray processing, using opposite total variance (relative total variation, hereinafter referred to as RTV) model (Xu L., Yan Q., Xia Y., Jia J., Structure Extraction from Texture via Relative Total Variation,ACM Transactions on Graphics 31 (6) 2012 Article 139) generation the edge clear based on I but texture obscure Gray level image Ic, IcReferred to as textile images cartoon component.
Binaryzation textile images Itc：Use Bradley methods (Bradley D., Roth G., Adaptive Thresholding Using the Integral Image,Journal of Graphics Tools 12(2)2007 13- 21) binaryzation IcAnd the I according to step 1.1 to binaryzationcCarry out noise reduction, the two-value obtained after two value object of suppressing exception area Image, wherein foreground pixel value are 1, background pixel value 0.
Two value object barycenter：ItcIn two value objects include foreground pixel image line index average value and column index it is flat Average.
Represent to be linked in sequence by operand and produce vector, such as scalar v1=1 and vector v2=[2 3]T, For scalar s1=8, s2=1, s3=5,For vector v1 =[2 3]T, v2=[5 0 4]T,
Represent by element vector multiplication, such as vector v1=[5 0.9 4]T, v2=[1 0 1]T, then
Wherein a,
Map grid indexes (ir,ic)：After image is divided into nonoverlapping map grid, according to the arrangement position of map grid in the picture, Each map grid has unique map grid line index irWith unique map grid column index ic, in image upper left corner map grid index for (1, 1) it is, (1,2) close to the right side map grid index of the map grid, is (2,1) close to the downside map grid index that index is (1,1) map grid, The rest may be inferred.Represent that map grid index is (ir,ic) map grid, wherein L1,1Referred to as first map grid.
Map grid pixel index：Map grid is made of pixel, therefore map grid is a sub-picture, image origin and pixel column column index Definition be also applied for map grid pixel index.
Map grid size：Number of lines of pixels contained by map grid and columns.
Map grid texture species：The species of map grid texture is produced based on map grid segmentation and textile gray level image.
Map grid matrix：Each element is a map grid in matrix in units of map grid, i.e. matrix.
Eigenmatrix：Using feature extracting method calculate map grid matrix in each element feature vector, with feature to Measure and form matrix for unit, i.e., each element is the feature vector of a map grid in matrix, and element index is right with it in matrix Index of the map grid answered in map grid matrix is identical.
Training sample set：N sub-pictures I1,I2…INResolution ratio it is identical, all images according to map grid segmentation produce map grid Texture species and its all identical training sample of quantity are image of having no time, and training sample set is only comprising image of having no time, and figure of having no time As being also only present in training sample concentration.
Test sample collection：Similar with training sample set, all image resolution ratios are identical, and the figure produced according to map grid segmentation Check manages species and its quantity is all identical, arrangement mode and the training sample set of each image map grid define described in it is consistent, Unlike training sample set, the image that test sample is concentrated contains position at random and texture is not belonging to map grid texture species Irregular area, the region are defined as flaw.The image that test sample is concentrated is known as test sample, and test sample is to have free time figure Picture, what test sample concentration included is all to have free time image.
Feature extracting method title ordered set T：Represent feature extracting method f1, f2…f|T|Name set, such as T= { HOG, LBP }, then | T |=2 and f1Represent HOG methods, f2Represent LBP methods.
On the basis of being as defined above, the technical solution adopted by the present invention to solve the technical problems is：One kind is based on peak It is worth the textile flaw detection method of coverage values and areal calculation, including two stages：Training stage and test phase.Training rank Section calculates map grid segmentation according to a series of training sample set being made of indefectible textile images (image of hereinafter referred to as having no time) And parameter needed for flaw identification；Test phase carries out map grid point according to the parameter that the training stage obtains to a secondary textile images Cut and judge whether map grid includes flaw, finally mark contains map grid defective.Training stage includes three steps：Step 1 figure Lattice partitioning parameters calculate, and step 2 training sample map grid segmentation, step 3 is had no time area interval computation.Test phase includes two steps Suddenly：Step 4 test sample map grid is split, the identification of step 5 flaw.Inventive method assumes that textile images have following features： Relative to the row and column of textile images, map grid is transversely arranged according to the direction of image line, and by the direction longitudinal arrangement of row；In Ic In, part map grid has geometry and there were significant differences in gray scale with background pixel.
Step 1 calculates map grid partitioning parameters.The step includes three sub-steps, i.e. step 1.1 background pixel projects, step 1.2 calculate peak value coverage values and step 1.3 calculating map grid ideal dimensions.
