CN105414774A - Laser cutting device capable of achieving autonomous cutting - Google Patents

Abstract

The invention discloses a laser cutting device capable of achieving autonomous cutting. The laser cutting device comprises a common laser cutting device and a target identification device installed on the laser cutting device. The identification device comprises a modeling module, a segmenting module, a combining module and a filtering module. The target identification module is additionally arranged on the laser cutting device, the self-adaptive cutting capacity of the laser cutting device can be effectively enhanced, the laser cutting device identifies a target through the contour of the target, target contour noise can be effectively filtered out in the identification process, and therefore the target can be cut according to the shape of the target.

Description

A kind of laser cutting device that can independently cut

Technical field

The present invention relates to laser cutting field, be specifically related to a kind of laser cutting device that can independently cut.

Background technology

Laser, owing to having concentrated high-energy in a narrow and small direction, therefore utilizes the laser beam after focusing on can cut various material.Owing to not having tooling cost, so laser cutting device is applicable to various component processing, laser cutting device is on the market expensive at present, but do not possess target recognition function, if can identify cut substrate, cutting process will greatly reduce the intervention of people, improves cutting efficiency.

Objective contour identification is as the important means of target identification, owing to being subject to the impact of the factor such as noise, quantization error in practical application, objective contour inevitably produces distortion, and in order to accurate description contour feature, the filtering process of objective contour is very necessary.At present, scholars propose the filtering algorithm of many noisy profiles, but ubiquity amount of calculation is huge, noise reduction is undesirable, the excessive filtering of easy generation causes the problems such as target distortion.

Summary of the invention

For the problems referred to above, the invention provides a kind of laser cutting device that can independently cut.

Object of the present invention realizes by the following technical solutions:

A kind of laser cutting device that can independently cut, comprise common laser cutter sweep and be arranged on the Target Identification Unit on laser cutting device, this laser cutting device has very strong self adaptation cutting power, Target Identification Unit can identify target according to objective contour, it is characterized in that, comprise 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 the Representation Equation is 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 the Representation Equation of noisy profile is: G _n(t)=G (t)+N ₁(t)+N ₂(t) G (t), wherein additive noise part N ₁(t)=N ₁(x ₁(t), y ₁(t)), multiplicative noise part N ₂(t)=N ₂(x ₂(t), y ₂(t));

Segmentation module, the segmentation for profile: objective contour G (t) and noisy profile G _nt the curvature corresponding to () is respectively k (t) and k _n(t); Width x width is selected to be window function W (n) of D, to curvature k _nt () carries out neighborhood averaging, obtain average curvature k _1N(t), simultaneously to the curvature value sequence in window, selected intermediate value curvature k _2Nt (), by average curvature k _1N(t) and intermediate value curvature k _2Nabsolute value and the selected threshold value T of (t) difference ₁compare, determine noisy contour curvature k according to comparative result ^' _n(t), that is:

When | k _1N(t)-k _2N(t) | >T ₁time, k ^' _n(t)=k _1N(t)

Otherwise, k ^' _n(t)=k _2N(t);

Because profile point that curvature value is larger reflects the notable feature of target, usually according to k ^' _nt profile point all in profile are divided into characteristic point or non-characteristic point by (), setting variable weight T _k, by judging that objective contour feature is how many, adaptive decision T _k, when | k ^' _n(t) | <T _k* 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 because noise jamming produces, and union operation is carried out to the characteristic point and non-characteristic point that cannot form continuum, thus obtain effective characteristic area and non-characteristic area: a selected starting point O, profile starting point extends the adjacent point of merging to both sides, using this starting point type as this region preset kind, extend each S × μ to both sides ₀in 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 μ ₀different for the curvature according to difference, auto modification development length, can effectively reduce the distortion phenomenon after merging; Calculate 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 of setting, then this region is identical with preset kind, otherwise, contrary with preset kind; Again with two halt O _{+ 1}with an O _-1restart to calculate as starting point, extend S × μ laterally _o+1or S × μ _o-1in time, stops, wherein μ _o+1and μ _o-1representative point O respectively _{+ 1}with an O _-1the real-time curvature correction factor at place, O _{+ 1}in 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 calculates dissimilarity number according to the ratio of itself and S, counts corresponding characteristic area; Adjacent region of the same type is merged, obtains continuous print characteristic area and non-characteristic area;

Filtration module: multiplicative noise is owing to being relevant with picture signal, change with the change of picture signal, adopt Wiener filtering to carry out first-level filtering and remove, now image information also includes remaining multiplicative noise, by F wave filter F (x, y)=q × exp (-(x ²+ y ²)/β ²carry out secondary filtering, wherein q is by the coefficient of function normalization, that is: ∫ ∫ q × exp (-(x ²+ y ²)/β ²) dxdy=1, β be image template parameter;

