CN105447485A - Landscape modeling detection tool realizing rapid detection - Google Patents
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Abstract
The invention discloses a landscape modeling detection tool realizing rapid detection. The landscape modeling detection tool comprises a common landscape modeling detection tool and an identification apparatus installed on the landscape modeling detection tool, wherein the identification apparatus comprises a modeling module, a segmentation module, a merging module and a filtering module. According to the invention, through additional arrangement of the identification apparatus on the landscape modeling detection tool, the landscape modeling detection tool can identify a landscape contour, can effectively filter landscape contour noise in the identification process and realizes a good identification effect. An identification result is vivid and visual, whether the contour is a specific shape can be easily seen, at the same time, accurate contour support is provided for the next step of modeling, and the burden of people is greatly mitigated.
Description
Technical Field
The invention relates to the field of landscape modeling, in particular to a landscape modeling detection tool capable of rapidly detecting landscape modeling.
Background
From ancient times to the present, the pursuit of beauty by human beings has never been stopped. Landscape modeling becomes an important part in urban construction, a landscape modeling detection tool is a necessary tool for most modelers in the modeling process, however, the naked eyes of the modelers are insensitive to the landscape outline, the traditional landscape modeling detection tool is low in identification efficiency and low in speed, great blindness is achieved in the modeling process, and the workload of the modelers is increased to a great extent. A landscape modeling detection tool for rapid detection is in urgent need of development.
The target contour recognition is an important means of target recognition, and because the target contour is influenced by factors such as noise, quantization error and the like in practical application, the target contour inevitably generates distortion, and filtering and smoothing processing of the target contour is necessary to accurately describe contour characteristics. At present, scholars propose a plurality of filtering smoothing algorithms of noisy contours, but the problems of huge calculation amount, non-ideal noise reduction effect, target distortion caused by excessive filtering and the like are generally existed.
Disclosure of Invention
In order to solve the problems, the invention provides a landscape modeling detection tool capable of rapidly detecting landscape modeling.
The purpose of the invention is realized by adopting the following technical scheme:
a landscape modeling detection tool for rapid detection comprises a common landscape modeling detection tool and a recognition device arranged on the landscape modeling detection tool, wherein the landscape modeling detection tool can recognize a landscape outline and is characterized by comprising a modeling module, a segmentation module, a combination module and a filtering module;
the modeling module is used for establishing a parameterized equation of the target contour: for a given target contour g (t), the arc length parameterization equation is expressed as g (t) ═ (x (t), y (t)), where x (t) and y (t) respectively represent the coordinates of the contour points, t represents the parameters of the contour curve equation, and t e [0,1 ];
the parametric equation of arc length of the noisy profile is expressed as GN(t)＝G(t)+N1(t)+N2(t) G (t), wherein the additive noise part N1(t)＝N1(x1(t),y1(t)), multiplicative noise part N2(t)＝N2(x2(t),y2(t))；
A segmentation module for segmenting the contour:target profile G (t) and noisy profile GN(t) the curvatures correspond to k (t) and k, respectivelyN(t); selecting a window function W (n) with width D for curvature kN(t) performing neighborhood averaging to obtain an average curvature k1N(t) simultaneously ordering the curvature values within the window, selecting a median curvature k2N(t) average curvature k1N(t) and median curvature k2N(T) the absolute value of the difference with a selected threshold T1Comparing, and determining the curvature k 'of the noisy contour according to the comparison result'N(t), namely:
when | k1N(t)-k2N(t)|>T1K'N(t)＝k1N(t)
Otherwise, k'N(t)＝k2N(t)；
Since contour points with larger curvature values generally reflect the salient features of the target in terms of k'N(T) dividing all contour points in the contour into feature points or non-feature points, and setting variable weight TKSelf-adaptive decision T by judging the amount of the target contour featuresKWhen k'N(t)|<TK*max|k′N(t) |, the characteristic function f (t) is 0
Otherwise, the feature function f (t) is 1.
A merging module for eliminating the false characteristic points generated by noise interference and merging the characteristic points and non-characteristic points which can not form continuous areas to obtain effective characteristic areas and non-characteristic areas, wherein a starting point O is selected, the outline starting points extend to two sides to merge adjacent points, the starting point type is used as the preset type of the area, and each S × mu extends to two sides0Stopping, wherein S is a preset minimum length,is the real-time curvature correction factor at point O,represents the radius of curvature of the O point,representing the mean radius of curvature of the O points obtained from the window function, the real-time curvature correction factor mu0The method is used for automatically correcting the extension length according to different curvatures of different points, and can effectively reduce the distortion phenomenon after combination; respectively calculating the number N +1 and the number N-1 of the different points in the two side areas, wherein if the number of the different points is less than the set minimum number of the different points of the type, the area is the same as the preset type, otherwise, the area is opposite to the preset type; then two stopping points O+1And point O-1Restart the calculation as the starting point, extend outward by S × mu0+1Or S × mu0-1Is stopped at a time of mu0+1And mu0-1Respectively represent point O+1And point O-1Real-time curvature correction coefficient of (d), O+1The number of the different points in the two side areas is N+2，O-1The number of the different points in the two side areas is N-2According to the judging conditions, sequentially determining the profile types of all the sections, calculating the number of different points of the part with the length less than S according to the ratio of the part with the length less than S, and counting the number into the corresponding characteristic area; adjacent areas of the same type are combined to obtain a continuous characteristic area and a non-characteristic area;
the filtering module adopts wiener filtering to carry out primary filtering because the multiplicative noise is related to the image signal and changes along with the change of the image signal, at the moment, the image information also contains residual multiplicative noise, and the F filter F (x, y) is q × exp- (x, y)2+y2)/β2Two-stage filtering is performed, where q is a coefficient that normalizes the function, i.e., (x ^ q × exp (- (x)2+y2)/β2) dxdy ═ 1, β are image template parameters;
after multiplicative noise filtering, the arc length parameterized equation of the noisy target contour is expressed as GN(t)’＝G(t)+N1(t); the additive noise is assumed to be white gaussian noise: x is the number ofN(t)’＝x(t)+g1(t,σ2)，yN(t)’＝y(t)+g2(t,σ2) Wherein x isN(t)' and yN(t)' represents coordinates of each point on the noisy contour after removing the multiplicative noise, g1(t,σ2) And g2(t,σ2) Respectively mean value of zero and variance of sigma2The Gaussian white noise is used for simulating additive noise in the noise-containing target contour;
using functionsSmoothing the noise-containing contour, named as K filter, and classifying contour points and dividing regions to obtain noise-containing contour GN(t)' is expressed as a combination of different types of contour segments:whereinRepresenting the segmentation of the contour that contains the region of the feature,representing contour segment containing non-feature region, selecting parameters of K filter according to contour feature distribution, and considering global feature and local feature factors, in the feature region, in order to retain detail informationIn the non-characteristic region, in order to raise the effect of suppressing noise Where σ' is the global variance, σ, estimated a priori1Estimating the variance, σ, a priori for selected feature regions0The variance is estimated a priori for the selected non-characteristic regions,the real-time curvature correction factor is averaged for the selected feature region,a real-time curvature correction factor for the mean of the selected non-characteristic region; in order to achieve a good smoothing effect, half of the minimum length S of each type of region is selected as the length of the 85% confidence interval of the K filter, so that the K filters with different parameters are self-adapted according to the lengths of the two types of regions.
