CN108830895A - Differentially expanding moving method based on segmentation in a kind of Stereo matching - Google Patents

Differentially expanding moving method based on segmentation in a kind of Stereo matching Download PDF

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CN108830895A
CN108830895A CN201810687866.2A CN201810687866A CN108830895A CN 108830895 A CN108830895 A CN 108830895A CN 201810687866 A CN201810687866 A CN 201810687866A CN 108830895 A CN108830895 A CN 108830895A
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expression formula
parallax
pixel
label
image
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赵玺
杨新宇
刘鹏康
苏振强
骆志伟
杜妍
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Xian Jiaotong University
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/50—Depth or shape recovery
    • G06T7/55—Depth or shape recovery from multiple images
    • G06T7/593—Depth or shape recovery from multiple images from stereo images
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10004—Still image; Photographic image
    • G06T2207/10012—Stereo images
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20228—Disparity calculation for image-based rendering

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Abstract

The invention discloses the differentially expanding moving methods in a kind of Stereo matching based on segmentation, use image partition method, and the marginal information of image can be fully considered in processing, parallax is avoided to be mutated the influence to result.The present invention can efficiently reduce image in the matching error in the region for lacking texture, and more preferable in the disparity computation result of object edge than differentially expanding moving method, overall accuracy is also higher.The present invention applies 3D label when calculating parallax, and compared with discrete parallax label, obtained parallax is more acurrate, as a result more preferably；The present invention carries out disparity computation on the basis of segmentation, more preferable to the holding of object edge；The present invention can effectively reduce image in the matching error for lacking texture region, and compared with the conventional method, overall accuracy is also higher.

Description

Differentially expanding moving method based on segmentation in a kind of Stereo matching
Technical field
The present invention relates to the differentially expanding moving methods in a kind of Stereo matching based on segmentation.
Background technique
Stereoscopic vision matches an important branch as computer vision, is one by two width of matching or several figures Matched pixel point as between converts two-dimensional location information to three-dimensional depth information, to estimate the three-dimensional mould of scene The process of type.Algorithm for stereo matching is applied earliest in photogrammetry field, the automatically structure from the aviation image of overlapping Build topographic height map.Nowadays, stereoscopic vision matching is in three-dimensional navigation, three-dimensional reconstruction, pilotless automobile, intelligent video The fields such as monitoring, remote Sensing Image Analysis, medical image, intelligent robot control, which suffer from, to be widely applied.
Although stereoscopic vision matching technique has been achieved for significant progress in recent years, it is still faced with many problems. These problems radiation sex differernce caused by block, lack texture (or texture repetition) and illumination, noise etc. this three Matching caused by a reason is fuzzy.It is main below that the problem of lacking texture is discussed.The region for lacking texture is wide in real world General presence, such as white wall, ground or the interior of articles of large stretch of solid color regions etc..Lacking texture means that image lacks in the region Weary characteristic point, can not only exist in sparse algorithm for stereo matching effectively matched can not ask because can not find characteristic point Topic, and even if as the cost of pixel and other pictures in shortage texture region in dense stereo vision matching algorithm Plain cost is identical and leads to matching error.When calculating similarity measurement, a pixel may be with multiple pixels in region Matching cost is consistent, and at this moment, only the matching cost between single pixel can no longer meet demand.
It just needs to improve algorithm in the effect for lacking texture region using more information, partial approach is carrying out generation Valence assemble when can increase window, more pixels are taken into account, but window increase meeting so that algorithm for texture The matching result decline in abundant region.So relying solely on the information in local window, it is difficult to further increase Stereo matching calculation Method is in the accuracy for lacking texture region, and moreover, with the increase of window, the computation complexity of cost aggregation can also be mentioned It is high.Global method can all take into account all pixels of image when calculating, as a result more more acurrate than partial approach, but calculate Complexity is also higher.And the extensive use with 3D stamp methods in Stereo matching, the search space of parallax is from discrete Label space is transformed to continuous Label space, and being equivalent to search result has infinite multiple, this has been further exacerbated by global side The computation complexity of method.And in global approach, the figure method of cutting can be using expansion mobile mechanism, while updating the mark of multiple pixels Label, update pixel-by-pixel are needed rather than belief propagation, this avoids it to fall into local optimum like that.Differentially expanding is mobile to be calculated Method just uses this expansion mobile mechanism cut based on figure, it is applied in regional area, enables it effectively complete While estimating the 3D label of pixel under office system, avoid being trapped in locally optimal solution, while updating the label of multiple pixels Improve the speed of algorithm.But this method relies solely on punishment and the filter function of the smooth item of MRF to keep the edge of object, this So that it can have some problems on the disparity computation of object edge.