After multiplicative noise filtering, the arc length parameterized the Representation Equation of noisy objective contour is G _n(t) '=G (t)+N ₁(t); Suppose that additive noise is white Gaussian noise: x _n(t) '=x (t)+g ₁(t, σ ²), y _n(t) '=y (t)+g ₂(t, σ ²), wherein x _n(t) ' and y _nt () ' represents respectively and to remove after multiplicative noise each point coordinates on noisy profile, g ₁(t, σ ²) and g ₂(t, σ ²) be average be respectively zero, variance is σ ²white Gaussian noise, for simulating the additive noise in noisy objective contour;

Adopt function smoothing to noisy profile, called after K wave filter, through profile point classification and Region dividing, noisy profile G _nt () ' is expressed as the combination of dissimilar contour segmentation: wherein represent 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, order at non-characteristic area, in order to improve the effect of restraint speckle, order the wherein overall variance that obtains for priori estimation of σ ', σ ₁for the priori estimation variance of selected characteristic area, σ ₀for the priori estimation variance of selected non-characteristic area, for the average real-time curvature correction factor of selected characteristic area, for the average real-time curvature correction factor of selected non-characteristic area; In order to reach good smooth effect, choose the length of half as K wave filter 85% confidential interval of every type region minimum length S, thus the K wave filter of length self adaptation different parameters according to two class regions.

The present invention by installing Target Identification Unit additional on laser cutting device, effectively can strengthen the self adaptation cutting power of laser cutting device, laser cutting device, can effective filtering objective contour noise in identifying by objective contour identification target, thus cuts according to target shape.

Accompanying drawing explanation

The invention will be further described to utilize accompanying drawing, but the embodiment in accompanying drawing does not form any limitation of the invention, for those of ordinary skill in the art, under the prerequisite not paying creative work, can also obtain other accompanying drawing according to the following drawings.

Fig. 1 is the structured flowchart of the laser cutting device that can independently cut of the present invention.

Detailed description of the invention

The invention will be further described with the following Examples.

Fig. 1 is structured flowchart of the present invention, and it comprises: MBM, segmentation module, merging module, filtration module.

Embodiment 1: a kind of laser cutting device that can independently cut, comprise common laser cutter sweep and be arranged on the Target Identification Unit on laser cutting device, this laser cutting device has very strong self adaptation cutting power, Target Identification Unit can identify target according to objective contour, it is characterized in that, comprise 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 the Representation Equation is 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 the Representation Equation of noisy profile is: G _n(t)=G (t)+N ₁(t)+N ₂(t) G (t), wherein additive noise part N ₁(t)=N ₁(x ₁(t), y ₁(t)), multiplicative noise part N ₂(t)=N ₂(x ₂(t), y ₂(t));

Segmentation module, the segmentation for profile: objective contour G (t) and noisy profile G _nt the curvature corresponding to () is respectively k (t) and k _n(t); Owing to being subject to the impact of noise, noisy profile G _nthe curvature value k of (t) upper part characteristic point _nt () accurately can not represent profile information, in order to obtain curvature accurately, select width to be that { window function W (n) of 7,9}, to curvature k for D ∈ _nt () carries out neighborhood averaging, obtain average curvature k _1N(t), simultaneously to the curvature value sequence in window, selected intermediate value curvature k _2Nt (), by average curvature k _1N(t) and intermediate value curvature k _2Nabsolute value and the selected threshold value T of (t) difference ₁=0.24 compares, and determines noisy contour curvature k according to comparative result ^' _n(t), that is:

When | k1N (t)-k2N (t) | > T ₁time, k ^' _n(t)=k _1N(t)

Otherwise, k ^' _n(t)=k _2N(t);

Because profile point that curvature value is larger reflects the notable feature of target, usually according to k ^' _nt profile point all in profile are divided into characteristic point or non-characteristic point by (), setting variable weight T _k, by judging that objective contour feature is how many, adaptive decision T _k, when | k ^' _n(t) | <T _k* max|k ^' _n(t) | time, characteristic function f (t)=0 otherwise, characteristic function f (t)=1;

The distribution of the characteristic point that obtains and non-characteristic point after classification is also discontinuous, cannot carry out effective contour smoothing by selecting filter to it.In order to obtain good contour smoothing effect, be necessary to carry out merging treatment to profile point of the same type.

Merge module: for rejecting the pseudo-random numbers generation because noise jamming produces, and union operation is carried out to the characteristic point and non-characteristic point that cannot form continuum, thus obtain effective characteristic area and non-characteristic area: a selected starting point O, profile starting point extends the adjacent point of merging to both sides, using this starting point type as this region preset kind, extend each S × μ to both sides ₀in 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 μ ₀different for the curvature according to difference, auto modification development length, the length that the place that curvature is large needs is less, and the length that the place that curvature is little needs more greatly, can effectively reduce the distortion phenomenon after merging like this; Calculate 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 of setting, then this region is identical with preset kind, otherwise, contrary with preset kind; Again with two halt O _{+ 1}with an O _-1restart to calculate as starting point, extend S × μ laterally _o+1or S × μ _o-1in time, stops, wherein μ _o+1and μ _o-1representative point O respectively _{+ 1}with an O _-1the real-time curvature correction factor at place, O _{+ 1}in 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 calculates dissimilarity number according to the ratio of itself and S, counts corresponding characteristic area; Adjacent region of the same type is merged, obtains continuous print characteristic area and non-characteristic area.

