CN105678770A - Wall crack detection device excellent in profile identification and filtering performances - Google Patents

Wall crack detection device excellent in profile identification and filtering performances Download PDF

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CN105678770A
CN105678770A CN201610012808.0A CN201610012808A CN105678770A CN 105678770 A CN105678770 A CN 105678770A CN 201610012808 A CN201610012808 A CN 201610012808A CN 105678770 A CN105678770 A CN 105678770A
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curvature
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profile
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noise
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潘燕
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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
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
    • G06V10/443—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components by matching or filtering
    • 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
    • G06T2207/20172—Image enhancement details
    • G06T2207/20182—Noise reduction or smoothing in the temporal domain; Spatio-temporal filtering
    • 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

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Abstract

The present invention discloses a wall crack detection device excellent in profile identification and filtering performances. The device comprises a common wall crack detection device and a target identification device mounted on the wall crack detection device. The identification device comprises a modeling module, a segmentation module, a merging module and a filtering module. According to the technical scheme of the invention, the target identification device is additionally arranged on the wall crack detection device, so that the detection capability of the wall crack detection device is effectively enhanced. The wall crack detection device can identify a target based the profile of the target. During the identification process, the profile noise of the target is effectively filtered, so that wall cracks can be found out.

Description

The wall body slit detecting device that a kind of outline identification filtering performance is good
Technical field
The present invention relates to wall body slit detection field, be specifically related to the wall body slit detecting device that a kind of outline identification filtering performance is good.
Background technology
In recent years, the phenomenons such as earthquake takes place frequently, jerry-built project are serious, and many houses all create wall body slit. The security of the lives and property of the wall body slit serious threat person mankind, if it is possible to find wall body slit in time, it becomes possible to avoid the generation of accident to a certain extent. But, current wall body slit detecting device detection poor performance, it is impossible to profile is well filtered, and the present invention arises at the historic moment for this problem of solution exactly.
Objective contour identification is as the important means of target recognition, owing to practical application being subject to the impact of the factor such as noise, quantization error, objective contour inevitably results from distortion, and for accurate description contour feature, it is very necessary that the filtering of objective contour processes. At present, scholars propose the filtering algorithm of many noisy profiles, but ubiquity amount of calculation is huge, noise reduction is undesirable, be susceptible to excessively filtering causes the problems such as target distortion.
Summary of the invention
For the problems referred to above, the present invention provides the wall body slit detecting device that a kind of outline identification filtering performance is good.
The purpose of the present invention realizes by the following technical solutions:
The wall body slit detecting device that a kind of outline identification filtering performance is good, including common wall crack detecting device and the Target Identification Unit being arranged on wall body slit detecting device, this wall body slit detecting device has very strong power of test, target can be identified by Target Identification Unit according to objective contour, it is characterized in that, including MBM, segmentation module, merging module and filtration module;
MBM, for setting up the parametrization equation of objective contour: for given objective contour G (t), its arc length parameterized equation is expressed as G (t)=(x (t), y (t)), wherein x (t) and y (t) represents the coordinate of profile point respectively, t represents the parameter of contour curve equation and t ∈ [0,1];
The arc length parameterized equation of noisy profile is expressed as: GN(t)=G (t)+N1(t)+N2(t) G (t), wherein additive noise part N1(t)=N1(x1(t),y1(t)), multiplicative noise part N2(t)=N2(x2(t),y2(t));
Segmentation module, the segmentation for profile: objective contour G (t) and noisy profile GNCurvature corresponding to (t) respectively k (t) and kN(t); Window function W (n) selecting width x width to be D, to curvature kNT () carries out neighborhood averaging, obtain average curvature k1NT (), sorts to the curvature value in window simultaneously, selected intermediate value curvature k2NT (), by average curvature k1N(t) and intermediate value curvature k2NThe absolute value of (t) difference and selected threshold value T1Compare, determine noisy contour curvature k ' according to comparative resultN(t), it may be assumed that
When | k1N(t)-k2N(t)|>T1Time, k 'N(t)=k1N(t)
Otherwise, k 'N(t)=k2N(t);
Owing to profile point that curvature value is bigger generally reflects the marked feature of target, according to k 'NT profile point all in profile are divided into characteristic point or non-characteristic point by (), set variable weight TK, by judging that objective contour feature is how many, adaptive decision TK, when | k 'N(t)|<TK*max|k′N(t) | time, characteristic function f (t)=0
Otherwise, characteristic function f (t)=1.