Step 1.1 calculates the cartoon component of textile gray level image I according to RTV models, using Bradley method binaryzations Cartoon component, by morphological erosion and expansive working to binaryzation cartoon component noise reduction, tracks using Moore-Neighbor Algorithm obtains binaryzation IcIn two value objects, calculate two-value object area in binaryzation cartoon component, delete area Bu areas Between ((1- α) ma,(1+α)·ma) in two value object (wherein maFor two-value object area median,And 0 ＜ α ＜ 1) binaryzation textile images I is obtainedtc.Calculate ItcMiddle each row and column background pixel number, often row background is arranged by line index ascending order Pixel number obtains the projection of background pixel rowThe projection of background pixel row is obtained by column index ascending order arrangement each column background pixel number
Step 1.2 calculates peak value coverage values.Calculate the projection of textile gray level image I background pixels rowPeak Value, by peak value by itsIn index ascending order arrange to obtain peak value sequenceFor prInA peak valueCalculate according to the following formulaCoverage values
Similarly, calculatePeak value sequenceCalculateWhereinMeter Calculate prThe ordered set of middle peak value coverage valuesDescending arranges middle element by size；ForInA element Meet in peak value sequence'sOrdered set is known asLevel peak valueTheLevel peak value in element by itsIn index ascending order arrangement；ForLevel peak Value, calculates each peak value and exists with its previous peak valueIn index difference absolute value, calculate the medians of these absolute valuesAnd its occurrence numberComposition setComposition setMiddle element takes Value composition setSimilarly, according toWithCalculate and meet'sOrdered set " theLevel Peak value "Calculate theFront and rear element exists in level peak valueIn index difference absolute value and its MedianWith median occurrence numberForm multisetForm multisetMiddle element value composition set
Step 1.3 calculates map grid ideal dimensions.To the I of training sample set1,I2…INIn i-thTraining sample Ii, I is calculated according to step 1.2i'spr,pc,WithCalculateValue setIiPreferable line numberIt is defined by the formula.
Wherein, δ is Dirac delta function (Dirac delta function).I.e. m isIn an element, IiPreferable columnsCalculate withIt is similar, i.e., the item with subscript r in above formula is replaced with into the correspondence with subscript c , such asReplace withMap grid ideal dimensions are defined asMedianWithMedian
I of the step 2 to training sample set1,I2…INCarry out map grid segmentation.For i-th of training sample Ii, the step bag Include three sub-steps：Step 2.1 background pixel projects, and step 2.2 calculates initial segmentation position and step 2.3 calculates final segmentation Position.
The calculating process of step 2.1 includes step 1.1 and step 1.2.
Step 2.2 calculates initial segmentation position.For i-th of training sample Ii, it is calculated according to step 2.1WithDefined in calculation procedure 1.2WithAndWithRoot It is calculated according to step 1.3WithIt is calculated as followsThere is most frequentLevel peak value
Similarly, can calculateThere is most frequentLevel peak value, i.e., replace with the item for having subscript r in above formula Respective items with subscript c, such asReplace withAssuming that theThere are a string of continuous peak values in level peak value And each peak value exists with previous peak valueIn the difference absolute value of index approachThen this string peak value existsIn index definition For row initial segmentation position Sr, this string peak value is theIndex in level peak value meets following formula definition.
Wherein dj+kRepresent theLevel peak value in index for j+k and j+k-1 two peak values itsThe difference of middle index Absolute value,And 0 ＜ β ＜ 1 be parameter.Row initial segmentation position ScIt is relevantWithDefinition withWithIt is similar, The item with subscript r in above formula is replaced with into the respective items with subscript c, such asReplace withAnd dj+kIs represented at this timeLevel peak value in index for j+k and j+k-1 two peak values itsThe difference of middle index Absolute value.Parameter beta pairWithCalculating it is general.
Step 2.3 calculates final split position, at once split positionWith column split positionFor i-th of training sample This Ii,WithInitial value be respectively step 2.2 calculate IiRow initial segmentation position SrWith row initial segmentation position Sc。 WillIn element by size ascending order arrange, find out least member thereinAnd greatest memberIt is calculated as follows four Predicted positionWith
I is obtained by step 1.1iBinaryzation textile imagesAnd updated by following three kinds of situationsWith
The first situation：IfCalculateMiddle line index x meetsTwo The average value of value object barycenterAndMiddle line index x meetsTwo value object barycenter it is flat AverageThenIt is added toNew element and becomeRecalculated according to definitionWith
The second situation：IfCalculateMiddle line index x meets's The average value of two value object barycenterThenIt is added toNew element and becomeRecalculated according to definition
The third situation：IfThen terminate and calculate.
Repeat the calculating of above-mentioned three kinds of situations untilWithUntill no longer changing.Similarly, press State three kinds of situation renewalsWith
The first situation：IfCalculateMiddle line index x meetsTwo value object barycenter average valueAndMiddle line index x meetsTwo value object barycenter average valueThenIt is added toIt is new Element simultaneously becomesRecalculated according to definitionWith
The second situation：IfCalculateMiddle line index x meetsTwo value object barycenter average valueThenIt is added toNew element and becomeRecalculated according to definition
The third situation：IfThen terminate and calculate.
Repeat the calculating of above-mentioned three kinds of situations untilWithUntill no longer changing, at this timeMeter Terminate.Calculating it is similarWillIn element by size ascending order arrange, find out least member thereinMost Big elementAccording toWithUpdate the three kinds of situations renewal being related toWithI.e. by three kinds of situations Middle every superscript r replaces with c, such asReplace withThe x in inequality and formula is replaced with into y at the same time, such asReplace withAccording toWithUpdate the three kinds of situations renewal being related toWithI.e. Superscript r every in three kinds of situations is replaced with into c, such asReplace withThe x in inequality and formula is replaced at the same time For y, such asReplace withAccording toWithThe row and column index included respectively, by IiBy where these indexes Row and column split, split gained rectangular area be map grid, it is defined as follows.