Filtration module: multiplicative noise is owing to being relevant with picture signal, change with the change of picture signal, adopt Wiener filtering to carry out first-level filtering and remove, now image information also includes remaining multiplicative noise, by F wave filter F (x, y)=q × exp (-(x ²+ y ²)/β ²carry out secondary filtering, wherein q is by the coefficient of function normalization, that is: ∫ ∫ q × exp (-(x ²+ y ²)/β ²) dxdy=1, β be image template parameter;

After multiplicative noise filtering, the arc length parameterized the Representation Equation of noisy objective contour is G _n(t) '=G (t)+N ₁(t); Suppose that additive noise is white Gaussian noise: x _n(t) '=x (t)+g ₁(t, σ ²), y _n(t) '=y (t)+g ₂(t, σ ²), wherein x _n(t) ' and y _nt () ' represents respectively and to remove after multiplicative noise each point coordinates on noisy profile, g ₁(t, σ ²) and g ₂(t, σ ²) be average be respectively zero, variance is σ ²white Gaussian noise, for simulating the additive noise in noisy objective contour;

Adopt function smoothing to noisy profile, called after K wave filter, through profile point classification and Region dividing, noisy profile G _nt () ' is expressed as the combination of dissimilar contour segmentation: wherein represent 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, order at non-characteristic area, pay close attention to the effect of restraint speckle, order the wherein overall variance that obtains for priori estimation of σ ', σ ₁for the priori estimation variance of selected characteristic area, σ ₀for the priori estimation variance of selected non-characteristic area, for the average real-time curvature correction factor of selected characteristic area, for the average real-time curvature correction factor of selected non-characteristic area; In order to reach good smooth effect, choose the length of half as K wave filter 85% confidential interval of every type region minimum length S, thus the K wave filter of length self adaptation different parameters according to two class regions.

In this embodiment, S=17, threshold value T ₁=0.24, window function width D ∈ { 7,9}, to noise intensity I ∈, { the noisy image of 10dB, 20dB} has good smooth effect, and laser cutting device is by objective contour identification target, can effective filtering objective contour noise in identifying, this device obtains position and the shape of object by recognition result, cuts, greatly improve cutting efficiency according to instruction to object.

Embodiment 2: a kind of laser cutting device that can independently cut, comprise common laser cutter sweep and be arranged on the Target Identification Unit on laser cutting device, this laser cutting device has very strong self adaptation cutting power, Target Identification Unit can identify target according to objective contour, it is characterized in that, comprise 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 the Representation Equation is 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 the Representation Equation of noisy profile is: G _n(t)=G (t)+N ₁(t)+N ₂(t) G (t), wherein additive noise part N ₁(t)=N ₁(x ₁(t), y ₁(t)), multiplicative noise part N ₂(t)=N ₂(x ₂(t), y ₂(t));

Segmentation module, the segmentation for profile: objective contour G (t) and noisy profile G _nt the curvature corresponding to () is respectively k (t) and k _n(t); Owing to being subject to the impact of noise, noisy profile G _nthe curvature value k of (t) upper part characteristic point _nt () accurately can not represent profile information, in order to obtain curvature accurately, select width to be that { window function W (n) of 10,12}, to curvature k for D ∈ _nt () carries out neighborhood averaging, obtain average curvature k _1N(t), simultaneously to the curvature value sequence in window, selected intermediate value curvature k _2Nt (), by average curvature k _1N(t) and intermediate value curvature k _2Nabsolute value and the selected threshold value T of (t) difference ₁=0.24 compares, and determines noisy contour curvature k according to comparative result ^' _n(t), that is:

When | k _1N(t)-k _2N(t) | >T ₁time, k ^' _n(t)=k _1N(t)

Otherwise, k ^' _n(t)=k _2N(t);

Because profile point that curvature value is larger reflects the notable feature of target, usually according to k ^' _nt profile point all in profile are divided into characteristic point or non-characteristic point by (), setting variable weight T _k, by judging that objective contour feature is how many, adaptive decision T _k, when | k ^' _n(t) | <T _k* max|k ^' _n(t) | time, characteristic function f (t)=0 otherwise, characteristic function f (t)=1;

The distribution of the characteristic point that obtains and non-characteristic point after classification is also discontinuous, cannot carry out effective contour smoothing by selecting filter to it.In order to obtain good contour smoothing effect, be necessary to carry out merging treatment to profile point of the same type.