Merge module: for rejecting the pseudo-random numbers generation owing to noise jamming produces, and the characteristic point and non-characteristic point that cannot form continuum is merged operation, thus obtaining effective characteristic area and non-characteristic area: a selected starting point O, profile starting point extends the point that merging is adjacent to both sides, using this starting point type as this region preset kind, extend each S × μ to both sides0In time, stops, and wherein S is default minimum length,For the real-time curvature correction factor at O point place,Represent the radius of curvature of O point,Represent the mean radius of curvature of the O point obtained by above-mentioned window function, real-time curvature correction factor μ0Different for the curvature according to difference, automatically revise development length, the distortion phenomenon after merging can be effectively reduced; Calculating number N+1 and the N-1 of dissimilarity in two side areas respectively, if the number of dissimilarity is less than the minimum number of the type dissimilarity set, then this region is identical with preset kind, otherwise, contrary with preset kind; Again with two halt O+1With an O-1Restart to calculate as starting point, extend S × μ laterallyO+1Or S × μO-1In time, stops, wherein μO+1And μO-1Represent some O respectively+1With an O-1The real-time curvature correction factor at place, O+1In two side areas, dissimilarity number is N+2, O-1In two side areas, dissimilarity number is N-2, according to above-mentioned decision condition, determine each section of types of profiles successively, the part of curtailment S, according to its ratio calculating dissimilarity number with S, counts corresponding characteristic area; Adjacent same type area is merged, obtains continuous print characteristic area and non-characteristic area;
Filtration module: multiplicative noise is owing to being relevant with picture signal, changing with the change of picture signal, adopt Wiener filtering to carry out first-level filtering and remove, now image information also includes the remnants property taken advantage of noise, by F wave filter F (x, y)=q × exp (-(x2+y2)/β2Carrying out two grades to filter, wherein q is by the coefficient of function normalization, it may be assumed that ∫ ∫ q × exp (-(x2+y2)/β2) dxdy=1, β be image template parameter;
After multiplicative noise filters, the arc length parameterized equation of noisy objective contour is expressed as GN(t) '=G (t)+N1(t); Assume that additive noise is white Gaussian noise: xN(t) '=x (t)+g1(t,σ2), yN(t) '=y (t)+g2(t,σ2), wherein xN(t) ' and yNT () ' represents after removal multiplicative noise each point coordinates, g on noisy profile respectively1(t,σ2) and g2(t,σ2) to be average respectively be zero, variance is σ2White Gaussian noise, for simulating additive noise in noisy objective contour;
Adopt functionNoisy profile is smoothed, called after K wave filter, divide through profile point classification and region, noisy profile GNT () ' is expressed as the combination of dissimilar contour segmentation:WhereinRepresent the contour segmentation comprising characteristic area,Represent the contour segmentation comprising non-characteristic area, choose the parameter of K wave filter according to contour feature distribution, consider global characteristics and local characteristic factor simultaneously, at characteristic area, in order to retain detailed information, orderAt non-characteristic area, in order to improve the effect suppressing noise, order Wherein σ ' estimates the overall variance obtained, σ for priori1Priori for selected characteristic area estimates variance, σ0Priori for selected non-characteristic area estimates variance,For the average real-time curvature correction factor of selected characteristic area,Average real-time curvature correction factor for selected non-characteristic area;In order to reach good smooth effect, choose the half length as K wave filter 85% confidence interval of each type region minimum length S, thus the K wave filter of the length self adaptation different parameters according to two class regions.
The present invention by installing Target Identification Unit additional on wall body slit detecting device, can effectively strengthen the power of test of wall body slit detecting device, wall body slit detecting device passes through objective contour identification target, can effectively filter out objective contour noise, thus finding wall body slit in identification process.
Accompanying drawing explanation
The invention will be further described to utilize accompanying drawing, but the embodiment in accompanying drawing does not constitute any limitation of the invention, for those of ordinary skill in the art, under the premise not paying creative work, it is also possible to obtain other accompanying drawing according to the following drawings.
Fig. 1 is the structured flowchart of the wall body slit detecting device that the outline identification filtering performance of the present invention is good.
Detailed description of the invention
The invention will be further described with the following Examples.
Fig. 1 is the structured flowchart of the present invention, comprising: MBM, segmentation module, merging module, filtration module.
Embodiment 1: the wall body slit detecting device that a kind of outline identification filtering performance is good, including common wall crack detecting device and the Target Identification Unit being arranged on wall body slit detecting device, this wall body slit detecting device has very strong power of test, target can be identified by Target Identification Unit according to objective contour, it is characterized in that, including MBM, segmentation module, merging module and filtration module;
MBM, for setting up the parametrization equation of objective contour: for given objective contour G (t), its arc length parameterized equation is expressed as G (t)=(x (t), y (t)), wherein x (t) and y (t) represents the coordinate of profile point respectively, t represents the parameter of contour curve equation and t ∈ [0,1];
The arc length parameterized equation of noisy profile is expressed as: GN(t)=G (t)+N1(t)+N2(t) G (t), wherein additive noise part N1(t)=N1(x1(t),y1(t)), multiplicative noise part N2(t)=N2(x2(t),y2(t));
Segmentation module, the segmentation for profile: objective contour G (t) and noisy profile GNCurvature corresponding to (t) respectively k (t) and kN(t); Owing to being subject to effect of noise, noisy profile GNThe curvature value k of (t) upper Partial Feature pointNT () can not accurately represent profile information, in order to obtain curvature accurately, selecting width is that { window function W (n) of 7,9}, to curvature k for D ∈NT () carries out neighborhood averaging, obtain average curvature k1NT (), sorts to the curvature value in window simultaneously, selected intermediate value curvature k2NT (), by average curvature k1N(t) and intermediate value curvature k2NThe absolute value of (t) difference and selected threshold value T1=0.24 compares, and determines noisy contour curvature k ' according to comparative resultN(t), it may be assumed that
As | k1N (t)-k2N (t) | > T1Time, k 'N(t)=k1N(t)
Otherwise, k 'N(t)=k2N(t);
Owing to profile point that curvature value is bigger generally reflects the marked feature of target, according to k 'NT profile point all in profile are divided into characteristic point or non-characteristic point by (), set variable weight TK, by judging that objective contour feature is how many, adaptive decision TK, when | k 'N(t)|<TK*max|k′N(t) | time, characteristic function f (t)=0
Otherwise, characteristic function f (t)=1;
The distribution of characteristic point obtained after classification and non-characteristic point is also discontinuous, it is impossible to it is carried out effective contour smoothing by selecting filter. In order to obtain good contour smoothing effect, it is necessary to the profile point of same type is merged process.