WhereinWith Represent the index of map grid arrangement position in I.
Step 3 is had no time area interval computation.If set training sample I binaryzation cartoon components ItcBackground pixel It is worth for 0, then map gridArea ai,jIt is defined asThe number of included foreground pixel, whereinAndI.e.：
For training sample set I1,I2…IN, map grid segmentation is carried out to each image and calculates map grid area, gained area Minimum value and maximum be denoted as a respectively0(i) and a1(i), then the lower bound a in area section of having no time0With upper bound a1Determined by following formula Justice.
WhereinWithA is represented respectively0(i) and a1(i) linear order collection (linearly ordered set), wherein i =1,2 ... N, i.e.,WithIn element according to value size ascending order arrangement,WithRespectivelyWithElement index,
Step 4 test sample map grid is split.To a secondary given test sample, the meter of repeat step 2.1 to step 2.3 Calculate, difference lies in the training sample involved in calculating is replaced with test sample, finally obtain the row split position of test sampleWith column split positionAnd according toWithTest sample is divided into map grid.
Step 5 flaw identifies.For a secondary test sample I, carry out map grid segmentation and calculate map grid area, calculate map grid face Product histogramT is made to representTransverse axis scale, i.e. map grid area value range, h (t) representLongitudinal axis scale, i.e., A in Ii,jFor t'sNumber, calculate notch value t ' and cliff of displacement value t " respectively according to the following formula.
If t ' exists, t ' is set as a0, otherwise see that t " whether there is, if setting t " in the presence of if as a0.For I, any map grid face Product is less than a0Or more than a1Map grid be labeled as flaw.
The beneficial effects of the invention are as follows：A kind of textile flaw based on peak value coverage values and areal calculation provided by the invention Defect detection method, Pixel of Digital Image half-tone information of this method analysis based on flat textile surface under lighting source, will scheme Grid as being divided into non-overlapping copies, calculates the area of each grid, and textile surface flaw is automatically positioned according to area distributions. The present invention is especially suitable for be automatically identified in the textile flat surfaces gray-scale image that is gathered under steady illumination light source Textile surface flaw.
Brief description of the drawings
The invention will be further described with reference to the accompanying drawings and examples.
Fig. 1 is the main-process stream schematic diagram of the present invention；
Fig. 2 is the background pixel projection flow diagram of the step 1.1 of the present invention；
Fig. 3 is the flow diagram for calculating row initial segmentation position of the step 1.2 of the present invention；
Fig. 4 is that the calculating of the step 1.4 of the present invention arranges the flow diagram of final split position；
Fig. 5 is the step 1.1 background pixel projection algorithm flow diagram of the present invention；
The step 1.2 that Fig. 6 is the present invention calculates peak value coverage values algorithm flow chart；
The step 1.3 that Fig. 7 is the present invention calculates map grid ideal dimensions algorithm flow chart；
The step 2.1 that Fig. 8 is the present invention calculates background pixel projection and coverage values algorithm flow chart；
The step 2.2 that Fig. 9 is the present invention calculates initial segmentation position algorithm flow chart；
The step 2.3 that Figure 10 is the present invention calculates final split position algorithm flow chart；
Figure 11 is the step 4 test sample map grid partitioning algorithm flow chart of the present invention；
Figure 12 is the step 5 flaw recognizer flow chart of the present invention.
Embodiment
Presently in connection with attached drawing, the present invention is described in detail.This figure is simplified schematic diagram, is only illustrated in a schematic way The basic structure of the present invention, therefore it only shows composition related to the present invention.
The embodiment of computational methods of the present invention is completed by writing computer program, and specific implementation process is related to self-defined Algorithm is described by pseudocode.Program input is the textile images of gray processing, and program output is the map grid set containing flaw.This hair Bright embodiment includes five steps, first three step is the training stage, and latter two steps are test phase.
The training stage comprises the following steps：
Step 1：A series of parameter according to needed for images of having no time calculate map grid segmentation, to determine map grid ideal dimensions；
Step 2：According to the map grid ideal dimensions obtained in step 1, map grid segmentation is carried out to training sample set, is trained Sample map grid；
Step 3：The map grid area of the training sample map grid of map grid segmentation generation in calculation procedure 2, so as to obtain trained sample This concentration is had no time the area section of having no time of image；
The test phase comprises the following steps：
Step 4：Test sample map grid is split, to a secondary given test sample, according to the method for step 2 to test sample Map grid segmentation is carried out, obtains test sample map grid；
Step 5：Method according to step 3 calculates the map grid area of test sample, and by result of calculation and has no time image Map grid area is compared, to identify map grid defective.
The order and logical relation of this method refer to Fig. 1.
Individually below to this five step expansion explanations.
1st, the training stage
A series of training stage parameter first according to needed for textile gray level images of having no time calculate map grid segmentation, then to nothing Free time image carries out map grid segmentation and calculates parameter needed for test phase.Training stage includes three steps：Step 1：Calculate map grid Partitioning parameters, step 2：Training sample map grid is split, and step 3 is had no time area interval computation.Map grid segmentation side proposed by the present invention The parameter that method is obtained according to step 1.3, splits map grid by step 2.1 to step 2.3.