Summary of the invention
It is an object of the invention to overcome the above-mentioned prior art, the office based on segmentation in a kind of Stereo matching is provided Portion expands moving method, for the differentially expanding moving method based on segmentation for lacking texture region in image, can effectively mention High algorithm is lacking the accuracy of texture region, while preferably keeping the edge of object.
In order to achieve the above objectives, the present invention is achieved by the following scheme：
Differentially expanding moving method based on segmentation in a kind of Stereo matching, includes the following steps：
Step 1) initialization：
Input picture pair, using the left image of image pair and right image as reference picture and target image；
Step 2) matching cost calculates：
Using the matching cost based on CNN, disparity space image DSI is calculated；
The selection of step 3) parallax：
Disparity computation is carried out based on the differentially expanding moving method of segmentation using disclosed by the invention, obtains original disparity Figure；
Step 4) parallax is refined：
Final left disparity map and right parallax are generated by left and right consistency detection, holes filling and Weighted median filtering Figure.
A further improvement of the present invention lies in that：
The specific method is as follows for step 3)：
Step 3-1) image is split using SLIC；By segmentation, each pixel of image is assigned one A Label for indicating Segment number, from 1 to maximum fractionation block number K；
Step 3-2), for each Segment, in the smallest rectangular area comprising the Segment, traversal is all Pixel just constructs extended region and filter window only when the pixel is inside Segment centered on the pixel；
Step 3-3) each pixel applies iteration α Extension algorithm in the extended region that step 3-2) is established, initial α by The current 3D label value of pixel of Segment internal random selection determines, compare when pixel parallax label takes α system capacity with The size of pixel parallax label energy of entire MRF system when taking current value, if take α, system capacity is smaller, then updates current The label of pixel is α, otherwise keeps current label constant；All pixels in Segment are iterated over every time, by α to expansion Region intramedullary expansion.
Step 3-3) in system capacity energy function it is as follows：
The energy function definition of MRF is as shown in expression formula (1)；
In expression formula (1), first item is data item, the degree of agreement between measurement parallax function and input picture, second Be smooth item, if parallax in adjacent pixel between discontinuous (p, q) ∈ Ν, can punish this, guarantee this in object Internal portion is smooth change, and acute variation can just only occur in object edge；λ is the power for equilibrium data item and smooth item Value, the value of λ are 10.
Data item is as follows：
The identical property between parallax function and input picture is measured using ramp blocks occurrence, is improved using 3D label Algorithm is in the accuracy on inclined-plane, and 3D label is by the parallax d of each pixel ppIndicate that these three parameters represent one with 3 parameters A plane fp, shown in the definition of data item such as expression formula (2)；
In expression formula (2), the 3D label of the parallax of point p is indicated by expression formula (3)；
In expression formula (3), pxAnd pyThe x coordinate and y-coordinate of p are respectively indicated, 3 parameters constitute a tripleThe parallax d of pixel p is estimated in this waypThe problem of being converted into three parameter values of estimation triple；These three Parameter is determined by the random searching strategy of PatchMatch, and a value z is randomly choosed in the range of continuous parallax value0Make For point (x0,y0) parallax, by random unit vector n=(nx,ny,nz) it is used as point (x0,y0,z0) normal vector, three parameters It is converted into：
In expression formula (2), WpIt is the square window centered on p, weight ωpsWith reference to ASW, filtered using navigational figure Method is specifically defined as shown in expression formula (7)；
In expression formula (7), Ip=ILIt (p)/255, is a normalized color vectors, μkAnd ΣkIt is I respectivelypIn window Wk' in mean value and covariance, e is in order to avoid over-fitting；
Given disparity planeIn expression formula (2) function ρ (s | fp) measure window WpInterior pixel s With the similarity of its corresponding points s ' in right image, shown in the definition of s ' such as expression formula (8)；
ρ(s|fp) definition such as expression formula (9) shown in；
ρ(s|fp)=min (CCNN(s,s′),τCNN) (9)
In expression formula (9), CCNNIt is the matching cost calculation method based on CNN, cutoff value τCNNIncrease matching cost pair In the robustness of occlusion area；When calculating the data item of right image, the opposite variation of left images parallax on the contrary, if p and s this When indicate right image in pixel, then to exchange the position of s and s ' in expression formula (9), the minus sign in expression formula (8) is changed For plus sige.