According to the invention, the landscape modeling detection tool can identify the landscape outline by additionally arranging the identification device on the landscape modeling detection tool, so that the landscape outline noise can be effectively filtered in the identification process, and a good identification effect is achieved. The recognition result is visual and intuitive, whether the shape is the designated shape can be easily seen, meanwhile, accurate contour support is provided for the next modeling, and the burden of people is greatly reduced.
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The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
Fig. 1 is a block diagram of the landscape architecture examination tool for rapid examination according to the present invention.
Detailed Description
The invention is further described with reference to the following examples.
FIG. 1 is a block diagram of the present invention, which includes: the device comprises a modeling module, a segmentation module, a combination module and a filtering module.
Example 1: a landscape modeling detection tool for rapid detection comprises a common landscape modeling detection tool and a recognition device arranged on the landscape modeling detection tool, wherein the landscape modeling detection tool can recognize a landscape outline and is characterized by comprising a modeling module, a segmentation module, a combination module and a filtering module;
the modeling module is used for establishing a parameterized equation of the target contour: for a given target contour g (t), the arc length parameterization equation is expressed as g (t) ═ (x (t), y (t)), where x (t) and y (t) respectively represent the coordinates of the contour points, t represents the parameters of the contour curve equation, and t e [0,1 ];
the parametric equation of arc length of the noisy profile is expressed as GN(t)＝G(t)+N1(t)+N2(t) G (t), wherein the additive noise part N1(t)＝N1(x1(t),y1(t)), multiplicative noise part N2(t)＝N2(x2(t),y2(t))；
A segmentation module for segmenting the contour: target profile G (t) and noisy profile GN(t) the curvatures correspond to k (t) and k, respectivelyN(t); due to the influence of noise, the noisy contour GN(t) curvature value k of upper part feature pointN(t) cannot accurately represent the contour information, and in order to obtain an accurate curvature, a window function W (n) having a width D ∈ {7, 9} is selected, and a curvature k is calculatedN(t) performing neighborhood averaging to obtain an average curvature k1N(t) simultaneously ordering the curvature values within the window, selecting a median curvature k2N(t) average curvature k1N(t) and median curvature k2N(T) the absolute value of the difference with a selected threshold T1Comparing the two values to 0.24, and determining the curvature k 'of the noisy contour according to the comparison result'N(t), namely:
when | k1N (T) -k2N (T) | > T1K'N(t)＝k1N(t)
Otherwise, k'N(t)＝k2N(t)；
Since contour points with larger curvature values generally reflect the salient features of the target in terms of k'N(T) dividing all contour points in the contour into feature points or non-feature points, and setting variable weight TKSelf-adaptive decision T by judging the amount of the target contour featuresKWhen k'N(t)|<TK*max|k′N(t) |, the characteristic function f (t) is 0
Otherwise, the feature function f (t) is 1;
the distribution of the characteristic points and the non-characteristic points obtained after classification is not continuous, and a filter cannot be selected to carry out effective contour smoothing on the characteristic points and the non-characteristic points. In order to obtain a good contour smoothing effect, it is necessary to perform merging processing on contour points of the same type.
A merging module for eliminating the false characteristic points generated by noise interference and merging the characteristic points and non-characteristic points which can not form continuous areas to obtain effective characteristic areas and non-characteristic areas, wherein a starting point O is selected, the outline starting points extend to two sides to merge adjacent points, the starting point type is used as the preset type of the area, and each S × mu extends to two sides0Where S is a preset minimum length, in this embodiment, S is 17,is the real-time curvature correction factor at point O,represents the radius of curvature of the O point,representing the mean radius of curvature of the O points obtained from the window function, the real-time curvature correction factor mu0For automatically correcting the extension length according to the curvature difference of different points, the part with large curvature needs to be shorter, and the part with small curvature needs to beThe length of the first and second groups is larger, so that the distortion phenomenon after combination can be effectively reduced; respectively calculating the number N +1 and the number N-1 of the different points in the two side areas, wherein if the number of the different points is less than the set minimum number of the different points of the type, the area is the same as the preset type, otherwise, the area is opposite to the preset type; then two stopping points O+1And point O-1Restart the calculation as the starting point, extend outward by S × mu0+1Or S × mu0-1Is stopped at a time of mu0+1And mu0-1Respectively represent point O+1And point O-1Real-time curvature correction coefficient of (d), O+1The number of the different points in the two side areas is N+2，O-1The number of the different points in the two side areas is N-2According to the judging conditions, sequentially determining the profile types of all the sections, calculating the number of different points of the part with the length less than S according to the ratio of the part with the length less than S, and counting the number into the corresponding characteristic area; and combining adjacent regions of the same type to obtain continuous characteristic regions and non-characteristic regions.