Merge module: for rejecting the pseudo-random numbers generation owing to noise jamming produces, and the characteristic point and non-characteristic point that cannot form continuum is merged operation, thus obtaining effective characteristic area and non-characteristic area: a selected starting point O, profile starting point extends the point that merging is adjacent to both sides, using this starting point type as this region preset kind, extend each S × μ to both sides0In time, stops, and wherein S is default minimum length, in this embodiment, and S=17,For the real-time curvature correction factor at O point place,Represent the radius of curvature of O point,Represent the mean radius of curvature of the O point obtained by above-mentioned window function, real-time curvature correction factor μ0Different for the curvature according to difference, automatically revise development length, the local length needed that curvature is big is less, and the length of the local needs that curvature is little more greatly, so can effectively reduce the distortion phenomenon after merging; Calculating number N+1 and the N-1 of dissimilarity in two side areas respectively, if the number of dissimilarity is less than the minimum number of the type dissimilarity set, then this region is identical with preset kind, otherwise, contrary with preset kind; Again with two halt O+1With an O-1Restart to calculate as starting point, extend S × μ laterallyO+1Or S × μO-1In time, stops, wherein μO+1And μO-1Represent some O respectively+1With an O-1The real-time curvature correction factor at place, O+1In two side areas, dissimilarity number is N+2, O-1In two side areas, dissimilarity number is N-2, according to above-mentioned decision condition, determine each section of types of profiles successively, the part of curtailment S, according to its ratio calculating dissimilarity number with S, counts corresponding characteristic area; Adjacent same type area is merged, obtains continuous print characteristic area and non-characteristic area.
Filtration module: multiplicative noise is owing to being relevant with picture signal, changing with the change of picture signal, adopt Wiener filtering to carry out first-level filtering and remove, now image information also includes the remnants property taken advantage of noise, by F wave filter F (x, y)=q × exp (-(x2+y2)/β2Carrying out two grades to filter, wherein q is by the coefficient of function normalization, it may be assumed that ∫ ∫ q × exp (-(x2+y2)/β2) dxdy=1, β be image template parameter;
After multiplicative noise filters, the arc length parameterized equation of noisy objective contour is expressed as GN(t) '=G (t)+N1(t); Assume that additive noise is white Gaussian noise: xN(t) '=x (t)+g1(t,σ2), yN(t) '=y (t)+g2(t,σ2), wherein xN(t) ' and yNT () ' represents after removal multiplicative noise each point coordinates, g on noisy profile respectively1(t,σ2) and g2(t,σ2) to be average respectively be zero, variance is σ2White Gaussian noise, for simulating additive noise in noisy objective contour;
Adopt functionNoisy profile is smoothed, called after K wave filter, divide through profile point classification and region, noisy profile GNT () ' is expressed as the combination of dissimilar contour segmentation:WhereinRepresent the contour segmentation comprising characteristic area,Represent the contour segmentation comprising non-characteristic area, choose the parameter of K wave filter according to contour feature distribution, consider global characteristics and local characteristic factor simultaneously, at characteristic area, in order to retain detailed information, orderAt non-characteristic area, pay close attention to the effect suppressing noise, orderWherein σ ' estimates the overall variance obtained, σ for priori1Priori for selected characteristic area estimates variance, σ0Priori for selected non-characteristic area estimates variance,For the average real-time curvature correction factor of selected characteristic area,Average real-time curvature correction factor for selected non-characteristic area; In order to reach good smooth effect, choose the half length as K wave filter 85% confidence interval of each type region minimum length S, thus the K wave filter of the length self adaptation different parameters according to two class regions.
In this embodiment, S=17, threshold value T1=0.24, window function width D ∈ { 7,9}, to noise intensity I ∈ { 10dB, the noisy image of 20dB} has good smooth effect, wall body slit detecting device passes through objective contour identification target, can effectively filter out objective contour noise, can both detect more than 5mm for wall body slit in identification process.
Embodiment 2: the wall body slit detecting device that a kind of outline identification filtering performance is good, including common wall crack detecting device and the Target Identification Unit being arranged on wall body slit detecting device, this wall body slit detecting device has very strong power of test, target can be identified by Target Identification Unit according to objective contour, it is characterized in that, including MBM, segmentation module, merging module and filtration module;
MBM, for setting up the parametrization equation of objective contour: for given objective contour G (t), its arc length parameterized equation is expressed as G (t)=(x (t), y (t)), wherein x (t) and y (t) represents the coordinate of profile point respectively, t represents the parameter of contour curve equation and t ∈ [0,1];
The arc length parameterized equation of noisy profile is expressed as: GN(t)=G (t)+N1(t)+N2(t) G (t), wherein additive noise part N1(t)=N1(x1(t),y1(t)), multiplicative noise part N2(t)=N2(x2(t),y2(t));
Segmentation module, the segmentation for profile: objective contour G (t) and noisy profile GNCurvature corresponding to (t) respectively k (t) and kN(t); Owing to being subject to effect of noise, noisy profile GNThe curvature value k of (t) upper Partial Feature pointNT () can not accurately represent profile information, in order to obtain curvature accurately, selecting width is that { window function W (n) of 10,12}, to curvature k for D ∈NT () carries out neighborhood averaging, obtain average curvature k1NT (), sorts to the curvature value in window simultaneously, selected intermediate value curvature k2NT (), by average curvature k1N(t) and intermediate value curvature k2NThe absolute value of (t) difference and selected threshold value T1=0.24 compares, and determines noisy contour curvature k ' according to comparative resultN(t), it may be assumed that
When | k1N(t)-k2N(t)|>T1Time, k 'N(t)=k1N(t)
Otherwise, k 'N(t)=k2N(t);
Owing to profile point that curvature value is bigger generally reflects the marked feature of target, according to k 'NT profile point all in profile are divided into characteristic point or non-characteristic point by (), set variable weight TK, by judging that objective contour feature is how many, adaptive decision TK, when | k 'N(t)|<TK*max|k′N(t) | time, characteristic function f (t)=0
Otherwise, characteristic function f (t)=1;
The distribution of characteristic point obtained after classification and non-characteristic point is also discontinuous, it is impossible to it is carried out effective contour smoothing by selecting filter. In order to obtain good contour smoothing effect, it is necessary to the profile point of same type is merged process.