Smooth item is as follows：
Smooth item measures the label (f for indicating that pixel generates { p, q } at label function fp,fqThe distance between)；Expression Smooth item in formula (1) is specifically defined as shown in expression formula (10)；
Esmooth(fp,fq)=max (wpq,ε)min(δpq(fp,fq),τdis) (10)
In expression formula (10), ε is an expression weight wpqThe constant of lower bound is worth very little, can increase weight to noise Robustness, δpq(fp,fq) punishment fpAnd fqBetween parallax discontinuity, cutoff value τdisParallax will be allowed in the violent of edge Variation；Weight wpqDefinition such as expression formula (11) shown in；
In expression formula (11), parameter γ measures the influence of color difference；
δpq(fp,fq) definition such as expression formula (12) shown in；
δpq(fp,fq)=| dp(fp)-dp(fq)|+|dq(fq)-dq(fp)| (12)
In expression formula (12), first item indicates p point in fpAnd fqThe difference of parallax under the plane of the two tag representations, the Binomial is then the difference of point q parallax under the two labels.
Compared with prior art, the invention has the advantages that：
Differentially expanding moving method in Stereo matching disclosed by the invention based on segmentation is due to using image segmentation side Method can fully consider the marginal information of image in processing, parallax is avoided to be mutated the influence to result.The present invention can be effective Ground reduce image lack texture region matching error, and than differentially expanding moving method object edge parallaxometer Calculation result is more preferable, and overall accuracy is also higher.It has the following advantages that：
First：The present invention applies 3D label when calculating parallax, and compared with discrete parallax label, obtained parallax is more Accurately, as a result more preferably；
Second：The present invention carries out disparity computation on the basis of segmentation, more preferable to the holding of object edge；
Third：The present invention can effectively reduce image lack texture region matching error, compared with the conventional method, Overall accuracy is also higher.
Detailed description of the invention
Fig. 1 is process of the invention；
Fig. 2 is the process that the present invention carries out window selection；
Fig. 3 is the Comparative result of the present invention with existing method.
Specific embodiment
The invention will be described in further detail with reference to the accompanying drawing：
Referring to Fig. 1, in order to improve accuracy of the stereoscopic vision matching to texture image is lacked, the present invention uses differentially expanding Mobile thought, first application image dividing method divide the image into Segment, and differentially expanding is then applied on Segment It is mobile, to keep the disparity computation of Stereo Matching Algorithm still can obtain preferable result in object edge.The present invention specifically walks It is rapid as follows：
Step 1) initialization
Input picture pair, using the left image of image pair and right image as reference picture and target image；
Step 2) matching cost calculates
Using the matching cost based on CNN, disparity space image DSI is calculated；
The selection of step 3) parallax：
Disparity computation is carried out based on the differentially expanding moving method of segmentation using disclosed by the invention, obtains original disparity Figure；Specific step is as follows for it：
Step 3-1) image is split using SLIC.By segmentation, each pixel of image is assigned one A Label for indicating Segment number, from 1 to maximum fractionation block number K；
Step 3-2), as shown in Fig. 2, for each Segment, in the smallest rectangular area comprising the Segment In (rectangle that the dotted line in Fig. 2 is surrounded), all pixels are traversed, only when the pixel is inside Segment, just with the picture Extended region (window that radius is r in Fig. 2) and filter window (window that radius is r+R in Fig. 2) are constructed centered on element.With one For a Segment, for each pixel in Segment, centered on it, r is that radius constructs extended region, extended region The window of guiding filtering when expanding R as computation energy function outward again.