Step 1 is used to calculate map grid partitioning parameters, which specifically includes three sub-steps, i.e. step 1.1：Background pixel Projection；Step 1.2：Calculate peak value coverage values；Step 1.3：Calculate map grid ideal dimensions.
Step 1.1, visible Fig. 2 of detailed process.For a width textile gray level image I, according to RTV models calculate cartoon into Divide Ic, I is obtained by Bradley methodscBianry image, Fig. 2 illustrates in binarization and is calculated by Bradley methods Pixel threshold schematic diagram, i.e. IcMesh figures gray plane, this method to each pixel calculate a local threshold, according to Pixel local threshold binaryzation I obtains the I of binaryzationc.The I of binaryzationcNoise reduction is realized by morphological erosion and expansive working, Then using Moore-Neighbor track algorithms, (Moore-Neighbor tracing algorithm, come from document Jia L.,Liang J.,Fabric defect inspection based on isotropic lattice segmentation, Journal of the Franklin Institute 354 (13) (2017) 5694-5738) obtain binaryzation I after noise reductioncIn Two value objects, i.e., 8 connection foreground pixel regions, calculate two value objects area, i.e. the foreground pixel number of two value objects. It is distributed according to two-value object area, obtains area median ma, by all areas not in section ((1- α) ma,(1+α)·ma) Two interior value objects are from binaryzation IcMiddle deletion obtains binaryzation textile images Itc,For the parameter being manually specified, take Value scope is 0≤α≤1, and this method takes α=0.6.Calculate ItcIn often capable background pixel number and arrange to obtain by line index ascending order Background pixel row projectsCalculate ItcThe background pixel number of middle each column simultaneously is arranged to obtain background pixel row throwing by column index ascending order ShadowOne-dimensional waveform in Fig. 2 isWithStep 1 algorithm flow refers to Fig. 5.
Step 1.2 part flow refers to Fig. 3, and for two-dimentional textile gray level image, it is initial that initial segmentation position includes row Split position and row initial segmentation position, Fig. 3 show only the conceptional flowchart for calculating row initial segmentation position, row initial segmentation The calculating process of position is similar therewith.According toCalculate peak value (i.e.From increasing to subtracting, or from the value for reducing to increasing, as Fig. 2 is one-dimensional The dark dot of waveform, and pressThe index ascending order of middle peak value arranges to obtain peak value sequenceFor pr InA peak valueCalculateCoverage valuesIt is defined as follows.
It is conceptive,Represent in prIn fromBoth sides start to prMove, be more than not running into end to endPeak Pass through the number of peak value before value, as shown in Figure 3, there is the peak value of identical coverage values with the triangular representation of same color. Similarly, calculatePeak value sequenceAnd calculateWherein
For prOr pc, coverage values often take limited a integer value, p as shown in Figure 3cValue is 0,1,2,4,11 and 27. Coverage values are arranged in descending order, obtain coverage values value setSuch as attached drawing 3According toA coverage values valuepcMiddle coverage valuesPeak valueReferred to asLevel peak value, theLevel peak value By itsIn index ascending order arrangement.Calculate theAdjacent peak exists in level peak valueIn index spacing(i.e. each peak Value exists with previous peak valueIn index difference absolute value), the median of computation index spacingAnd its occurrence numberForIn each element, all there are it is adjacent index spacing median and its occurrence number, these medians Value then forms setSimilarly, calculateWithStep 1.2 algorithm flow refers to figure 6。
Step 1.3 calculates map grid ideal dimensions.The training sample I concentrated according to training sample1,I2…IN, i-th can be calculated It is a Sample Ii'sWithThen IiPreferable line numberIt is defined as follows.
Wherein δ is Dirac delta function (Dirac delta function).The preferable columns of IDefinition withClass Seemingly, only need byItem with subscript r in definition replaces with the respective items with subscript c, such asReplace ForMap grid ideal dimensions are defined asMedianWithMedian Step 1.3 algorithm flow refers to Fig. 7.
The calculating process of step 2.1 includes step 1.1 and step 1.2.Step 2.1 algorithm flow refers to Fig. 8.
Step 2.2 calculates initial segmentation position, and flow refers to Fig. 3.For i-th of training sample Ii, according to step 2.1 are calculatedWithDefined in calculation procedure 1.2WithAndWithIt is calculated according to step 1.3WithIt is calculated as followsOccur most frequent TheLevel peak value
WhereinWithRepresent to be projected according to background pixel row respectivelyPeak value sequence counted The coverage values value set of calculation, theLevel peak value index spacing median, theLevel peak value index spacing median occurrence number and All rank peak values (The corresponding peak value of all elements) median value sequence.Calculating process withIt is similar, only Needing willItem with subscript r in definition replaces with the respective items with subscript c, such asReplace with
Assuming that theThere are a string of continuous peak values and each peak value in level peak value to exist with previous peak valueIn index difference Absolute value approachesThen this string peak value existsIn index be defined as row initial segmentation position Sr, this string peak value is theLevel peak Index in value meets following formula definition.