Merge module: for rejecting the pseudo-random numbers generation because noise jamming produces, and union operation is carried out to the characteristic point and non-characteristic point that cannot form continuum, thus obtain effective characteristic area and non-characteristic area: a selected starting point O, profile starting point extends the adjacent point of merging to both sides, using this starting point type as this region preset kind, extend each S × μ to both sides ₀in 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 μ ₀different for the curvature according to difference, auto modification development length, the length that the place that curvature is large needs is less, and the length that the place that curvature is little needs more greatly, can effectively reduce the distortion phenomenon after merging like this; Calculate 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 of setting, then this region is identical with preset kind, otherwise, contrary with preset kind; Again with two halt O _{+ 1}with an O _-1restart to calculate as starting point, extend S × μ laterally _o+1or S × μ _o-1in time, stops, wherein μ _o+1and μ _o-1representative point O respectively _{+ 1}with an O _-1the real-time curvature correction factor at place, O _{+ 1}in 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 calculates dissimilarity number according to the ratio of itself and S, counts corresponding characteristic area; Adjacent region of the same type is merged, obtains continuous print characteristic area and non-characteristic area.

Filtration module: multiplicative noise is owing to being relevant with picture signal, change with the change of picture signal, adopt Wiener filtering to carry out first-level filtering and remove, now image information also includes remaining multiplicative noise, by F wave filter F (x, y)=q × exp (-(x ²+ y ²)/β ²carry out secondary filtering, wherein q is by the coefficient of function normalization, that is: ∫ ∫ q × exp (-(x ²+ y ²)/β ²) dxdy=1, β be image template parameter;

After multiplicative noise filtering, the arc length parameterized the Representation Equation of noisy objective contour is G _n(t) '=G (t)+N ₁(t); Suppose that additive noise is white Gaussian noise: x _n(t) '=x (t)+g ₁(t, σ ²), y _n(t) '=y (t)+g ₂(t, σ ²), wherein x _n(t) ' and y _nt () ' represents respectively and to remove after multiplicative noise each point coordinates on noisy profile, g ₁(t, σ ²) and g ₂(t, σ ²) be average be respectively zero, variance is σ ²white Gaussian noise, for simulating the additive noise in noisy objective contour;

Adopt function smoothing to noisy profile, called after K wave filter, through profile point classification and Region dividing, noisy profile G _nt () ' is expressed as the combination of dissimilar contour segmentation: wherein represent 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, order at non-characteristic area, pay close attention to the effect of restraint speckle, order the wherein overall variance that obtains for priori estimation of σ ', σ ₁for the priori estimation variance of selected characteristic area, σ ₀for the priori estimation variance of selected non-characteristic area, for the average real-time curvature correction factor of selected characteristic area, for the average real-time curvature correction factor of selected non-characteristic area; In order to reach good smooth effect, choose the length of half as K wave filter 85% confidential interval of every type region minimum length S, thus the K wave filter of length self adaptation different parameters according to two class regions.

In this embodiment, S=19, threshold value T ₁=0.24, window function width D ∈ { 10,12}, to noise intensity I ∈, { the noisy image of 20dB, 30dB} has good smooth effect, and laser cutting device is by objective contour identification target, can effective filtering objective contour noise in identifying, this device obtains position and the shape of object by recognition result, cuts, greatly improve cutting efficiency according to instruction to object.

Embodiment 3: a kind of laser cutting device that can independently cut, comprise common laser cutter sweep and be arranged on the Target Identification Unit on laser cutting device, this laser cutting device has very strong self adaptation cutting power, Target Identification Unit can identify target according to objective contour, it is characterized in that, comprise 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 the Representation Equation is 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 the Representation Equation of noisy profile is: G _n(t)=G (t)+N ₁(t)+N ₂(t) G (t), wherein additive noise part N ₁(t)=N ₁(x ₁(t), y ₁(t)), multiplicative noise part N ₂(t)=N ₂(x ₂(t), y ₂(t));

Segmentation module, the segmentation for profile: objective contour G (t) and noisy profile G _nt the curvature corresponding to () is respectively k (t) and k _n(t); Owing to being subject to the impact of noise, noisy profile G _nthe curvature value k of (t) upper part characteristic point _nt () accurately can not represent profile information, in order to obtain curvature accurately, select width to be that { window function W (n) of 13,14}, to curvature k for D ∈ _nt () carries out neighborhood averaging, obtain average curvature k _1N(t), simultaneously to the curvature value sequence in window, selected intermediate value curvature k _2Nt (), by average curvature k _1N(t) and intermediate value curvature k _2Nabsolute value and the selected threshold value T of (t) difference ₁=0.26 compares, and determines noisy contour curvature k according to comparative result ^' _n(t), that is:

When | k _1N(t)-k _2N(t) | >T ₁time, k ^' _n(t)=k _1N(t)

Otherwise, k ^' _n(t)=k _2N(t);

Because profile point that curvature value is larger reflects the notable feature of target, usually according to k ^' _nt profile point all in profile are divided into characteristic point or non-characteristic point by (), setting variable weight T _k, by judging that objective contour feature is how many, adaptive decision T _k, when | k ^' _n(t) | <T _k* max|k ^' _n(t) | time, characteristic function f (t)=0 otherwise, characteristic function f (t)=1;

The distribution of the characteristic point that obtains and non-characteristic point after classification is also discontinuous, cannot carry out effective contour smoothing by selecting filter to it.In order to obtain good contour smoothing effect, be necessary to carry out merging treatment to profile point of the same type.