Merge module: for rejecting the pseudo-random numbers generation owing to noise jamming produces, and the characteristic point and non-characteristic point that cannot form continuum is merged operation, thus obtaining effective characteristic area and non-characteristic area: a selected starting point O, profile starting point extends the point that merging is adjacent to both sides, using this starting point type as this region preset kind, extend each S × μ to both sides0In time, stops, and wherein S is default minimum length, in this embodiment S=19,For the real-time curvature correction factor at O point place,Represent the radius of curvature of O point,Represent the mean radius of curvature of the O point obtained by above-mentioned window function, real-time curvature correction factor μ0Different for the curvature according to difference, automatically revise development length, the local length needed that curvature is big is less, and the length of the local needs that curvature is little more greatly, so can effectively reduce the distortion phenomenon after merging; Calculating number N+1 and the N-1 of dissimilarity in two side areas respectively, if the number of dissimilarity is less than the minimum number of the type dissimilarity set, then this region is identical with preset kind, otherwise, contrary with preset kind;Again with two halt O+1With an O-1Restart to calculate as starting point, extend S × μ laterallyO+1Or S × μO-1In time, stops, wherein μO+1And μO-1Represent some O respectively+1With an O-1The real-time curvature correction factor at place, O+1In two side areas, dissimilarity number is N+2, O-1In two side areas, dissimilarity number is N-2, according to above-mentioned decision condition, determine each section of types of profiles successively, the part of curtailment S, according to its ratio calculating dissimilarity number with S, counts corresponding characteristic area; Adjacent same type area is merged, obtains continuous print characteristic area and non-characteristic area.
Filtration module: multiplicative noise is owing to being relevant with picture signal, changing with the change of picture signal, adopt Wiener filtering to carry out first-level filtering and remove, now image information also includes the remnants property taken advantage of noise, by F wave filter F (x, y)=q × exp (-(x2+y2)/β2Carrying out two grades to filter, wherein q is by the coefficient of function normalization, it may be assumed that ∫ ∫ q × exp (-(x2+y2)/β2) dxdy=1, β be image template parameter;
After multiplicative noise filters, the arc length parameterized equation of noisy objective contour is expressed as GN(t) '=G (t)+N1(t); Assume that additive noise is white Gaussian noise: xN(t) '=x (t)+g1(t,σ2), yN(t) '=y (t)+g2(t,σ2), wherein xN(t) ' and yNT () ' represents after removal multiplicative noise each point coordinates, g on noisy profile respectively1(t,σ2) and g2(t,σ2) to be average respectively be zero, variance is σ2White Gaussian noise, for simulating additive noise in noisy objective contour;
Adopt functionNoisy profile is smoothed, called after K wave filter, divide through profile point classification and region, noisy profile GNT () ' is expressed as the combination of dissimilar contour segmentation:WhereinRepresent the contour segmentation comprising characteristic area,Represent the contour segmentation comprising non-characteristic area, choose the parameter of K wave filter according to contour feature distribution, consider global characteristics and local characteristic factor simultaneously, at characteristic area, in order to retain detailed information, orderAt non-characteristic area, pay close attention to the effect suppressing noise, orderWherein σ ' estimates the overall variance obtained, σ for priori1Priori for selected characteristic area estimates variance, σ0Priori for selected non-characteristic area estimates variance,For the average real-time curvature correction factor of selected characteristic area,Average real-time curvature correction factor for selected non-characteristic area; In order to reach good smooth effect, choose the half length as K wave filter 85% confidence interval of each type region minimum length S, thus the K wave filter of the length self adaptation different parameters according to two class regions.
In this embodiment, S=19, threshold value T1=0.24, window function width D ∈ { 10,12}, to noise intensity I ∈ { 20dB, the noisy image of 30dB} has good smooth effect, wall body slit detecting device passes through objective contour identification target, can effectively filter out objective contour noise, can both detect more than 5mm for wall body slit in identification process.
Embodiment 3: the wall body slit detecting device that a kind of outline identification filtering performance is good, including common wall crack detecting device and the Target Identification Unit being arranged on wall body slit detecting device, this wall body slit detecting device has very strong power of test, target can be identified by Target Identification Unit according to objective contour, it is characterized in that, including MBM, segmentation module, merging module and filtration module;
MBM, for setting up the parametrization equation of objective contour: for given objective contour G (t), its arc length parameterized equation is expressed as G (t)=(x (t), y (t)), wherein x (t) and y (t) represents the coordinate of profile point respectively, t represents the parameter of contour curve equation and t ∈ [0,1];
The arc length parameterized equation of noisy profile is expressed as: GN(t)=G (t)+N1(t)+N2(t) G (t), wherein additive noise part N1(t)=N1(x1(t),y1(t)), multiplicative noise part N2(t)=N2(x2(t),y2(t));
Segmentation module, the segmentation for profile: objective contour G (t) and noisy profile GNCurvature corresponding to (t) respectively k (t) and kN(t); Owing to being subject to effect of noise, noisy profile GNThe curvature value k of (t) upper Partial Feature pointNT () can not accurately represent profile information, in order to obtain curvature accurately, selecting width is that { window function W (n) of 13,14}, to curvature k for D ∈NT () carries out neighborhood averaging, obtain average curvature k1NT (), sorts to the curvature value in window simultaneously, selected intermediate value curvature k2NT (), by average curvature k1N(t) and intermediate value curvature k2NThe absolute value of (t) difference and selected threshold value T1=0.26 compares, and determines noisy contour curvature k ' according to comparative resultN(t), it may be assumed that
When | k1N(t)-k2N(t)|>T1Time, k 'N(t)=k1N(t)
Otherwise, k 'N(t)=k2N(t);
Owing to profile point that curvature value is bigger generally reflects the marked feature of target, according to k 'NT profile point all in profile are divided into characteristic point or non-characteristic point by (), set variable weight TK, by judging that objective contour feature is how many, adaptive decision TK, when | k 'N(t)|<TK*max|k′N(t) | time, characteristic function f (t)=0
Otherwise, characteristic function f (t)=1;
The distribution of characteristic point obtained after classification and non-characteristic point is also discontinuous, it is impossible to it is carried out effective contour smoothing by selecting filter. In order to obtain good contour smoothing effect, it is necessary to the profile point of same type is merged process.