Wherein dj+kRepresent theLevel peak value in index for j+k and j+k-1 two peak values itsThe difference of middle index Absolute value,And 0 ＜ β ＜ 1 be parameter, this method takes β=0.1.Row initial segmentation position ScIt is relevantWithDetermine Justice withWithIt is similar, i.e., the item with subscript r in above formula is replaced with into the respective items with subscript c, such asReplace withAnd dj+kIs represented at this timeLevel peak value in index for j+k and j+k-1 two peak values itsThe absolute value of the difference of middle index.Parameter beta pairWithCalculating it is general.Step 2.2 algorithm flow refers to Fig. 9.
Step 2.3 flow refers to Fig. 4, which, which show only, calculates column split positionConceptional flowchart, row framing bits PutCalculating process it is similar therewith.Due to the interference of the factors such as flaw and noise, usual SrAnd ScIt cover only image section Region (S at oncerMinimum and maximum between image line index account for less than 80% or S of all image line indexcMinimum Image column index between maximum accounts for the 80% of all image column indexes, does not include 80%) either way, so needing Extend SrAnd Sc.For i-th of training sample Ii,And ScInitial value be respectively step 2.2 calculate IiSrAnd Sc.Will In element by size ascending order arrange, find out least member thereinAnd greatest memberWithDeviate for step size computation S1And S∞And close to the row predicted position of image boundary, that is, four predicted positions are calculated as followsWith
I is obtained by step 1.1iBinaryzation textile imagesAnd updated by following three kinds of situationsWith
The first situation：IfCalculateMiddle column index y meetsTwo The average value of value object barycenterAndMiddle column index y meetsTwo value object barycenter it is flat AverageThenIt is added toNew element and becomeRecalculated according to definitionWith
The second situation：IfCalculateMiddle column index y meetsTwo The average value of value object barycenterThenIt is added toNew element and becomeRecalculated according to definition
The third situation：IfThen terminate and calculate.
Repeat the calculating of above-mentioned three kinds of situations untilWithUntill no longer changing.Similarly, press State three kinds of situation renewalsWith
The first situation：IfCalculateMiddle column index y meetsTwo value object barycenter average valueAndMiddle column index y meetsTwo value object barycenter average valueThenIt is added toIt is new Element simultaneously becomesRecalculated according to definitionWith
The second situation：IfCalculateMiddle column index y meetsTwo value object barycenter average valueThenIt is added toNew element and becomeRecalculated according to definition
The third situation：IfThen terminate and calculate.
Repeat the calculating of above-mentioned three kinds of situations untilWithUntill no longer changing, at this timeMeter Terminate.Calculating it is similarWillIn element by size ascending order arrange, find out least member thereinMost Big elementAccording toWithUpdate the three kinds of situations renewal being related toWithI.e. by three kinds of situations Middle every superscript c replaces with r, such asReplace withThe y in inequality and formula is replaced with into x at the same time, such asReplace withAccording toWithUpdate the three kinds of situations renewal being related toWith Superscript c every in three kinds of situations is replaced with into r, such asReplace withAt the same time by the y in inequality and formula X is replaced with, such asReplace withAccording toWithThe row and column index included respectively, by IiBy these indexes The row and column at place is split, and the rectangular area for splitting gained is map grid, it is defined as follows.
WhereinWith Represent the index of map grid arrangement position in I.Such as 2 lower left corner legend of attached drawing, upper left corner figure in the legend Lattice are denoted as L1,1, L1,1The adjacent map grid in right side is L1,2, L1,1The adjacent map grid in downside is L2,1, and so on.Map gridBy I In includeRow, and comprisingRow determine map grid border.Step 2.3 algorithm flow refers to figure 10。
Step 3 is had no time area interval computation.If set training sample I binaryzation cartoon components ItcThe back of the body Scape pixel value is 0, then map gridArea ai,jIt is defined asThe number of included foreground pixel, whereinAndI.e.：
The map grid area that training sample is concentrated is often similar, therefore there are the section of a map grid area of having no time, this area Between be known as area section of having no time.If for the area of a map grid not in area section of having no time, which is probably to have free time figure Lattice (map grid for including flaw).For training sample set I1,I2…IN, map grid segmentation is carried out to each image and calculates map grid face Product, the minimum value and maximum of gained area are denoted as a respectively0(i) and a1(i), then the lower bound a in area section of having no time0And the upper bound a1It is defined by the formula：
WhereinWithA is represented respectively0(i) and a1(i) linear order collection (linearly ordered set), wherein i =1,2 ... N, i.e.,WithIn element according to value size ascending order arrangement,WithRespectivelyWithElement index,
2nd, test phase
On the parameter basis obtained in the training stage, the sub-picture that test phase concentrates test sample carries out flaw inspection Survey and position.Test phase includes two steps：Step 4 test sample map grid splits and the identification of step 5 flaw.
Step 4 test sample map grid is split.To a secondary given test sample, the meter of repeat step 2.1 to step 2.3 Calculate, difference lies in the training sample involved in calculating is replaced with test sample, finally obtain the row split position of test sampleWith column split positionAnd according toWithTest sample is divided into map grid.