Merge module: for rejecting the pseudo-random numbers generation because noise jamming produces, and union operation is carried out to the characteristic point and non-characteristic point that cannot form continuum, thus obtain effective characteristic area and non-characteristic area: a selected starting point O, profile starting point extends the adjacent point of merging to both sides, using this starting point type as this region preset kind, extend each S × μ to both sides ₀in 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 μ ₀different for the curvature according to difference, auto modification development length, the length that the place that curvature is large needs is less, and the length that the place that curvature is little needs more greatly, can effectively reduce the distortion phenomenon after merging like this; Calculate 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 of setting, then this region is identical with preset kind, otherwise, contrary with preset kind; Again with two halt O _{+ 1}with an O _-1restart to calculate as starting point, extend S × μ laterally _o+1or S × μ _o-1in time, stops, wherein μ _o+1and μ _o-1representative point O respectively _{+ 1}with an O _-1the real-time curvature correction factor at place, O _{+ 1}in 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 calculates dissimilarity number according to the ratio of itself and S, counts corresponding characteristic area; Adjacent region of the same type is merged, obtains continuous print characteristic area and non-characteristic area.

Filtration module: multiplicative noise is owing to being relevant with picture signal, change with the change of picture signal, adopt Wiener filtering to carry out first-level filtering and remove, now image information also includes remaining multiplicative noise, by F wave filter F (x, y)=q × exp (-(x ²+ y ²)/β ²carry out secondary filtering, wherein q is by the coefficient of function normalization, that is: ∫ ∫ q × exp (-(x ²+ y ²)/β ²) dxdy=1, β be image template parameter;

After multiplicative noise filtering, the arc length parameterized the Representation Equation of noisy objective contour is G _n(t) '=G (t)+N ₁(t); Suppose that additive noise is white Gaussian noise: x _n(t) '=x (t)+g ₁(t, σ ²), y _n(t) '=y (t)+g ₂(t, σ ²), wherein x _n(t) ' and y _nt () ' represents respectively and to remove after multiplicative noise each point coordinates on noisy profile, g ₁(t, σ ²) and g ₂(t, σ ²) be average be respectively zero, variance is σ ²white Gaussian noise, for simulating the additive noise in noisy objective contour;

Adopt function smoothing to noisy profile, called after K wave filter, through profile point classification and Region dividing, noisy profile G _nt () ' is expressed as the combination of dissimilar contour segmentation: wherein represent 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, order at non-characteristic area, pay close attention to the effect of restraint speckle, order the wherein overall variance that obtains for priori estimation of σ ', σ ₁for the priori estimation variance of selected characteristic area, σ ₀for the priori estimation variance of selected non-characteristic area, for the average real-time curvature correction factor of selected characteristic area, for the average real-time curvature correction factor of selected non-characteristic area; In order to reach good smooth effect, choose the length of half as K wave filter 85% confidential interval of every type region minimum length S, thus the K wave filter of length self adaptation different parameters according to two class regions.

In this embodiment, S=21, threshold value T ₁=0.26, window function width D ∈ { 13,14}, to noise intensity I ∈, { the noisy image of 30dB, 40dB} has good smooth effect, amount of calculation and detailed information retain situation and all obtain and preferably balance in zone of acceptability, laser cutting device, can effective filtering objective contour noise in identifying by objective contour identification target, and this device obtains position and the shape of object by recognition result, according to instruction, object is cut, greatly improve cutting efficiency.

Embodiment 4: a kind of laser cutting device that can independently cut, comprise common laser cutter sweep and be arranged on the Target Identification Unit on laser cutting device, this laser cutting device has very strong self adaptation cutting power, Target Identification Unit can identify target according to objective contour, it is characterized in that, comprise 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 the Representation Equation is 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 the Representation Equation of noisy profile is: G _n(t)=G (t)+N ₁(t)+N ₂(t) G (t), wherein additive noise part N ₁(t)=N ₁(x ₁(t), y ₁(t)), multiplicative noise part N ₂(t)=N ₂(x ₂(t), y ₂(t));

Segmentation module, the segmentation for profile: objective contour G (t) and noisy profile G _nt the curvature corresponding to () is respectively k (t) and k _n(t); Owing to being subject to the impact of noise, noisy profile G _nthe curvature value k of (t) upper part characteristic point _nt () accurately can not represent profile information, in order to obtain curvature accurately, select width to be that { window function W (n) of 15,17}, to curvature k for D ∈ _nt () carries out neighborhood averaging, obtain average curvature k _1N(t), simultaneously to the curvature value sequence in window, selected intermediate value curvature k _2Nt (), by average curvature k _1N(t) and intermediate value curvature k _2Nabsolute value and the selected threshold value T of (t) difference ₁=0.28 compares, and determines noisy contour curvature k according to comparative result ^' _n(t), that is:

When | k _1N(t)-k _2N(t) | >T ₁time, k ^' _n(t)=k _1N(t)

Otherwise, k ^' _n(t)=k _2N(t);

Because profile point that curvature value is larger reflects the notable feature of target, usually according to k ^' _nt profile point all in profile are divided into characteristic point or non-characteristic point by (), setting variable weight T _k, by judging that objective contour feature is how many, adaptive decision T _k, when | k ^' _n(t) | <T _k* max|k ^' _n(t) | time, characteristic function f (t)=0 otherwise, characteristic function f (t)=1;

The distribution of the characteristic point that obtains and non-characteristic point after classification is also discontinuous, cannot carry out effective contour smoothing by selecting filter to it.In order to obtain good contour smoothing effect, be necessary to carry out merging treatment to profile point of the same type.