Merge module: for rejecting the pseudo-random numbers generation owing to noise jamming produces, and the characteristic point and non-characteristic point that cannot form continuum is merged operation, thus obtaining effective characteristic area and non-characteristic area: a selected starting point O, profile starting point extends the point that merging is adjacent to both sides, using this starting point type as this region preset kind, extend each S × μ to both sides0In time, stops, and wherein S is default minimum length, in this embodiment S=21,For the real-time curvature correction factor at O point place,Represent the radius of curvature of O point,Represent the mean radius of curvature of the O point obtained by above-mentioned window function, real-time curvature correction factor μ0Different for the curvature according to difference, automatically revise development length, the local length needed that curvature is big is less, and the length of the local needs that curvature is little more greatly, so can effectively reduce the distortion phenomenon after merging; Calculating number N+1 and the N-1 of dissimilarity in two side areas respectively, if the number of dissimilarity is less than the minimum number of the type dissimilarity set, then this region is identical with preset kind, otherwise, contrary with preset kind; Again with two halt O+1With an O-1Restart to calculate as starting point, extend S × μ laterallyO+1Or S × μO-1In time, stops, wherein μO+1And μO-1Represent some O respectively+1With an O-1The real-time curvature correction factor at place, O+1In two side areas, dissimilarity number is N+2, O-1In two side areas, dissimilarity number is N-2, according to above-mentioned decision condition, determine each section of types of profiles successively, the part of curtailment S, according to its ratio calculating dissimilarity number with S, counts corresponding characteristic area; Adjacent same type area is merged, obtains continuous print characteristic area and non-characteristic area.
Filtration module: multiplicative noise is owing to being relevant with picture signal, changing with the change of picture signal, adopt Wiener filtering to carry out first-level filtering and remove, now image information also includes the remnants property taken advantage of noise, by F wave filter F (x, y)=q × exp (-(x2+y2)/β2Carrying out two grades to filter, wherein q is by the coefficient of function normalization, it may be assumed that ∫ ∫ q × exp (-(x2+y2)/β2) dxdy=1, β be image template parameter;
After multiplicative noise filters, the arc length parameterized equation of noisy objective contour is expressed as GN(t) '=G (t)+N1(t); Assume that additive noise is white Gaussian noise: xN(t) '=x (t)+g1(t,σ2), yN(t) '=y (t)+g2(t,σ2), wherein xN(t) ' and yNT () ' represents after removal multiplicative noise each point coordinates, g on noisy profile respectively1(t,σ2) and g2(t,σ2) to be average respectively be zero, variance is σ2White Gaussian noise, for simulating additive noise in noisy objective contour;
Adopt functionNoisy profile is smoothed, called after K wave filter, divide through profile point classification and region, noisy profile GNT () ' is expressed as the combination of dissimilar contour segmentation:WhereinRepresent the contour segmentation comprising characteristic area,Represent the contour segmentation comprising non-characteristic area, choose the parameter of K wave filter according to contour feature distribution, consider global characteristics and local characteristic factor simultaneously, at characteristic area, in order to retain detailed information, orderAt non-characteristic area, pay close attention to the effect suppressing noise, orderWherein σ ' estimates the overall variance obtained, σ for priori1Priori for selected characteristic area estimates variance, σ0Priori for selected non-characteristic area estimates variance,For the average real-time curvature correction factor of selected characteristic area,Average real-time curvature correction factor for selected non-characteristic area; In order to reach good smooth effect, choose the half length as K wave filter 85% confidence interval of each type region minimum length S, thus the K wave filter of the length self adaptation different parameters according to two class regions.
In this embodiment, S=21, threshold value T1=0.26, window function width D ∈ { 13,14}, to noise intensity I ∈ { 30dB, the noisy image of 40dB} has good smooth effect, amount of calculation and detailed information reservation situation all interior in zone of acceptability and acquirement preferably balances, and wall body slit detecting device passes through objective contour identification target, identification process can effectively filter out objective contour noise, can both detect more than 5mm for wall body slit.
Embodiment 4: the wall body slit detecting device that a kind of outline identification filtering performance is good, including common wall crack detecting device and the Target Identification Unit being arranged on wall body slit detecting device, this wall body slit detecting device has very strong power of test, target can be identified by Target Identification Unit according to objective contour, it is characterized in that, including MBM, segmentation module, merging module and filtration module;
MBM, for setting up the parametrization equation of objective contour: for given objective contour G (t), its arc length parameterized equation is expressed as G (t)=(x (t), y (t)), wherein x (t) and y (t) represents the coordinate of profile point respectively, t represents the parameter of contour curve equation and t ∈ [0,1];
The arc length parameterized equation of noisy profile is expressed as: GN(t)=G (t)+N1(t)+N2(t) G (t), wherein additive noise part N1(t)=N1(x1(t),y1(t)), multiplicative noise part N2(t)=N2(x2(t),y2(t));
Segmentation module, the segmentation for profile: objective contour G (t) and noisy profile GNCurvature corresponding to (t) respectively k (t) and kN(t); Owing to being subject to effect of noise, noisy profile GNThe curvature value k of (t) upper Partial Feature pointNT () can not accurately represent profile information, in order to obtain curvature accurately, selecting width is that { window function W (n) of 15,17}, to curvature k for D ∈NT () carries out neighborhood averaging, obtain average curvature k1NT (), sorts to the curvature value in window simultaneously, selected intermediate value curvature k2NT (), by average curvature k1N(t) and intermediate value curvature k2NThe absolute value of (t) difference and selected threshold value T1=0.28 compares, and determines noisy contour curvature k ' according to comparative resultN(t), it may be assumed that
When | k1N(t)-k2N(t)|>T1Time, k 'N(t)=k1N(t)
Otherwise, k 'N(t)=k2N(t);
Owing to profile point that curvature value is bigger generally reflects the marked feature of target, according to k 'NT profile point all in profile are divided into characteristic point or non-characteristic point by (), set variable weight TK, by judging that objective contour feature is how many, adaptive decision TK, when | k 'N(t)|<TK*max|k′N(t) | time, characteristic function f (t)=0
Otherwise, characteristic function f (t)=1;
The distribution of characteristic point obtained after classification and non-characteristic point is also discontinuous, it is impossible to it is carried out effective contour smoothing by selecting filter. In order to obtain good contour smoothing effect, it is necessary to the profile point of same type is merged process.