Step 5 flaw identifies.For a secondary test sample I, carry out map grid segmentation and calculate map grid area, calculate map grid face Product histogramT is made to representTransverse axis scale, i.e. map grid area value range, h (t) representLongitudinal axis scale, i.e., A in Ii,jFor t'sNumber, calculate notch value t ' and cliff of displacement value t " respectively according to the following formula.
If t ' exists, t ' is set as a0, otherwise see that t " whether there is, if setting t " in the presence of if as a0.For I, any map grid face Product is less than a0Or more than a1Map grid be labeled as flaw.
The high efficiency experiment of the present invention proves：
Use Hong Kong University's Electrical and Electronic engineering department industry automatic in the Defect Detection recruitment evaluation of the method for the present invention Change 24 color textile product images that the 56 width pixel sizes that laboratory provides are 256 × 256, these images are turned in an experiment It is changed to the gray level image of 8.These images include a kind of pattern：Box-shaped image.Box-shaped image includes 30 indefectible and 26 width There is flaw image.Box-shaped image includes 5 kinds of flaw types：The broken ends of fractured bone (broken end), hole (hole), reticulate pattern (netting Multiple), cord (thick bar) and stria (thin bar), the particular number of every kind of flaw type refer to table 1 First row.All flaw images have the flaw reference map (ground-truth image) of formed objects, and flaw reference map is 2 It is worth image, wherein 1 represents flaw, 0 represents background.Algorithm for comparing includes WGIS, BB, RB and ER, the ginseng of these algorithms Number is set and document (Jia L., Liang J., Fabric defect inspection based on isotropic lattice segmentation,Journal of the Franklin Institute 354(13)(2017)5694- 5738) it is identical.
Index for assessment includes true positives (true positive, hereinafter referred to as TP), false positive (positiverate, hereinafter referred to as FPR), True Positive Rate (truepositiverate, hereinafter referred to as TPR), false positive rate (positiverate, hereinafter referred to as FPR), positive predictive value (positivepredictivevalue, hereinafter referred to as PPV) and Negative predictive value negativepredictivevalue, hereinafter referred to as NPV).TPR, which is weighed, represents flaw in flaw reference map Pixel is correctly demarcated as the ratio of flaw by algorithm, and FPR, which is weighed, represents the pixel of background by algorithmic error mark in flaw reference map It is set to the ratio of flaw, the flaw proportion in the flaw of PPV measure algorithms output in flaw reference map, NPV measure algorithms Background proportion in the background of output in flaw reference map.For TPR, PPV and NPV, desired value is the bigger the better, for FPR is then the smaller the better.Related mathematical definition can document (M.K.Ng, H.Y.T.Ngan, X.Yuan, et al., Patterned fabric inspection and visualization by the method of image Decomposition, IEEETrans.Autom.Sci.Eng.11 (3) (2014) 943-947) in find.The method of the present invention, The index calculating method of WGIS, BB, RB and ER and document (Jia L., Liang J., Fabric defect inspection based on isotropic lattice segmentation,Journal of the Franklin Institute 354 (13) (2017) 5694-5738) it is identical.Experimental Hardware platform is the CoreTMi7-3610QM of Intel containing processor 230-GHz With the laptop of 8.00GB memories, software is Windows 10 and Maltab8.4.
Table 1 enumerates box-shaped image Defect Detection as a result, the every row index value for wherein marking flaw type is corresponding method To the index average value of all test sample operation results of the flaw type.According to 1 overview of table, one column, the method for the present invention has most The very close optimal value (1.00) of excellent global ACC (0.54) and PPV (0.67), NPV (0.99).For the broken ends of fractured bone and hole type Flaw, the TPR of the method for the present invention is optimal.TPR (0.28) relatively optimal value of the method for the present invention on reticulate pattern (0.31).The method of the present invention is time figure of merit on the TPR of cord, and the TPR of stria is relatively low.The method of the present invention on the broken ends of fractured bone, The FPR of cord and stria is optimal or suboptimum, and the FPR of hole and reticulate pattern is larger.To sum up, the method for the present invention has reached complete Office optimal T PR and PPV, its very close optimal value of overall situation NPV, while the method for the present invention are especially suitable for detection box-shaped image The flaw of broken ends of fractured bone type.
1 box-shaped image Defect Detection result of table
Using the above-mentioned desirable embodiment according to the present invention as enlightenment, by above-mentioned description, relevant staff Various changes and amendments can be carried out in without departing from the scope of the present invention completely.The technical scope of this invention is not The content being confined on specification, it is necessary to which its technical scope is determined according to right.

Claims (6)

  1. A kind of 1. textile flaw detection method based on peak value coverage values and areal calculation, it is characterised in that：Including training rank Section and two stages of test phase；A series of training stage, according to indefectible textile gray level image (figures of hereinafter referred to as having no time As) calculate map grid segmentation needed for parameter, then to have no time image carry out map grid segmentation and calculate test phase flaw identification needed for Parameter；One secondary textile images are carried out map grid segmentation according to the parameter that the training stage obtains and judge that map grid is by test phase No to include flaw, finally mark contains map grid defective；
    The training stage comprises the following steps：
    Step 1：A series of parameter according to needed for images of having no time calculate map grid segmentation, to determine map grid ideal dimensions；
    Step 2：According to the map grid ideal dimensions obtained in step 1, map grid segmentation is carried out to training sample set, obtains training sample Map grid；
    Step 3：The map grid area of the training sample map grid of map grid segmentation generation in calculation procedure 2, so as to obtain training sample set In have no time the area section of having no time of image；
    The test phase comprises the following steps：
    Step 4：Test sample map grid is split, and to a secondary given test sample, test sample is carried out according to the method for step 2 Map grid is split, and obtains test sample map grid；
    Step 5：Method according to step 3 calculates the map grid area of test sample, and by result of calculation and the map grid of image of having no time Area is compared, to identify map grid defective.