Merge module: for rejecting the pseudo-random numbers generation because noise jamming produces, and union operation is carried out to the characteristic point and non-characteristic point that cannot form continuum, thus obtain effective characteristic area and non-characteristic area: a selected starting point O, profile starting point extends the adjacent point of merging to both sides, using this starting point type as this region preset kind, extend each S × μ to both sides ₀in 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 μ ₀different for the curvature according to difference, auto modification development length, the length that the place that curvature is large needs is less, and the length that the place that curvature is little needs more greatly, can effectively reduce the distortion phenomenon after merging like this; Calculate 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 of setting, then this region is identical with preset kind, otherwise, contrary with preset kind; Again with two halt O _{+ 1}with an O _-1restart to calculate as starting point, extend S × μ laterally _o+1or S × μ _o-1in time, stops, wherein μ _o+1and μ _o-1representative point O respectively _{+ 1}with an O _-1the real-time curvature correction factor at place, O _{+ 1}in 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 calculates dissimilarity number according to the ratio of itself and S, counts corresponding characteristic area; Adjacent region of the same type is merged, obtains continuous print characteristic area and non-characteristic area.

Filtration module: multiplicative noise is owing to being relevant with picture signal, change with the change of picture signal, adopt Wiener filtering to carry out first-level filtering and remove, now image information also includes remaining multiplicative noise, by F wave filter F (x, y)=q × exp (-(x ²+ y ²)/β ²carry out secondary filtering, wherein q is by the coefficient of function normalization, that is: ∫ ∫ q × exp (-(x ²+ y ²)/β ²) dxdy=1, β be image template parameter;

After multiplicative noise filtering, the arc length parameterized the Representation Equation of noisy objective contour is G _n(t) '=G (t)+N ₁(t); Suppose that additive noise is white Gaussian noise: x _n(t) '=x (t)+g ₁(t, σ ²), y _n(t) '=y (t)+g ₂(t, σ ²), wherein x _n(t) ' and y _nt () ' represents respectively and to remove after multiplicative noise each point coordinates on noisy profile, g ₁(t, σ ²) and g ₂(t, σ ²) be average be respectively zero, variance is σ ²white Gaussian noise, for simulating the additive noise in noisy objective contour;

Adopt function smoothing to noisy profile, called after K wave filter, through profile point classification and Region dividing, noisy profile G _nt () ' is expressed as the combination of dissimilar contour segmentation: wherein represent 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, order at non-characteristic area, pay close attention to the effect of restraint speckle, order the wherein overall variance that obtains for priori estimation of σ ', σ ₁for the priori estimation variance of selected characteristic area, σ ₀for the priori estimation variance of selected non-characteristic area, for the average real-time curvature correction factor of selected characteristic area, for the average real-time curvature correction factor of selected non-characteristic area; In order to reach good smooth effect, choose the length of half as K wave filter 85% confidential interval of every type region minimum length S, thus the K wave filter of length self adaptation different parameters according to two class regions.

In this embodiment, S=23, threshold value T ₁=0.28, { 15,17}, to noise intensity I ∈ { 40dB for window function width D ∈, although the noisy image of 50dB} adds part amount of calculation, but have excellent smooth effect to the image in this interval, and detailed information retains situation better, laser cutting device is by objective contour identification target, can effective filtering objective contour noise in identifying, this device obtains position and the shape of object by recognition result, cuts, greatly improve cutting efficiency according to instruction to object.

Embodiment 5: a kind of laser cutting device that can independently cut, comprise common laser cutter sweep and be arranged on the Target Identification Unit on laser cutting device, this laser cutting device has very strong self adaptation cutting power, Target Identification Unit can identify target according to objective contour, it is characterized in that, comprise 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 the Representation Equation is 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 the Representation Equation of noisy profile is: G _n(t)=G (t)+N ₁(t)+N ₂(t) G (t), wherein additive noise part N ₁(t)=N ₁(x ₁(t), y ₁(t)), multiplicative noise part N ₂(t)=N ₂(x ₂(t), y ₂(t));

Segmentation module, the segmentation for profile: objective contour G (t) and noisy profile G _nt the curvature corresponding to () is respectively k (t) and k _n(t); Owing to being subject to the impact of noise, noisy profile G _nthe curvature value k of (t) upper part characteristic point _nt () accurately can not represent profile information, in order to obtain curvature accurately, select width to be that { window function W (n) of 17,19}, to curvature k for D ∈ _nt () carries out neighborhood averaging, obtain average curvature k _1N(t), simultaneously to the curvature value sequence in window, selected intermediate value curvature k _2Nt (), by average curvature k _1N(t) and intermediate value curvature k _2Nabsolute value and the selected threshold value T of (t) difference ₁=0.26 compares, and determines noisy contour curvature k according to comparative result ^' _n(t), that is:

When | k _1N(t)-k _2N(t) | >T ₁time, k ^' _n(t)=k _1N(t)

Otherwise, k ^' _n(t)=k _2N(t);

Because profile point that curvature value is larger reflects the notable feature of target, usually according to k ^' _nt profile point all in profile are divided into characteristic point or non-characteristic point by (), setting variable weight T _k, by judging that objective contour feature is how many, adaptive decision T _k, when | k ^' _n(t) | <T _k* max|k ^' _n(t) | time, characteristic function f (t)=0 otherwise, characteristic function f (t)=1;

The distribution of the characteristic point that obtains and non-characteristic point after classification is also discontinuous, cannot carry out effective contour smoothing by selecting filter to it.In order to obtain good contour smoothing effect, be necessary to carry out merging treatment to profile point of the same type.

Merge module: for rejecting the pseudo-random numbers generation because noise jamming produces, and union operation is carried out to the characteristic point and non-characteristic point that cannot form continuum, thus obtain effective characteristic area and non-characteristic area: a selected starting point O, profile starting point extends the adjacent point of merging to both sides, using this starting point type as this region preset kind, extend each S × μ to both sides ₀in 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 μ ₀different for the curvature according to difference, auto modification development length, the length that the place that curvature is large needs is less, and the length that the place that curvature is little needs more greatly, can effectively reduce the distortion phenomenon after merging like this; Calculate 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 of setting, then this region is identical with preset kind, otherwise, contrary with preset kind; Again with two halt O _{+ 1}with an O _-1restart to calculate as starting point, extend S × μ laterally _o+1or S × μ _o-1in time, stops, wherein μ _o+1and μ _o-1representative point O respectively _{+ 1}with an O _-1the real-time curvature correction factor at place, O _{+ 1}in 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 calculates dissimilarity number according to the ratio of itself and S, counts corresponding characteristic area; Adjacent region of the same type is merged, obtains continuous print characteristic area and non-characteristic area.

Filtration module: multiplicative noise is owing to being relevant with picture signal, change with the change of picture signal, adopt Wiener filtering to carry out first-level filtering and remove, now image information also includes remaining multiplicative noise, by F wave filter F (x, y)=q × exp (-(x ²+ y ²)/β ²carry out secondary filtering, wherein q is by the coefficient of function normalization, that is: ∫ ∫ q × exp (-(x ²+ y ²)/β ²) dxdy=1, β be image template parameter;

After multiplicative noise filtering, the arc length parameterized the Representation Equation of noisy objective contour is G _n(t) '=G (t)+N ₁(t); Suppose that additive noise is white Gaussian noise: x _n(t) '=x (t)+g ₁(t, σ ²), y _n(t) '=y (t)+g ₂(t, σ ²), wherein x _n(t) ' and y _nt () ' represents respectively and to remove after multiplicative noise each point coordinates on noisy profile, g ₁(t, σ ²) and g ₂(t, σ ²) be average be respectively zero, variance is σ ²white Gaussian noise, for simulating the additive noise in noisy objective contour;

Adopt function smoothing to noisy profile, called after K wave filter, through profile point classification and Region dividing, noisy profile G _nt () ' is expressed as the combination of dissimilar contour segmentation: wherein represent 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, order at non-characteristic area, pay close attention to the effect of restraint speckle, order the wherein overall variance that obtains for priori estimation of σ ', σ ₁for the priori estimation variance of selected characteristic area, σ ₀for the priori estimation variance of selected non-characteristic area, for the average real-time curvature correction factor of selected characteristic area, for the average real-time curvature correction factor of selected non-characteristic area; In order to reach good smooth effect, choose the length of half as K wave filter 85% confidential interval of every type region minimum length S, thus the K wave filter of length self adaptation different parameters according to two class regions.

In this embodiment, S=25, threshold value T ₁=0.26, window function width D ∈ { 17,19}, to noise intensity I ∈, { the noisy image of 50dB, 60dB} has preferably smooth effect, and detailed information reservation situation is better, laser cutting device, can effective filtering objective contour noise in identifying by objective contour identification target, and this device obtains position and the shape of object by recognition result, according to instruction, object is cut, greatly improve cutting efficiency.

Finally should be noted that; above embodiment is only in order to illustrate technical scheme of the present invention; but not limiting the scope of the invention; although done to explain to the present invention with reference to preferred embodiment; those of ordinary skill in the art is to be understood that; can modify to technical scheme of the present invention or equivalent replacement, and not depart from essence and the scope of technical solution of the present invention.