The filtering module adopts wiener filtering to carry out primary filtering because the multiplicative noise is related to the image signal and changes along with the change of the image signal, at the moment, the image information also contains residual multiplicative noise, and the F filter F (x, y) is q × exp- (x, y)2+y2)/β2Two-stage filtering is performed, where q is a coefficient that normalizes the function, i.e., (x ^ q × exp (- (x)2+y2)/β2) dxdy ═ 1, β are image template parameters;
after multiplicative noise filtering, the arc length parameterized equation of the noisy target contour is expressed as GN(t)’＝G(t)+N1(t); the additive noise is assumed to be white gaussian noise: x is the number ofN(t)’＝x(t)+g1(t,σ2)，yN(t)’＝y(t)+g2(t,σ2) Wherein x isN(t)' and yN(t)' represents coordinates of each point on the noisy contour after removing the multiplicative noise, g1(t,σ2) And g2(t,σ2) Respectively mean value of zero and variance of sigma2The Gaussian white noise is used for simulating additive noise in the noise-containing target contour;
using functionsSmoothing the noise-containing contour, named as K filter, and classifying contour points and dividing regions to obtain noise-containing contour GN(t)' is expressed as a combination of different types of contour segments:whereinRepresenting the segmentation of the contour that contains the region of the feature,representing contour segment containing non-feature region, selecting parameters of K filter according to contour feature distribution, and considering global feature and local feature factors, in the feature region, in order to retain detail informationIn the non-characteristic region, the effect of suppressing noise is focused onWhere σ' is the global variance, σ, estimated a priori1Estimating the variance, σ, a priori for selected feature regions0The variance is estimated a priori for the selected non-characteristic regions,the real-time curvature correction factor is averaged for the selected feature region,a real-time curvature correction factor for the mean of the selected non-characteristic region; in order to achieve better smoothing effect, half of the minimum length S of each type of area is selectedAs the length of the K-filter 85% confidence interval, to adapt the K-filter for different parameters according to the length of the two types of regions.
In this embodiment, S is 17, threshold T10.24, window function width D ∈ {7, 9}, has better smooth effect to the noisy image of noise intensity I ∈ {10dB, 20dB }, and this view molding detection instrument can discern the view profile, can effectively filter the target profile noise in the identification process for stylist's view molding efficiency has improved 40%, has alleviateed stylist's the amount of labour greatly.
Example 2: a landscape modeling detection tool for rapid detection comprises a common landscape modeling detection tool and a recognition device arranged on the landscape modeling detection tool, wherein the landscape modeling detection tool can recognize a landscape outline and is characterized by comprising a modeling module, a segmentation module, a combination module and a filtering module;
the modeling module is used for establishing a parameterized equation of the target contour: for a given target contour g (t), the arc length parameterization equation is expressed as g (t) ═ (x (t), y (t)), where x (t) and y (t) respectively represent the coordinates of the contour points, t represents the parameters of the contour curve equation, and t e [0,1 ];
the parametric equation of arc length of the noisy profile is expressed as GN(t)＝G(t)+N1(t)+N2(t) G (t), wherein the additive noise part N1(t)＝N1(x1(t),y1(t)), multiplicative noise part N2(t)＝N2(x2(t),y2(t))；
A segmentation module for segmenting the contour: target profile G (t) and noisy profile GN(t) the curvatures correspond to k (t) and k, respectivelyN(t); due to the influence of noise, the noisy contour GN(t) curvature value k of upper part feature pointN(t) cannot accurately represent the contour information, and in order to obtain an accurate curvature, a window function W (n) having a width D ∈ {10, 12} is selected, and a curvature k is calculatedN(t) performing neighborhood averaging to obtain an average curvature k1N(t) simultaneously ordering the curvature values within the window, selecting a median curvature k2N(t) average curvature k1N(t) and median curvature k2N(T) the absolute value of the difference with a selected threshold T1Comparing the two values to 0.24, and determining the curvature k 'of the noisy contour according to the comparison result'N(t), namely:
when | k1N(t)-k2N(t)|>T1K'N(t)＝k1N(t)
Otherwise, k'N(t)＝k2N(t)；
Since contour points with larger curvature values generally reflect the salient features of the target in terms of k'N(T) dividing all contour points in the contour into feature points or non-feature points, and setting variable weight TKSelf-adaptive decision T by judging the amount of the target contour featuresKWhen k'N(t)|<TK*max|k′N(t) |, the characteristic function f (t) is 0
Otherwise, the feature function f (t) is 1;
the distribution of the characteristic points and the non-characteristic points obtained after classification is not continuous, and a filter cannot be selected to carry out effective contour smoothing on the characteristic points and the non-characteristic points. In order to obtain a good contour smoothing effect, it is necessary to perform merging processing on contour points of the same type.