  2. 2. the textile flaw detection method based on peak value coverage values and areal calculation, its feature exist as claimed in claim 1 In：Step 1 specifically includes following steps：
    Step 1.1：Background pixel projects, and the cartoon component of textile gray level image I is calculated according to RTV models, using Bradley Method binaryzation cartoon component, by morphological erosion and expansive working to binaryzation cartoon component noise reduction, using Moore- Neighbor track algorithms obtain binaryzation IcIn two value objects, calculate binaryzation cartoon component in two-value object area, delete Except area is not in section ((1- α) ma,(1+α)·ma) in two value object (wherein maFor two-value object area median,And 0 ＜ α ＜ 1) obtain binaryzation textile images Itc；Calculate ItcMiddle each row and column background pixel number, by line index liter Often row background pixel number obtains the projection of background pixel row for sequence arrangementCarried on the back by column index ascending order arrangement each column background pixel number Scape pixel column projects
    Step 1.2：Peak value coverage values are calculated, calculate the background pixel row projection of textile gray level image IPeak value, by peak value Projected by it in background pixel rowIn index ascending order arrange to obtain peak value sequenceFor prInA peak valueCalculate according to the following formulaCoverage values
    Projected with background pixel rowCoverage valuesComputational methods are identical, and the item of subscript r in above formula is replaced with tool There are the respective items of subscript c, calculatePeak value sequenceCalculateWherein1 ≤ipc；Calculate prThe ordered set of middle peak value coverage values Descending arranges middle element by size；ForInIt is a Element Meet in peak value sequence'sOrdered set is known asLevel peak valueTheLevel peak value in element by itsIn index ascending order arrangement；ForLevel peak Value, calculates each peak value and exists with its previous peak valueIn index difference absolute value, calculate the medians of these absolute valuesAnd its occurrence number Composition set Composition set Middle element value group Into setSimilarly, according toAnd pcCalculate and meet'sOrdered set " theLevel peak value "Calculate theFront and rear element exists in level peak valueIn index difference absolute value and its middle position ValueWith median occurrence number Form multiset Form multiset Middle element value composition set
    Step 1.3：Map grid ideal dimensions are calculated, to the I of training sample set1,I2…INIn i-th Training sample Ii, I is calculated according to step 1.2i'spr,pc,With CalculateValue setIiPreferable line numberIt is defined by the formula：
    Wherein, δ is Dirac delta function (Dirac delta function),IiPreferable columnsCalculate with It is similar, i.e., the item with subscript r in above formula is replaced with into the respective items with subscript c, such asReplace withMap grid ideal dimensions are defined asMedianWithMedian
  3. 3. the textile flaw detection method based on peak value coverage values and areal calculation, its feature exist as claimed in claim 2 In：Step 2 specifically includes following steps：
    Step 2.1：Background pixel projects, and calculating process includes step 1.1 and step 1.2；
    Step 2.2：Initial segmentation position is calculated, for i-th of training sample Ii, it is calculated according to step 2.1With Defined in calculation procedure 1.2WithAndWithAccording to What step 1.3 was calculatedWithIt is calculated as followsThere is most frequentLevel peak value
    Similarly, can calculateThere is most frequentLevel peak value, i.e., replace with the item with subscript r in above formula with The respective items of footmark c；
    Step 2.3：Final split position is calculated, at once split positionWith column split positionFor i-th of training sample Ii,WithInitial value be respectively step 2.2 calculate IiRow initial segmentation position SrWith row initial segmentation position Sc；WillIn Element by size ascending order arrange, find out least member thereinAnd greatest memberFour prediction bits are calculated as follows PutWith
    According toWithThe row and column index included respectively, by IiSplit by the row and column where these indexes, split institute The rectangular area obtained is map grid, it is defined as follows：
    <mrow> <msub> <mi>L</mi> <mrow> <msub> <mi>i</mi> <mi>r</mi> </msub> <mo>,</mo> <msub> <mi>i</mi> <mi>c</mi> </msub> </mrow> </msub> <mo>=</mo> <mo>{</mo> <mi>I</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>|</mo> <msubsup> <mi>S</mi> <msub> <mi>i</mi> <mi>r</mi> </msub> <mrow> <mo>(</mo> <mi>r</mi> <mo>)</mo> </mrow> </msubsup> <mo>&lt;</mo> <mi>x</mi> <mo>&lt;</mo> <msubsup> <mi>S</mi> <mrow> <msub> <mi>i</mi> <mi>r</mi> </msub> <mo>+</mo> <mn>1</mn> </mrow> <mrow> <mo>(</mo> <mi>r</mi> <mo>)</mo> </mrow> </msubsup> <mo>,</mo> <msubsup> <mi>S</mi> <msub> <mi>i</mi> <mi>c</mi> </msub> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> </msubsup> <mo>&lt;</mo> <mi>y</mi> <mo>&lt;</mo> <msubsup> <mi>S</mi> <mrow> <msub> <mi>i</mi> <mi>c</mi> </msub> <mo>+</mo> <mn>1</mn> </mrow> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> </msubsup> <mo>,</mo> <mo>}</mo> </mrow>
    Wherein,With Represent the index of map grid arrangement position in I.