Step 3-3) each pixel applies iteration α Extension algorithm in the extended region that step 3-2 is established, initial α by The current 3D label value of pixel of Segment internal random selection determines, compare when pixel parallax label takes α system capacity with The size of pixel parallax label energy of entire MRF system when taking current value, if take α, system capacity is smaller, then updates current The label of pixel is α, otherwise keeps current label constant.All pixels in Segment are iterated over every time, by α to expansion Region intramedullary expansion.Generally pass through 7-9 iteration, pixel can obtain more satisfactory parallax label.
The energy function of above system energy is as follows：
Differentially expanding moving algorithm is established on the basis of the α expansion cut based on figure, substantially a kind of still overall situation Method, therefore solution procedure is still one and seeks the smallest process of markov random file system integral energy.The energy of MRF Function definition is as shown in expression formula (1).
In expression formula (1), first item is data item, the degree of agreement between measurement parallax function and input picture, second Be smooth item, if parallax in adjacent pixel between discontinuous (p, q) ∈ Ν, can punish this, guarantee this in object Internal portion is smooth change, and acute variation can just only occur in object edge.λ is the power for equilibrium data item and smooth item Value, the value of λ are 10.Data item and being specifically defined for smooth item will be provided below.
1) data item
The identical property between parallax function and input picture is measured using ramp blocks occurrence, is improved using 3D label Algorithm is in the accuracy on inclined-plane, and 3D label is by the parallax d of each pixel ppIndicate that these three parameters represent one with 3 parameters A plane fp, shown in the definition of data item such as expression formula (2).
In expression formula (2), the 3D label of the parallax of point p is indicated by expression formula (3).
In expression formula (3), pxAnd pyThe x coordinate and y-coordinate of p are respectively indicated, 3 parameters constitute a tripleThe parallax d of pixel p is estimated in this waypThe problem of being converted into three parameter values of estimation triple.These three Parameter can determine that random selection one is worth in the range of continuous parallax value by the random searching strategy of PatchMatch z0As point (x0,y0) parallax, by random unit vector n=(nx,ny,nz) it is used as point (x0,y0,z0) normal vector, three Parameter can be converted into：
In expression formula (2), WpIt is the square window centered on p, weight ωpsWith reference to ASW, but used in the ASW It is bilateral filtering, the outstanding navigational figure filtering of service performance of the present invention is specifically defined as shown in expression formula (7).
In expression formula (7), Ip=ILIt (p)/255, is a normalized color vectors, μkAnd ΣkIt is I respectivelypIn window Wk' in mean value and covariance, e is in order to avoid over-fitting.
Given disparity planeIn expression formula (2) function ρ (s | fp) measure window WpInterior pixel s with The similarity of its corresponding points s ' in right image, shown in the definition of s ' such as expression formula (8).
ρ(s|fp) definition such as expression formula (9) shown in.
ρ(s|fp)=min (CCNN(s,s′),τCNN) (9)
In expression formula (9), CCNNIt is a kind of matching cost calculation method based on CNN proposed by Zbontar and LeCun, Cutoff value τCNNMatching cost is increased for the robustness of occlusion area.When calculating the data item of right image, left images view The opposite variation of difference will exchange s and s ' in expression formula (9) on the contrary, if p and s indicate the pixel in right image at this time Minus sign in expression formula (8) is changed to plus sige by position.
2) smooth item
Smooth item measures the label (f for indicating that pixel generates { p, q } at label function fp,fq) the distance between it is (similar Degree, smoothness).The present invention uses a kind of smooth item of second order based on curvature, and the smooth item in expression formula (1) is specifically defined As shown in expression formula (10).