A merging module for eliminating the false characteristic points generated by noise interference and merging the characteristic points and non-characteristic points which can not form continuous areas to obtain effective characteristic areas and non-characteristic areas, wherein a starting point O is selected, the outline starting points extend to two sides to merge adjacent points, the starting point type is used as the preset type of the area, and each S × mu extends to two sides0A stop, where S is a preset minimum length, in this embodiment S19,is a point OThe real-time curvature correction coefficient of (a),represents the radius of curvature of the O point,representing the mean radius of curvature of the O points obtained from the window function, the real-time curvature correction factor mu0The method is used for automatically correcting the extension length according to different curvatures of different points, wherein the part with large curvature needs smaller length, and the part with small curvature needs larger length, so that the distortion phenomenon after combination can be effectively reduced; respectively calculating the number N +1 and the number N-1 of the different points in the two side areas, wherein if the number of the different points is less than the set minimum number of the different points of the type, the area is the same as the preset type, otherwise, the area is opposite to the preset type; then two stopping points O+1And point O-1Restart the calculation as the starting point, extend outward by S × mu0+1Or S × mu0-1Is stopped at a time of mu0+1And mu0-1Respectively represent point O+1And point O-1Real-time curvature correction coefficient of (d), O+1The number of the different points in the two side areas is N+2，O-1The number of the different points in the two side areas is N-2According to the judging conditions, sequentially determining the profile types of all the sections, calculating the number of different points of the part with the length less than S according to the ratio of the part with the length less than S, and counting the number into the corresponding characteristic area; and combining adjacent regions of the same type to obtain continuous characteristic regions and non-characteristic regions.
The filtering module adopts wiener filtering to carry out primary filtering because the multiplicative noise is related to the image signal and changes along with the change of the image signal, at the moment, the image information also contains residual multiplicative noise, and the F filter F (x, y) is q × exp- (x, y)2+y2)/β2Two-stage filtering is performed, where q is a coefficient that normalizes the function, i.e., (x ^ q × exp (- (x)2+y2)/β2) dxdy ═ 1, β are image template parameters;
after multiplicative noise filtering, the arc length parameterized equation of the noisy target contour is expressed as GN(t)’＝G(t)+N1(t); the additive noise is assumed to be white gaussian noise: x is the number ofN(t)’＝x(t)+g1(t,σ2)，yN(t)’＝y(t)+g2(t,σ2) Wherein x isN(t)' and yN(t)' represents coordinates of each point on the noisy contour after removing the multiplicative noise, g1(t,σ2) And g2(t,σ2) Respectively mean value of zero and variance of sigma2The Gaussian white noise is used for simulating additive noise in the noise-containing target contour;
using functionsSmoothing the noise-containing contour, named as K filter, and classifying contour points and dividing regions to obtain noise-containing contour GN(t)' is expressed as a combination of different types of contour segments:whereinRepresenting the segmentation of the contour that contains the region of the feature,representing contour segment containing non-feature region, selecting parameters of K filter according to contour feature distribution, and considering global feature and local feature factors, in the feature region, in order to retain detail informationIn the non-characteristic region, the effect of suppressing noise is focused onWhere σ' is the global variance, σ, estimated a priori1For selected featuresA priori estimated variance, σ, of the region0The variance is estimated a priori for the selected non-characteristic regions,the real-time curvature correction factor is averaged for the selected feature region,a real-time curvature correction factor for the mean of the selected non-characteristic region; in order to achieve a good smoothing effect, half of the minimum length S of each type of region is selected as the length of the 85% confidence interval of the K filter, so that the K filters with different parameters are self-adapted according to the lengths of the two types of regions.
In this embodiment, S is 19, threshold T10.24, window function width D ∈ {10, 12}, has better smooth effect to the noisy image of noise intensity I ∈ {20dB, 30dB }, and this view molding detection instrument can discern the view profile, can effectively filter the target profile noise in the identification process for stylist's view molding efficiency has improved 40%, has alleviateed stylist's the amount of labour greatly.
Example 3: a landscape modeling detection tool for rapid detection comprises a common landscape modeling detection tool and a recognition device arranged on the landscape modeling detection tool, wherein the landscape modeling detection tool can recognize a landscape outline and is characterized by comprising a modeling module, a segmentation module, a combination module and a filtering module;
the modeling module is used for establishing a parameterized equation of the target contour: for a given target contour g (t), the arc length parameterization equation is expressed as g (t) ═ (x (t), y (t)), where x (t) and y (t) respectively represent the coordinates of the contour points, t represents the parameters of the contour curve equation, and t e [0,1 ];
the parametric equation of arc length of the noisy profile is expressed as GN(t)＝G(t)+N1(t)+N2(t) G (t), wherein the additive noise part N1(t)＝N1(x1(t),y1(t)), multiplicative noise part N2(t)＝N2(x2(t),y2(t))；
A segmentation module for segmenting the contour: target profile G (t) and noisy profile GN(t) the curvatures correspond to k (t) and k, respectivelyN(t); due to the influence of noise, the noisy contour GN(t) curvature value k of upper part feature pointN(t) cannot accurately represent the contour information, and in order to obtain an accurate curvature, a window function W (n) having a width D ∈ {13, 14} is selected, and a curvature k is calculatedN(t) performing neighborhood averaging to obtain an average curvature k1N(t) simultaneously ordering the curvature values within the window, selecting a median curvature k2N(t) average curvature k1N(t) and median curvature k2N(T) the absolute value of the difference with a selected threshold T1Comparing the two parameters to 0.26, and determining the curvature k 'of the profile containing noise according to the comparison result'N(t), namely:
when | k1N(t)-k2N(t)|>T1K'N(t)＝k1N(t)
Otherwise, k'N(t)＝k2N(t)；
Since contour points with larger curvature values generally reflect the salient features of the target in terms of k'N(T) dividing all contour points in the contour into feature points or non-feature points, and setting variable weight TKSelf-adaptive decision T by judging the amount of the target contour featuresKWhen k'N(t)|<TK*max|k′N(t) |, the characteristic function f (t) is 0
Otherwise, the feature function f (t) is 1;
the distribution of the characteristic points and the non-characteristic points obtained after classification is not continuous, and a filter cannot be selected to carry out effective contour smoothing on the characteristic points and the non-characteristic points. In order to obtain a good contour smoothing effect, it is necessary to perform merging processing on contour points of the same type.