Merge module: for rejecting the pseudo-random numbers generation owing to noise jamming produces, and the characteristic point and non-characteristic point that cannot form continuum is merged operation, thus obtaining effective characteristic area and non-characteristic area: a selected starting point O, profile starting point extends the point that merging is adjacent to both sides, using this starting point type as this region preset kind, extend each S × μ to both sides0In time, stops, and wherein S is default minimum length, in this embodiment S=23,For the real-time curvature correction factor at O point place,Represent the radius of curvature of O point,Represent the mean radius of curvature of the O point obtained by above-mentioned window function, real-time curvature correction factor μ0Different for the curvature according to difference, automatically revise development length, the local length needed that curvature is big is less, and the length of the local needs that curvature is little more greatly, so can effectively reduce the distortion phenomenon after merging; Calculating number N+1 and the N-1 of dissimilarity in two side areas respectively, if the number of dissimilarity is less than the minimum number of the type dissimilarity set, then this region is identical with preset kind, otherwise, contrary with preset kind; Again with two halt O+1With an O-1Restart to calculate as starting point, extend S × μ laterallyO+1Or S × μO-1In time, stops, wherein μO+1And μO-1Represent some O respectively+1With an O-1The real-time curvature correction factor at place, O+1In two side areas, dissimilarity number is N+2, O-1In two side areas, dissimilarity number is N-2, according to above-mentioned decision condition, determine each section of types of profiles successively, the part of curtailment S, according to its ratio calculating dissimilarity number with S, counts corresponding characteristic area; Adjacent same type area is merged, obtains continuous print characteristic area and non-characteristic area.
Filtration module: multiplicative noise is owing to being relevant with picture signal, changing with the change of picture signal, adopt Wiener filtering to carry out first-level filtering and remove, now image information also includes the remnants property taken advantage of noise, by F wave filter F (x, y)=q × exp (-(x2+y2)/β2Carrying out two grades to filter, wherein q is by the coefficient of function normalization, it may be assumed that ∫ ∫ q × exp (-(x2+y2)/β2) dxdy=1, β be image template parameter;
After multiplicative noise filters, the arc length parameterized equation of noisy objective contour is expressed as GN(t) '=G (t)+N1(t); Assume that additive noise is white Gaussian noise: xN(t) '=x (t)+g1(t,σ2), yN(t) '=y (t)+g2(t,σ2), wherein xN(t) ' and yNT () ' represents after removal multiplicative noise each point coordinates, g on noisy profile respectively1(t,σ2) and g2(t,σ2) to be average respectively be zero, variance is σ2White Gaussian noise, for simulating additive noise in noisy objective contour;
Adopt functionNoisy profile is smoothed, called after K wave filter, divide through profile point classification and region, noisy profile GNT () ' is expressed as the combination of dissimilar contour segmentation:WhereinRepresent the contour segmentation comprising characteristic area,Represent the contour segmentation comprising non-characteristic area, choose the parameter of K wave filter according to contour feature distribution, consider global characteristics and local characteristic factor simultaneously, at characteristic area, in order to retain detailed information, orderAt non-characteristic area, pay close attention to the effect suppressing noise, orderWherein σ ' estimates the overall variance obtained, σ for priori1Priori for selected characteristic area estimates variance, σ0Priori for selected non-characteristic area estimates variance,For the average real-time curvature correction factor of selected characteristic area,Average real-time curvature correction factor for selected non-characteristic area;In order to reach good smooth effect, choose the half length as K wave filter 85% confidence interval of each type region minimum length S, thus the K wave filter of the length self adaptation different parameters according to two class regions.
In this embodiment, S=23, threshold value T1=0.28, window function width D ∈ { 15,17}, to noise intensity I ∈, { 40dB, although the noisy image of 50dB} adds part amount of calculation, but has excellent smooth effect to the image in this interval, and detailed information reservation situation is better, wall body slit detecting device passes through objective contour identification target, can effectively filter out objective contour noise, can both detect more than 5mm for wall body slit in identification process.