Esmooth(fp,fq)=max (wpq,ε)min(δpq(fp,fq),τdis) (10)
In expression formula (10), ε is an expression weight wpqThe constant of lower bound is worth very little, can increase weight to noise Robustness, δpq(fp,fq) punishment fpAnd fqBetween parallax discontinuity, cutoff value τdisParallax will be allowed in the violent of edge Variation.Weight wpqDefinition such as expression formula (11) shown in.
In expression formula (11), parameter γ measures the influence of color difference.
δpq(fp,fq) definition such as expression formula (12) shown in.
δpq(fp,fq)=| dp(fp)-dp(fq)|+|dq(fq)-dq(fp)| (12)
In expression formula (12), first item indicates p point in fpAnd fqThe difference of parallax under the plane of the two tag representations, the Binomial is then the difference of point q parallax under the two labels.
Step 4) parallax is refined：
Final left disparity map and right parallax are generated by left and right consistency detection, holes filling and Weighted median filtering Figure.
As shown in figure 3, Fig. 3 provides the Comparative result of the present invention with existing differentially expanding moving method, (a) indicates input Left image, (b) indicate image Ground Truth.(c) it is proposed by the present invention based on segmentation that-(f) shows respectively displaying Differentially expanding moving method and differentially expanding moving method generate depth map and error map (in error map, the picture of black Element is erroneous pixel, and black region is bigger, and mistake is more).As can be seen that the depth map of method of the invention from (c) and (e) It is substantially better than differentially expanding moving method, especially in clothes hanger peripheral region and wall section.From can be in (d) and (f) It was found that the erroneous pixel of method of the invention is less, as a result more excellent compared with differentially expanding moving method.The reason is that of the invention Method based on image segmentation as a result, segmentation can keep the marginal information of object, compared with directly differentially expanding being applied to move, base It seldom will appear the case where across planar expanded in the differentially expanding moving method of segmentation, and differentially expanding moving method does not have this Limitation.These results all demonstrate the performance of method of the invention in terms of keeping edge and are better than differentially expanding moving algorithm.
The above content is merely illustrative of the invention's technical idea, and this does not limit the scope of protection of the present invention, all to press According to technical idea proposed by the present invention, any changes made on the basis of the technical scheme each falls within claims of the present invention Protection scope within.

Claims (5)

1. the differentially expanding moving method in a kind of Stereo matching based on segmentation, which is characterized in that include the following steps：
Step 1) initialization：
Input picture pair, using the left image of image pair and right image as reference picture and target image；
Step 2) matching cost calculates：
Using the matching cost based on CNN, disparity space image DSI is calculated；
The selection of step 3) parallax：
Disparity computation is carried out based on the differentially expanding moving method of segmentation using disclosed by the invention, obtains original disparity map；
Step 4) parallax is refined：
Final left disparity map and right disparity map are generated by left and right consistency detection, holes filling and Weighted median filtering.
2. the differentially expanding moving method in Stereo matching according to claim 1 based on segmentation, which is characterized in that step 3) the specific method is as follows：
Step 3-1) image is split using SLIC；By segmentation, a table is assigned in each pixel of image The Label for showing Segment number, from 1 to maximum fractionation block number K；
Step 3-2), all pictures are traversed in the smallest rectangular area comprising the Segment for each Segment Element just constructs extended region and filter window only when the pixel is inside Segment centered on the pixel；
Step 3-3) each pixel applies iteration α Extension algorithm in the extended region that step 3-2) is established, initial α by The current 3D label value of pixel of Segment internal random selection determines, compare when pixel parallax label takes α system capacity with The size of pixel parallax label energy of entire MRF system when taking current value, if take α, system capacity is smaller, then updates current The label of pixel is α, otherwise keeps current label constant；All pixels in Segment are iterated over every time, by α to expansion Region intramedullary expansion.