Merging moduleSelecting a starting point O, extending the outline starting point to two sides, merging the adjacent points, taking the starting point type as the preset type of the area, and extending each S × mu to two sides0A time stop, where S is a preset minimum length, in this embodiment S21,is the real-time curvature correction factor at point O,represents the radius of curvature of the O point,representing the mean radius of curvature of the O points obtained from the window function, the real-time curvature correction factor mu0The method is used for automatically correcting the extension length according to different curvatures of different points, wherein the part with large curvature needs smaller length, and the part with small curvature needs larger length, so that the distortion phenomenon after combination can be effectively reduced; respectively calculating the number N +1 and the number N-1 of the different points in the two side areas, wherein if the number of the different points is less than the set minimum number of the different points of the type, the area is the same as the preset type, otherwise, the area is opposite to the preset type; then two stopping points O+1And point O-1Restart the calculation as the starting point, extend outward by S × mu0+1Or S × mu0-1Is stopped at a time of mu0+1And mu0-1Respectively represent point O+1And point O-1Real-time curvature correction coefficient of (d), O+1The number of the different points in the two side areas is N+2，O-1The number of the different points in the two side areas is N-2According to the judging conditions, sequentially determining the profile types of all the sections, calculating the number of different points of the part with the length less than S according to the ratio of the part with the length less than S, and counting the number into the corresponding characteristic area; adjacent areas of the same type are combined to obtain continuousA feature region and a non-feature region.
The filtering module adopts wiener filtering to carry out primary filtering because the multiplicative noise is related to the image signal and changes along with the change of the image signal, at the moment, the image information also contains residual multiplicative noise, and the F filter F (x, y) is q × exp- (x, y)2+y2)/β2Two-stage filtering is performed, where q is a coefficient that normalizes the function, i.e., (x ^ q × exp (- (x)2+y2)/β2) dxdy ═ 1, β are image template parameters;
after multiplicative noise filtering, the arc length parameterized equation of the noisy target contour is expressed as GN(t)’＝G(t)+N1(t); the additive noise is assumed to be white gaussian noise: x is the number ofN(t)’＝x(t)+g1(t,σ2)，yN(t)’＝y(t)+g2(t,σ2) Wherein x isN(t)' and yN(t)' represents coordinates of each point on the noisy contour after removing the multiplicative noise, g1(t,σ2) And g2(t,σ2) Respectively mean value of zero and variance of sigma2The Gaussian white noise is used for simulating additive noise in the noise-containing target contour;
using functionsSmoothing the noise-containing contour, named as K filter, and classifying contour points and dividing regions to obtain noise-containing contour GN(t)' is expressed as a combination of different types of contour segments:whereinRepresenting the segmentation of the contour that contains the region of the feature,representing wheels containing non-characteristic regionsContour segmentation, selecting parameters of K filter according to contour feature distribution, simultaneously considering global feature and local feature factors, and in the feature region, making order to retain detail informationIn the non-characteristic region, the effect of suppressing noise is focused onWhere σ' is the global variance, σ, estimated a priori1Estimating the variance, σ, a priori for selected feature regions0The variance is estimated a priori for the selected non-characteristic regions,the real-time curvature correction factor is averaged for the selected feature region,a real-time curvature correction factor for the mean of the selected non-characteristic region; in order to achieve a good smoothing effect, half of the minimum length S of each type of region is selected as the length of the 85% confidence interval of the K filter, so that the K filters with different parameters are self-adapted according to the lengths of the two types of regions.
In this embodiment, S is 21, threshold T1The landscape modeling detection tool has the advantages that the noise-containing image with the noise intensity I ∈ {30dB, 40dB } is well smoothed by 0.26 and the window function width D ∈ {13, 14}, the calculated amount and the detail information retention condition are within an acceptable interval and well balanced, the landscape modeling detection tool can identify the landscape outline, the target outline noise can be effectively filtered in the identification process, the landscape modeling efficiency of a modeler is improved by 45%, and the labor amount of the modeler is greatly reduced.
Example 4: a landscape modeling detection tool for rapid detection comprises a common landscape modeling detection tool and a recognition device arranged on the landscape modeling detection tool, wherein the landscape modeling detection tool can recognize a landscape outline and is characterized by comprising a modeling module, a segmentation module, a combination module and a filtering module;
the modeling module is used for establishing a parameterized equation of the target contour: for a given target contour g (t), the arc length parameterization equation is expressed as g (t) ═ (x (t), y (t)), where x (t) and y (t) respectively represent the coordinates of the contour points, t represents the parameters of the contour curve equation, and t e [0,1 ];
the parametric equation of arc length of the noisy profile is expressed as GN(t)＝G(t)+N1(t)+N2(t) G (t), wherein the additive noise part N1(t)＝N1(x1(t),y1(t)), multiplicative noise part N2(t)＝N2(x2(t),y2(t))；
A segmentation module for segmenting the contour: target profile G (t) and noisy profile GN(t) the curvatures correspond to k (t) and k, respectivelyN(t); due to the influence of noise, the noisy contour GN(t) curvature value k of upper part feature pointN(t) cannot accurately represent the contour information, and in order to obtain an accurate curvature, a window function W (n) having a width D ∈ {15, 17} is selected, and a curvature k is calculatedN(t) performing neighborhood averaging to obtain an average curvature k1N(t) simultaneously ordering the curvature values within the window, selecting a median curvature k2N(t) average curvature k1N(t) and median curvature k2N(T) the absolute value of the difference with a selected threshold T1Comparing the two values to 0.28, and determining the curvature k 'of the noisy contour according to the comparison result'N(t), namely:
when | k1N(t)-k2N(t)|>T1K'N(t)＝k1N(t)
Otherwise, k'N(t)＝k2N(t)；
Since contour points with larger curvature values generally reflect the salient features of the target in terms of k'N(t) dividing all contour points in the contourSetting variable weight T for characteristic point or non-characteristic pointKSelf-adaptive decision T by judging the amount of the target contour featuresKWhen k'N(t)|<TK*max|k′N(t) |, the characteristic function f (t) is 0
Otherwise, the feature function f (t) is 1;
the distribution of the characteristic points and the non-characteristic points obtained after classification is not continuous, and a filter cannot be selected to carry out effective contour smoothing on the characteristic points and the non-characteristic points. In order to obtain a good contour smoothing effect, it is necessary to perform merging processing on contour points of the same type.