Embodiment 5: the wall body slit detecting device that a kind of outline identification filtering performance is good, including common wall crack detecting device and the Target Identification Unit being arranged on wall body slit detecting device, this wall body slit detecting device has very strong power of test, target can be identified by Target Identification Unit according to objective contour, it is characterized in that, including MBM, segmentation module, merging module and filtration module;
MBM, for setting up the parametrization equation of objective contour: for given objective contour G (t), its arc length parameterized equation is expressed as G (t)=(x (t), y (t)), wherein x (t) and y (t) represents the coordinate of profile point respectively, t represents the parameter of contour curve equation and t ∈ [0,1];
The arc length parameterized equation of noisy profile is expressed as: GN(t)=G (t)+N1(t)+N2(t) G (t), wherein additive noise part N1(t)=N1(x1(t),y1(t)), multiplicative noise part N2(t)=N2(x2(t),y2(t));
Segmentation module, the segmentation for profile: objective contour G (t) and noisy profile GNCurvature corresponding to (t) respectively k (t) and kN(t); Owing to being subject to effect of noise, noisy profile GNThe curvature value k of (t) upper Partial Feature pointNT () can not accurately represent profile information, in order to obtain curvature accurately, selecting width is that { window function W (n) of 17,19}, to curvature k for D ∈NT () carries out neighborhood averaging, obtain average curvature k1NT (), sorts to the curvature value in window simultaneously, selected intermediate value curvature k2NT (), by average curvature k1N(t) and intermediate value curvature k2NThe absolute value of (t) difference and selected threshold value T1=0.26 compares, and determines noisy contour curvature k ' according to comparative resultN(t), it may be assumed that
When | k1N(t)-k2N(t)|>T1Time, k 'N(t)=k1N(t)
Otherwise, k 'N(t)=k2N(t);
Owing to profile point that curvature value is bigger generally reflects the marked feature of target, according to k 'NT profile point all in profile are divided into characteristic point or non-characteristic point by (), set variable weight TK, by judging that objective contour feature is how many, adaptive decision TK, when | k 'N(t)|<TK*max|k′N(t) | time, characteristic function f (t)=0
Otherwise, characteristic function f (t)=1;
The distribution of characteristic point obtained after classification and non-characteristic point is also discontinuous, it is impossible to it is carried out effective contour smoothing by selecting filter. In order to obtain good contour smoothing effect, it is necessary to the profile point of same type is merged process.
Merge module: for rejecting the pseudo-random numbers generation owing to noise jamming produces, and the characteristic point and non-characteristic point that cannot form continuum is merged operation, thus obtaining effective characteristic area and non-characteristic area: a selected starting point O, profile starting point extends the point that merging is adjacent to both sides, using this starting point type as this region preset kind, extend each S × μ to both sides0In time, stops, and wherein S is default minimum length, in this embodiment S=25,For the real-time curvature correction factor at O point place,Represent the radius of curvature of O point,Represent the mean radius of curvature of the O point obtained by above-mentioned window function, real-time curvature correction factor μ0Different for the curvature according to difference, automatically revise development length, the local length needed that curvature is big is less, and the length of the local needs that curvature is little more greatly, so can effectively reduce the distortion phenomenon after merging;Calculating number N+1 and the N-1 of dissimilarity in two side areas respectively, if the number of dissimilarity is less than the minimum number of the type dissimilarity set, then this region is identical with preset kind, otherwise, contrary with preset kind; Again with two halt O+1With an O-1Restart to calculate as starting point, extend S × μ laterallyO+1Or S × μO-1In time, stops, wherein μO+1And μO-1Represent some O respectively+1With an O-1The real-time curvature correction factor at place, O+1In two side areas, dissimilarity number is N+2, O-1In two side areas, dissimilarity number is N-2, according to above-mentioned decision condition, determine each section of types of profiles successively, the part of curtailment S, according to its ratio calculating dissimilarity number with S, counts corresponding characteristic area; Adjacent same type area is merged, obtains continuous print characteristic area and non-characteristic area.
Filtration module: multiplicative noise is owing to being relevant with picture signal, changing with the change of picture signal, adopt Wiener filtering to carry out first-level filtering and remove, now image information also includes the remnants property taken advantage of noise, by F wave filter F (x, y)=q × exp (-(x2+y2)/β2Carrying out two grades to filter, wherein q is by the coefficient of function normalization, it may be assumed that ∫ ∫ q × exp (-(x2+y2)/β2) dxdy=1, β be image template parameter;
After multiplicative noise filters, the arc length parameterized equation of noisy objective contour is expressed as GN(t) '=G (t)+N1(t); Assume that additive noise is white Gaussian noise: xN(t) '=x (t)+g1(t,σ2), yN(t) '=y (t)+g2(t,σ2), wherein xN(t) ' and yNT () ' represents after removal multiplicative noise each point coordinates, g on noisy profile respectively1(t,σ2) and g2(t,σ2) to be average respectively be zero, variance is σ2White Gaussian noise, for simulating additive noise in noisy objective contour;
Adopt functionNoisy profile is smoothed, called after K wave filter, divide through profile point classification and region, noisy profile GNT () ' is expressed as the combination of dissimilar contour segmentation:WhereinRepresent the contour segmentation comprising characteristic area,Represent the contour segmentation comprising non-characteristic area, choose the parameter of K wave filter according to contour feature distribution, consider global characteristics and local characteristic factor simultaneously, at characteristic area, in order to retain detailed information, orderAt non-characteristic area, pay close attention to the effect suppressing noise, orderWherein σ ' estimates the overall variance obtained, σ for priori1Priori for selected characteristic area estimates variance, σ0Priori for selected non-characteristic area estimates variance,For the average real-time curvature correction factor of selected characteristic area,Average real-time curvature correction factor for selected non-characteristic area; In order to reach good smooth effect, choose the half length as K wave filter 85% confidence interval of each type region minimum length S, thus the K wave filter of the length self adaptation different parameters according to two class regions.
In this embodiment, S=25, threshold value T1=0.26, window function width D ∈ { 17,19}, to noise intensity I ∈ { 50dB, the noisy image of 60dB} has preferably smooth effect, and detailed information retains situation better, and wall body slit detecting device passes through objective contour identification target, identification process can effectively filter out objective contour noise, can both detect more than 5mm for wall body slit.
Finally should be noted that; above example is only in order to illustrate technical scheme; but not limiting the scope of the invention; although having made to explain to the present invention with reference to preferred embodiment; it will be understood by those within the art that; technical scheme can be modified or equivalent replacement, without deviating from the spirit and scope of technical solution of the present invention.
Data simulation
This wall body slit detecting device have the beneficial effect that the multiformity for noise type and the unicity of current denoising method, adopt a kind of novel repeatedly filter, and propose new contour segmentation, merging means and filter function;Amount of calculation is relative and uncomplicated, considers the factor of global characteristics and local feature and smooth effective except making an uproar simultaneously; Consider profile diversity between dissimilar region, between suppression noise and reservation details, obtain good balance; Curvature according to difference is different, and development length is automatic adaptive change correspondingly, effectively reduces the distortion phenomenon after merging.