Data simulation

The beneficial effect of this laser cutting device is: for the diversity of noise type and the unicity of current denoising method, adopts a kind of novel repeatedly filter, and proposes 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 level and smooth effective except making an uproar simultaneously; Consider the otherness of profile between dissimilar region, between restraint speckle and reservation details, obtain good balance; Different according to the curvature of difference, development length is automatic adaptive change correspondingly, effectively reduces the distortion phenomenon after merging.

By emulation, this device is adopted to test under noise intensity N, to the discrimination of target as following table:

Claims (2)

1. the laser cutting device that can independently cut, comprise common laser cutter sweep and be arranged on the Target Identification Unit on laser cutting device, this laser cutting device has very strong self adaptation cutting power, Target Identification Unit can identify target according to objective contour, it is characterized in that, comprise 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 the Representation Equation is 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 the Representation Equation of noisy profile is: G _n(t)=G (t)+N ₁(t)+N ₂(t) G (t), wherein additive noise part N ₁(t)=N ₁(x ₁(t), y ₁(t)), multiplicative noise part N ₂(t)=N ₂(x ₂(t), y ₂(t));

Segmentation module, the segmentation for profile: objective contour G (t) and noisy profile G _nt the curvature corresponding to () is respectively k (t) and k _n(t); Select width to be window function W (n) of D, { 7,9}, to curvature k for D ∈ _nt () carries out neighborhood averaging, obtain average curvature k _1N(t), simultaneously to the curvature value sequence in window, selected intermediate value curvature k _2Nt (), by average curvature k _1N(t) and intermediate value curvature k _2Nabsolute value and the selected threshold value T of (t) difference ₁compare, determine noisy contour curvature k ' according to comparative result _n(t), T ₁=0.2, that is:

When | k _1N(t)-k _2N(t) | >T ₁time, k ' _n(t)=k _1N(t)

Otherwise, k ' _n(t)=k _2N(t);

Because profile point that curvature value is larger reflects the notable feature of target, usually according to k ' _nt profile point all in profile are divided into characteristic point or non-characteristic point by (), setting variable weight T _k, by judging that objective contour feature is how many, adaptive decision T _k, when | k ' _n(t) | <T _k* max|k ' _n(t) | time, characteristic function f (t)=0

Otherwise, characteristic function f (t)=1.

2. laser cutting device according to claim 1, be further characterized in that, merge module: for rejecting the pseudo-random numbers generation because noise jamming produces, and union operation is carried out to the characteristic point and non-characteristic point that cannot form continuum, thus obtain effective characteristic area and non-characteristic area: a selected starting point O, profile starting point extends the adjacent point of merging to both sides, using this starting point type as this region preset kind, extend each S × μ to both sides ₀in 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 μ ₀different for the curvature according to difference, auto modification development length, can effectively reduce the distortion phenomenon after merging; Calculate 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 of setting, then this region is identical with preset kind, otherwise, contrary with preset kind; Again with two halt O _{+ 1}with an O _-1restart to calculate as starting point, extend S × μ laterally ₀₊₁or S × μ _0-1in time, stops, wherein μ ₀₊₁and μ _0-1representative point O respectively _{+ 1}with an O _-1the real-time curvature correction factor at place, O _{+ 1}in 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 calculates dissimilarity number according to the ratio of itself and S, counts corresponding characteristic area; Adjacent region of the same type is merged, obtains continuous print characteristic area and non-characteristic area;

Filtration module: multiplicative noise is owing to being relevant with picture signal, change with the change of picture signal, adopt Wiener filtering to carry out first-level filtering and remove, now image information also includes remaining multiplicative noise, by F wave filter F (x, y)=q × exp (-(x ²+ y ²)/β ²carry out secondary filtering, wherein q is by the coefficient of function normalization, that is: ∫ ∫ q × exp (-(x ²+ y ²)/β ²) dxdy=1, β be image template parameter;

After multiplicative noise filtering, the arc length parameterized the Representation Equation of noisy objective contour is G _n(t) '=G (t)+N ₁(t); Suppose that additive noise is white Gaussian noise: x _n(t) '=x (t)+g ₁(t, σ ²), y _n(t) '=y (t)+g ₂(t, σ ²), wherein x _n(t) ' and y _nt () ' represents respectively and to remove after multiplicative noise each point coordinates on noisy profile, g ₁(t, σ ²) and g ₂(t, σ ²) be average be respectively zero, variance is σ ²white Gaussian noise, for simulating the additive noise in noisy objective contour;

Adopt function smoothing to noisy profile, called after K wave filter, through profile point classification and Region dividing, noisy profile G _nt () ' is expressed as the combination of dissimilar contour segmentation: wherein represent 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, order at non-characteristic area, in order to improve the effect of restraint speckle, order the wherein overall variance that obtains for priori estimation of σ ', σ ₁for the priori estimation variance of selected characteristic area, σ ₀for the priori estimation variance of selected non-characteristic area, for the average real-time curvature correction factor of selected characteristic area, for the average real-time curvature correction factor of selected non-characteristic area; In order to reach good smooth effect, choose the length of half as K wave filter 85% confidential interval of every type region minimum length S, thus the K wave filter of length self adaptation different parameters according to two class regions.