A merging module for eliminating the false characteristic points generated by noise interference and merging the characteristic points and non-characteristic points which can not form continuous areas to obtain effective characteristic areas and non-characteristic areas, wherein a starting point O is selected, the outline starting points extend to two sides to merge adjacent points, the starting point type is used as the preset type of the area, and each S × mu extends to two sides0A stop, where S is a preset minimum length, in this embodiment S23,is the real-time curvature correction factor at point O,represents the radius of curvature of the O point,representing the mean radius of curvature of the O points obtained from the window function, the real-time curvature correction factor mu0The method is used for automatically correcting the extension length according to different curvatures of different points, wherein the part with large curvature needs smaller length, and the part with small curvature needs larger length, so that the distortion phenomenon after combination can be effectively reduced; respectively calculating the number N +1 and N-1 of the different points in the two side regions, if the number of the different points is less than the set minimum number of the different points of the type, determining that the region and the preset type are differentThe same, otherwise, the preset type is opposite; then two stopping points O+1And point O-1Restart the calculation as the starting point, extend outward by S × mu0+1Or S × mu0-1Is stopped at a time of mu0+1And mu0-1Respectively represent point O+1And point O-1Real-time curvature correction coefficient of (d), O+1The number of the different points in the two side areas is N+2，O-1The number of the different points in the two side areas is N-2According to the judging conditions, sequentially determining the profile types of all the sections, calculating the number of different points of the part with the length less than S according to the ratio of the part with the length less than S, and counting the number into the corresponding characteristic area; and combining adjacent regions of the same type to obtain continuous characteristic regions and non-characteristic regions.
The filtering module adopts wiener filtering to carry out primary filtering because the multiplicative noise is related to the image signal and changes along with the change of the image signal, at the moment, the image information also contains residual multiplicative noise, and the F filter F (x, y) is q × exp- (x, y)2+y2)/β2Two-stage filtering is performed, where q is a coefficient that normalizes the function, i.e., (x ^ q × exp (- (x)2+y2)/β2) dxdy ═ 1, β are image template parameters;
after multiplicative noise filtering, the arc length parameterized equation of the noisy target contour is expressed as GN(t)’＝G(t)+N1(t); the additive noise is assumed to be white gaussian noise: x is the number ofN(t)’＝x(t)+g1(t,σ2)，yN(t)’＝y(t)+g2(t,σ2) Wherein x isN(t)' and yN(t)' represents coordinates of each point on the noisy contour after removing the multiplicative noise, g1(t,σ2) And g2(t,σ2) Respectively mean value of zero and variance of sigma2The Gaussian white noise is used for simulating additive noise in the noise-containing target contour;
using functionsTo noise-containing wheelSmoothing the contour, named K filter, classifying contour points and dividing regions, and generating noise contour GN(t)' is expressed as a combination of different types of contour segments:whereinRepresenting the segmentation of the contour that contains the region of the feature,representing contour segment containing non-feature region, selecting parameters of K filter according to contour feature distribution, and considering global feature and local feature factors, in the feature region, in order to retain detail informationIn the non-characteristic region, the effect of suppressing noise is focused onWhere σ' is the global variance, σ, estimated a priori1Estimating the variance, σ, a priori for selected feature regions0The variance is estimated a priori for the selected non-characteristic regions,the real-time curvature correction factor is averaged for the selected feature region,a real-time curvature correction factor for the mean of the selected non-characteristic region; in order to achieve a good smoothing effect, half of the minimum length S of each type of region is selected as the length of the 85% confidence interval of the K filter, so that the K filters with different parameters are self-adapted according to the lengths of the two types of regions.
In this embodiment, S-23, threshold T10.28, window function width D ∈ {15, 17}, although increasing some calculated amount to the noisy image of noise intensity I ∈ {40dB, 50dB }, but the image of this interval has excellent smoothing effect, and the detailed information retention condition is better, this view style detection instrument can discern the view profile, can effectively filter the target profile noise in the identification process, make the view style efficiency of stylist improve 45%, has alleviateed stylist's amount of labour greatly.