3. the differentially expanding moving method in Stereo matching according to claim 2 based on segmentation, which is characterized in that step The energy function of system capacity in 3-3) is as follows：
The energy function definition of MRF is as shown in expression formula (1)；
In expression formula (1), first item is data item, measures the degree of agreement between parallax function and input picture, and Section 2 is Smooth item, if parallax in adjacent pixel between discontinuous (p, q) ∈ Ν, can punish this, guarantee this in object Portion is smooth change, and acute variation can just only occur in object edge；λ is the weight for equilibrium data item and smooth item, λ Value be 10.
4. the differentially expanding moving method in Stereo matching according to claim 3 based on segmentation, which is characterized in that data Item is as follows：
The identical property between parallax function and input picture is measured using ramp blocks occurrence, improves algorithm using 3D label Accuracy on inclined-plane, 3D label is by the parallax d of each pixel ppIt is indicated with 3 parameters, these three parameters represent one and put down Face fp, shown in the definition of data item such as expression formula (2)；
In expression formula (2), the 3D label of the parallax of point p is indicated by expression formula (3)；
In expression formula (3), pxAnd pyThe x coordinate and y-coordinate of p are respectively indicated, 3 parameters constitute a tripleThe parallax d of pixel p is estimated in this waypThe problem of being converted into three parameter values of estimation triple；These three Parameter is determined by the random searching strategy of PatchMatch, and a value z is randomly choosed in the range of continuous parallax value0Make For point (x0,y0) parallax, by random unit vector n=(nx,ny,nz) it is used as point (x0,y0,z0) normal vector, three parameters It is converted into：
In expression formula (2), WpIt is the square window centered on p, weight ωpsWith reference to ASW, the side filtered using navigational figure Method is specifically defined as shown in expression formula (7)；
In expression formula (7), Ip=ILIt (p)/255, is a normalized color vectors, μkAnd ΣkIt is I respectivelypIn window Wk' in Mean value and covariance, e is in order to avoid over-fitting；
Given disparity planeIn expression formula (2) function ρ (s | fp) measure window WpInterior pixel s and its The similarity of corresponding points s ' in right image, shown in the definition of s ' such as expression formula (8)；
ρ(s|fp) definition such as expression formula (9) shown in；
ρ(s|fp)=min (CCNN(s,s′),τCNN) (9)
In expression formula (9), CCNNIt is the matching cost calculation method based on CNN, cutoff value τCNNMatching cost is increased for hiding Keep off the robustness in region；When calculating the data item of right image, the opposite variation of left images parallax is on the contrary, if p and s are equal at this time It indicates the pixel in right image, then to exchange the position of s and s ' in expression formula (9), the minus sign in expression formula (8) is changed to add Number.
5. the differentially expanding moving method in Stereo matching according to claim 3 based on segmentation, which is characterized in that smooth Item is as follows：
Smooth item measures the label (f for indicating that pixel generates { p, q } at label function fp,fqThe distance between)；Expression formula (1) smooth item in is specifically defined as shown in expression formula (10)；
Esmooth(fp,fq)=max (wpq,ε)min(δpq(fp,fq),τdis) (10)
In expression formula (10), ε is an expression weight wpqThe constant of lower bound is worth very little, can increase weight to the robust of noise Property, δpq(fp,fq) punishment fpAnd fqBetween parallax discontinuity, cutoff value τdisParallax will be allowed in the violent change of edge Change；Weight wpqDefinition such as expression formula (11) shown in；
In expression formula (11), parameter γ measures the influence of color difference；
δpq(fp,fq) definition such as expression formula (12) shown in；
δpq(fp,fq)=| dp(fp)-dp(fq)|+|dq(fq)-dq(fp)| (12)
In expression formula (12), first item indicates p point in fpAnd fqThe difference of parallax, Section 2 under the plane of the two tag representations It is then the difference of point q parallax under the two labels.
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CN114842010A (en) * 2022-07-04 2022-08-02 南通东方雨虹建筑材料有限公司 Building fireproof wood defect detection method based on Gaussian filtering

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Application publication date: 20181116