  4. 4. the textile flaw detection method based on peak value coverage values and areal calculation, its feature exist as claimed in claim 3 In：Step 3 area interval computation of having no time comprises the following steps：
    If set training sample I binaryzation cartoon components ItcBackground pixel value be 0, then map gridAreaIt is defined asThe number of included foreground pixel, whereinAnd I.e.：
    For training sample set I1, I2…IN, map grid segmentation is carried out to each image and calculates map grid area, gained area is most Small value and maximum are denoted as respectivelyWithSo have no time the lower bound in area sectionAnd the upper boundIt is defined by the formula：
    WhereinWithRepresent respectivelyWithLinear order collection (linearly ordered set), wherein i=1, 2 ... N, i.e.,WithIn element according to value size ascending order arrangement,WithRespectivelyWithElement index,
  5. 5. the textile flaw detection method based on peak value coverage values and areal calculation, its feature exist as claimed in claim 4 In：The segmentation of step 4 test sample map grid specifically includes：To a secondary given test sample, repeat step 2.1 to step 2.3 Calculate, difference lies in the training sample involved in calculating is replaced with test sample, finally obtain the row framing bits of test sample PutWith column split positionAnd according toWithTest sample is divided into map grid.
  6. 6. the textile flaw detection method based on peak value coverage values and areal calculation, its feature exist as claimed in claim 5 In：The identification of step 5 flaw specifically includes：For a secondary test sample I, carry out map grid segmentation and calculate map grid area, calculate figure Lattice area histogramT is made to representTransverse axis scale, i.e. map grid area value range, h (t) representThe longitudinal axis carve Spend, i.e. in IFor t'sNumber, calculate notch value t ' and cliff of displacement value t " respectively according to the following formula：
    <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msup> <mi>t</mi> <mo>&amp;prime;</mo> </msup> <mo>=</mo> <mi>max</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>|</mo> <mi>h</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>&amp;equiv;</mo> <mn>0</mn> <mo>,</mo> <mi>h</mi> <mo>(</mo> <mrow> <mi>t</mi> <mo>+</mo> <mn>1</mn> </mrow> <mo>)</mo> <mo>&gt;</mo> <mn>0</mn> <mo>,</mo> <mi>t</mi> <mo>&lt;</mo> <msub> <mi>a</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msup> <mi>t</mi> <mrow> <mo>&amp;prime;</mo> <mo>&amp;prime;</mo> </mrow> </msup> <mo>=</mo> <mi>max</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>|</mo> <mi>h</mi> <mo>(</mo> <mrow> <mi>t</mi> <mo>+</mo> <mn>1</mn> </mrow> <mo>)</mo> <mo>/</mo> <mi>h</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>&gt;</mo> <mn>2</mn> <mo>,</mo> <mi>t</mi> <mo>&lt;</mo> <msub> <mi>a</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> </mtable> </mfenced>
    If t ' exists, set t ' asOtherwise see that t " whether there is, if set in the presence of if t " asFor I, any map grid area It is less thanOr it is more thanMap grid be labeled as flaw.
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101419706A (en) * 2008-12-11 2009-04-29 天津工业大学 Jersey wear flokkit and balling up grading method based on image analysis
CN102930531A (en) * 2012-09-28 2013-02-13 中国科学院自动化研究所 Detection method for repetition structure of building surface image
CN106770323A (en) * 2016-12-15 2017-05-31 常州大学 Based on the textile flaw detection method that hierarchical clustering and Gabor are filtered
CN107248152A (en) * 2017-05-18 2017-10-13 常州大学 Textile flaw detection method based on class grid pattern and its area features
CN107274385A (en) * 2017-05-18 2017-10-20 常州大学 Textile flaw detection method based on class grid pattern and its Gabor characteristic

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101419706A (en) * 2008-12-11 2009-04-29 天津工业大学 Jersey wear flokkit and balling up grading method based on image analysis
CN102930531A (en) * 2012-09-28 2013-02-13 中国科学院自动化研究所 Detection method for repetition structure of building surface image
CN106770323A (en) * 2016-12-15 2017-05-31 常州大学 Based on the textile flaw detection method that hierarchical clustering and Gabor are filtered
CN107248152A (en) * 2017-05-18 2017-10-13 常州大学 Textile flaw detection method based on class grid pattern and its area features
CN107274385A (en) * 2017-05-18 2017-10-20 常州大学 Textile flaw detection method based on class grid pattern and its Gabor characteristic

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
LIANG JIA等: "Fabric defect inspection based on isotropic lattice segmentation", 《《JOURNAL OF THE FRANKLIN INSTITUTE》》 *

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