Example 5: a landscape modeling detection tool for rapid detection comprises a common landscape modeling detection tool and a recognition device arranged on the landscape modeling detection tool, wherein the landscape modeling detection tool can recognize a landscape outline and is characterized by comprising a modeling module, a segmentation module, a combination module and a filtering module;
the modeling module is used for establishing a parameterized equation of the target contour: for a given target contour g (t), the arc length parameterization equation is expressed as g (t) ═ (x (t), y (t)), where x (t) and y (t) respectively represent the coordinates of the contour points, t represents the parameters of the contour curve equation, and t e [0,1 ];
the parametric equation of arc length of the noisy profile is expressed as GN(t)＝G(t)+N1(t)+N2(t) G (t), wherein the additive noise part N1(t)＝N1(x1(t),y1(t)), multiplicative noise part N2(t)＝N2(x2(t),y2(t))；
A segmentation module for segmenting the contour: target profile G (t) and noisy profile GN(t) the curvatures correspond to k (t) and k, respectivelyN(t); due to the influence of noise, the noisy contour GN(t) curvature value k of upper part feature pointN(t) cannot accurately represent the contour information, and in order to obtain an accurate curvature, a window function W (n) having a width D ∈ {17, 19} is selected, and a curvature k is calculatedN(t) performing neighborhood averaging to obtain an average curvature k1N(t) simultaneously ordering the curvature values within the window, selecting the median curvaturek2N(t) average curvature k1N(t) and median curvature k2N(T) the absolute value of the difference with a selected threshold T1Comparing the two parameters to 0.26, and determining the curvature k 'of the profile containing noise according to the comparison result'N(t), namely:
when | k1N(t)-k2N(t)|>T1K'N(t)＝k1N(t)
Otherwise, k'N(t)＝k2N(t)；
Since contour points with larger curvature values generally reflect the salient features of the target in terms of k'N(T) dividing all contour points in the contour into feature points or non-feature points, and setting variable weight TKSelf-adaptive decision T by judging the amount of the target contour featuresKWhen k'N(t)|<TK*max|k′N(t) |, the characteristic function f (t) is 0
Otherwise, the feature function f (t) is 1;
the distribution of the characteristic points and the non-characteristic points obtained after classification is not continuous, and a filter cannot be selected to carry out effective contour smoothing on the characteristic points and the non-characteristic points. In order to obtain a good contour smoothing effect, it is necessary to perform merging processing on contour points of the same type.
A merging module for eliminating the false characteristic points generated by noise interference and merging the characteristic points and non-characteristic points which can not form continuous areas to obtain effective characteristic areas and non-characteristic areas, wherein a starting point O is selected, the outline starting points extend to two sides to merge adjacent points, the starting point type is used as the preset type of the area, and each S × mu extends to two sides0A stop, where S is a preset minimum length, in this embodiment 25,is the real-time curvature correction factor at point O,represents the radius of curvature of the O point,representing the mean radius of curvature of the O points obtained from the window function, the real-time curvature correction factor mu0The method is used for automatically correcting the extension length according to different curvatures of different points, wherein the part with large curvature needs smaller length, and the part with small curvature needs larger length, so that the distortion phenomenon after combination can be effectively reduced; respectively calculating the number N +1 and the number N-1 of the different points in the two side areas, wherein if the number of the different points is less than the set minimum number of the different points of the type, the area is the same as the preset type, otherwise, the area is opposite to the preset type; then two stopping points O+1And point O-1Restart the calculation as the starting point, extend outward by S × mu0+1Or S × mu0-1Is stopped at a time of mu0+1And mu0-1Respectively represent point O+1And point O-1Real-time curvature correction coefficient of (d), O+1The number of the different points in the two side areas is N+2，O-1The number of the different points in the two side areas is N-2According to the judging conditions, sequentially determining the profile types of all the sections, calculating the number of different points of the part with the length less than S according to the ratio of the part with the length less than S, and counting the number into the corresponding characteristic area; and combining adjacent regions of the same type to obtain continuous characteristic regions and non-characteristic regions.
The filtering module adopts wiener filtering to carry out primary filtering because the multiplicative noise is related to the image signal and changes along with the change of the image signal, at the moment, the image information also contains residual multiplicative noise, and the F filter F (x, y) is q × exp- (x, y)2+y2)/β2Two-stage filtering is performed, where q is a coefficient that normalizes the function, i.e., (x ^ q × exp (- (x)2+y2)/β2) dxdy ═ 1, β are image template parameters;
after multiplicative noise filtering, the arc length parameterized equation of the noisy target contour is expressed as GN(t)’＝G(t)+N1(t); suppose thatAdditive noise is white gaussian noise: x is the number ofN(t)’＝x(t)+g1(t,σ2)，yN(t)’＝y(t)+g2(t,σ2) Wherein x isN(t)' and yN(t)' represents coordinates of each point on the noisy contour after removing the multiplicative noise, g1(t,σ2) And g2(t,σ2) Respectively mean value of zero and variance of sigma2The Gaussian white noise is used for simulating additive noise in the noise-containing target contour;
using functionsSmoothing the noise-containing contour, named as K filter, and classifying contour points and dividing regions to obtain noise-containing contour GN(t)' is expressed as a combination of different types of contour segments:whereinRepresenting the segmentation of the contour that contains the region of the feature,representing contour segment containing non-feature region, selecting parameters of K filter according to contour feature distribution, and considering global feature and local feature factors, in the feature region, in order to retain detail informationIn the non-characteristic region, the effect of suppressing noise is focused onWhere σ' is the global variance, σ, estimated a priori1Estimating the variance, σ, a priori for selected feature regions0The variance is estimated a priori for the selected non-characteristic regions,the real-time curvature correction factor is averaged for the selected feature region,a real-time curvature correction factor for the mean of the selected non-characteristic region; in order to achieve a good smoothing effect, half of the minimum length S of each type of region is selected as the length of the 85% confidence interval of the K filter, so that the K filters with different parameters are self-adapted according to the lengths of the two types of regions.
In this embodiment, S is 25, threshold T10.26, window function width D ∈ {17, 19}, has the smooth effect of preferred to the noisy image of noise intensity I ∈ {50dB, 60dB }, and the detail information retention condition is better, and this view molding detection instrument can discern the view profile, can effectively filter the target profile noise in the identification process for the view molding efficiency of stylist has improved 50%, has alleviateed stylist's amount of labour greatly.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Data emulation
This view molding detects instrument's beneficial effect does: aiming at the diversity of noise types and the singleness of the current denoising method, a novel multi-time filtering device is adopted, and a novel contour segmentation and combination means and a filtering function are provided; the calculated amount is relatively uncomplicated, the factors of global characteristics and local characteristics are considered, and the smooth denoising effect is good; the difference of the contour among different types of regions is considered, and good balance is achieved between noise suppression and detail retention; according to the different curvatures of different points, the extension length is correspondingly and automatically changed in an adaptive manner, and the distortion phenomenon after combination is effectively reduced.