By emulating, this device and other device is adopted to compare under noise intensity N, the discrimination such as following table to target:

Claims (2)

1. the wall body slit detecting device that an outline identification filtering performance is good, including common wall crack detecting device and the Target Identification Unit being arranged on wall body slit detecting device, this wall body slit detecting device has very strong power of test, target can be identified by Target Identification Unit according to objective contour, it is characterized in that, including MBM, segmentation module, merging module and filtration module; Wherein,
MBM, for setting up the parametrization equation of objective contour: for given objective contour G (t), its arc length parameterized equation is expressed as G (t)=(x (t), y (t)), wherein x (t) and y (t) represents the coordinate of profile point respectively, t represents the parameter of contour curve equation and t ∈ [0,1];
The arc length parameterized equation of noisy profile is expressed as: GN(t)=G (t)+N1(t)+N2(t) G (t), wherein additive noise part N1(t)=N1(x1(t),y1(t)), multiplicative noise part N2(t)=N2(x2(t),y2(t));
Segmentation module, the segmentation for profile: objective contour G (t) and noisy profile GNCurvature corresponding to (t) respectively k (t) and kN(t); Window function W (n) selecting width to be D, D ∈ 7,9}, to curvature kNT () carries out neighborhood averaging, obtain average curvature k1NT (), sorts to the curvature value in window simultaneously, selected intermediate value curvature k2NT (), by average curvature k1N(t) and intermediate value curvature k2NThe absolute value of (t) difference and selected threshold value T1Compare, determine noisy contour curvature k ' according to comparative resultN(t), T1=0.2, it may be assumed that
When | k1N(t)-k2N(t)|>T1Time, k 'N(t)=k1N(t)
Otherwise, k 'N(t)=k2N(t);
Owing to profile point that curvature value is bigger generally reflects the marked feature of target, according to k 'NT profile point all in profile are divided into characteristic point or non-characteristic point by (), set variable weight TK, by judging that objective contour feature is how many, adaptive decision TK,
When | k 'N(t)|<TK*max|k′N(t) | time, characteristic function f (t)=0
Otherwise, characteristic function f (t)=1.
2. wall body slit detecting device according to claim 1, it is further characterized in that, merge module: for rejecting the pseudo-random numbers generation owing to noise jamming produces, and the characteristic point and non-characteristic point that cannot form continuum is merged operation, thus obtaining effective characteristic area and non-characteristic area: a selected starting point O, profile starting point extends the point that merging is adjacent to both sides, using this starting point type as this region preset kind, extends each S × μ to both sides0In time, stops, and wherein S is default minimum length, if S=15,For the real-time curvature correction factor at O point place,Represent the radius of curvature of O point,Represent the mean radius of curvature of the O point obtained by above-mentioned window function, real-time curvature correction factor μ0Different for the curvature according to difference, automatically revise development length, the distortion phenomenon after merging can be effectively reduced; Calculating number N+1 and the N-1 of dissimilarity in two side areas respectively, if the number of dissimilarity is less than the minimum number of the type dissimilarity set, then this region is identical with preset kind, otherwise, contrary with preset kind;Again with two halt O+1With an O-1Restart to calculate as starting point, extend S × μ laterallyO+1Or S × μO-1In time, stops, wherein μo+1And μO-1Represent some O respectively+1With an O-1The real-time curvature correction factor at place, O+1In two side areas, dissimilarity number is N+2, O-1In two side areas, dissimilarity number is N-2, according to above-mentioned decision condition, determine each section of types of profiles successively, the part of curtailment S, according to its ratio calculating dissimilarity number with S, counts corresponding characteristic area; Adjacent same type area is merged, obtains continuous print characteristic area and non-characteristic area;
Filtration module: multiplicative noise is owing to being relevant with picture signal, changing with the change of picture signal, adopt Wiener filtering to carry out first-level filtering and remove, now image information also includes the property the taken advantage of noise of remnants, by F wave filter F (x, y)=q × exp (-(x2+y2)/β2Carrying out two grades to filter, wherein q is by the coefficient of function normalization, it may be assumed that ∫ ∫ q × exp (-(x2+y2)/β2) dxdy=1, β be image template parameter;
After multiplicative noise filters, the arc length parameterized equation of noisy objective contour is expressed as GN(t) '=G (t)+N1(t); Assume that additive noise is white Gaussian noise: xN(t) '=x (t)+g1(t,σ2), yN(t) '=y (t)+g2(t,σ2), wherein xN(t) ' and yNT () ' represents after removal multiplicative noise each point coordinates, g on noisy profile respectively1(t,σ2) and g2(t,σ2) to be average respectively be zero, variance is σ2White Gaussian noise, for simulating additive noise in noisy objective contour;
Adopt functionNoisy profile is smoothed, called after K wave filter, divide through profile point classification and region, noisy profile GNT () ' is expressed as the combination of dissimilar contour segmentation:WhereinRepresent the contour segmentation comprising characteristic area,Represent the contour segmentation comprising non-characteristic area, choose the parameter of K wave filter according to contour feature distribution, consider global characteristics and local characteristic factor simultaneously, at characteristic area, in order to retain detailed information, orderAt non-characteristic area, in order to improve the effect suppressing noise, order Wherein σ ' estimates the overall variance obtained, σ for priori1Priori for selected characteristic area estimates variance, σ0Priori for selected non-characteristic area estimates variance,For the average real-time curvature correction factor of selected characteristic area,Average real-time curvature correction factor for selected non-characteristic area; In order to reach good smooth effect, choose the half length as K wave filter 85% confidence interval of each type region minimum length S, thus the K wave filter of the length self adaptation different parameters according to two class regions.
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