Through simulation, the device is compared with other devices under the noise intensity N, and the recognition rate of the target is as follows:
Claims (2)
1. A landscape modeling detection tool for rapid detection comprises a common landscape modeling detection tool and a recognition device arranged on the landscape modeling detection tool, wherein the landscape modeling detection tool can recognize a landscape outline and is characterized by comprising a modeling module, a segmentation module, a combination module and a filtering module; wherein,
the modeling module is used for establishing a parameterized equation of the target contour: for a given target contour g (t), the arc length parameterization equation is expressed as g (t) ═ (x (t), y (t)), where x (t) and y (t) respectively represent the coordinates of the contour points, t represents the parameters of the contour curve equation, and t e [0,1 ];
the parametric equation for the arc length of the noisy profile is expressed as: gN(t)＝G(t)+N1(t)+N2(t) G (t), wherein the additive noise part N1(t)＝N1(x1(t),y1(t)), multiplicative noise part N2(t)＝N2(x2(t),y2(t))；
A segmentation module for segmenting the contour: target profile G (t) and noisy profile GN(t) the curvatures correspond to k (t) and k, respectivelyN(t) selecting a window function W (n) of width D, D ∈ {7, 9}, for curvature kN(t) performing neighborhood averaging to obtain an average curvature k1N(t) simultaneously ordering the curvature values within the window, selecting a median curvature k2N(t) average curvature k1N(t) and median curvature k2N(T) the absolute value of the difference with a selected threshold T1Comparing, and determining the curvature k 'of the noisy contour according to the comparison result'N(t)，T10.2, i.e.:
when | k1N(t)-k2N(t)|>T1K'N(t)＝k1N(t)
Otherwise, k'N(t)＝k2N(t)；
Since contour points with larger curvature values generally reflect the salient features of the target in terms of k'N(T) dividing all contour points in the contour into feature points or non-feature points, and setting variable weight TKSelf-adaptive decision T by judging the amount of the target contour featuresKWhen k'N(t)|<TK*max|k′N(t) |, the characteristic function f (t) is 0
Otherwise, the feature function f (t) is 1.
2. The landscape architecture modeling detection tool of claim 1, further characterized by a consolidation module: the method is used for eliminating the pseudo feature points generated by noise interference and carrying out merging operation on the feature points and the non-feature points which cannot form a continuous area, so that an effective feature area and a non-feature area are obtained: selecting a starting point O, contourExtending the starting point to two sides and merging the adjacent points, taking the starting point type as the preset type of the region, and extending each S × mu to two sides0Stopping, wherein S is a preset minimum length, S is 15,is the real-time curvature correction factor at point O,represents the radius of curvature of the O point,representing the mean radius of curvature of the O points obtained from the window function, the real-time curvature correction factor mu0The method is used for automatically correcting the extension length according to different curvatures of different points, and can effectively reduce the distortion phenomenon after combination; respectively calculating the number N +1 and the number N-1 of the different points in the two side areas, wherein if the number of the different points is less than the set minimum number of the different points of the type, the area is the same as the preset type, otherwise, the area is opposite to the preset type; then two stopping points O+1And point O-1Restart the calculation as the starting point, extend outward by S × mu0+1Or S × mu0-1Is stopped at a time of mu0+1And mu0-1Respectively represent point O+1And point O-1Real-time curvature correction coefficient of (d), O+1The number of the different points in the two side areas is N+2，O-1The number of the different points in the two side areas is N-2According to the judging conditions, sequentially determining the profile types of all the sections, calculating the number of different points of the part with the length less than S according to the ratio of the part with the length less than S, and counting the number into the corresponding characteristic area; adjacent areas of the same type are combined to obtain a continuous characteristic area and a non-characteristic area;
the filtering module adopts wiener filtering to carry out primary filtering because the multiplicative noise is related to the image signal and changes along with the change of the image signal, at the moment, the image information also contains residual multiplicative noise, and the F filter F (x, y) is q × exp- (x, y)2+y2)/β2Two-stage filtering is performed, where q is a coefficient that normalizes the function, i.e., (x ^ q × exp (- (x)2+y2)/β2) dxdy ═ 1, β are image template parameters;
after multiplicative noise filtering, the arc length parameterized equation of the noisy target contour is expressed as GN(t)’＝G(t)+N1(t); the additive noise is assumed to be white gaussian noise: x is the number ofN(t)’＝x(t)+g1(t,σ2)，yN(t)’＝y(t)+g2(t,σ2) Wherein x isN(t)' and yN(t)' represents coordinates of each point on the noisy contour after removing the multiplicative noise, g1(t,σ2) And g2(t,σ2) Respectively mean value of zero and variance of sigma2The Gaussian white noise is used for simulating additive noise in the noise-containing target contour;
using functionsSmoothing the noise-containing contour, named as K filter, and classifying contour points and dividing regions to obtain noise-containing contour GN(t)' is expressed as a combination of different types of contour segments:whereinRepresenting the segmentation of the contour that contains the region of the feature,representing contour segment containing non-feature region, selecting parameters of K filter according to contour feature distribution, and considering global feature and local feature factors, in the feature region, in order to retain detail informationIn the non-characteristic region, in order to raise the effect of suppressing noise Where σ' is the global variance, σ, estimated a priori1Estimating the variance, σ, a priori for selected feature regions0The variance is estimated a priori for the selected non-characteristic regions,the real-time curvature correction factor is averaged for the selected feature region,a real-time curvature correction factor for the mean of the selected non-characteristic region; in order to achieve a good smoothing effect, half of the minimum length S of each type of region is selected as the length of the 85% confidence interval of the K filter, so that the K filters with different parameters are self-adapted according to the lengths of the two types of regions.
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Application publication date: 20160330 |