CN103996201A - Stereo matching method based on improved gradient and adaptive window - Google Patents

Abstract

The invention provides a stereo matching method based on an improved gradient and an adaptive window, and aims to solve the problem that an existing local stereo matching algorithm is low in precision and is influenced by amplitude distortion easily. Firstly, on the basis that a traditional gradient vector only contains amplitude information, phase information is introduced, conversion is carried out on the original matching cost, and abnormal values are further eliminated. Then, the adaptive window can be built through the criss-cross adaptive window generation method according to the color and spatial position relation between adjacent pixels, wherein a large window is provided for a low-texture area to improve matching precision, a small window is generated in a high-texture area to protect detail information of the edge of an object and the like, and the aggregated cost is selected according to the Winner-Takes-All strategy so that a disparity value corresponding to the minimum value of the total cost can serve as an initial matching result. Finally, a disparity refinement method based on a local disparity histogram is provided, and a high-precision disparity map is obtained. As is shown in experimental results, the stereo matching method is high in machining precision and has high robustness under the condition of amplitude distortion.

Description

A kind of solid matching method based on improving gradient and self-adapting window

Technical field:

The present invention relates to a kind of binocular image Stereo matching and obtain depth map method, be particularly related to a kind of solid matching method based on improving gradient and self-adapting window, the depth map result that the present invention obtains can further be applied to the fields such as vision measurement, three-dimensionalreconstruction, drawing virtual view image.

Background technology:

Stereo matching is a classical problem in computer vision, is the focus of research always.For many years, researchers have proposed a large amount of algorithms and have attempted to address this problem, but due to the pathosis of problem itself, at present perfect solution without comparison also.Scharstein is (referring to Daniel Scharstein, Richard Szeliski.A taxonomy and evaluation of dense two-frame stereo correspondence algorithms[J] .International Journal of Computer Vision, 2002,47 (1): 7-42.) etc. furtherd investigate some typical Stereo Matching Algorithm, various main method have been carried out to more comprehensive summary.They Stereo matching process be summarised as that coupling cost is calculated, coupling cost polymerization, initial parallax calculate and parallax four steps of refining, and according to cost polymerization methods, Stereo Matching Algorithm be divided into partial approach and global approach.Global approach generally has higher matching precision, but efficiency is lower; Partial approach travelling speed is fast, be easy to realize, but the coupling cost computing method of the support window that How to choose is suitable and pixel are difficult problem (referring to YANG Qing-xiong.A non-local cost aggregation method for stereo matching[C] .IEEE Conference on Computer Vision and Pattern Recognition, 2012:1402-1409.).

The similarity measure of most of Stereo Matching Algorithm is all the gray-scale value based on pixel, the same unique point in two width images should have identical gray-scale value (referring to Wang Junzheng under desirable illumination condition, Zhu Huajian, Li Jing. a kind of variable weight Stereo Matching Algorithm [J] based on Census conversion. Beijing Institute of Technology's journal, 2013,33 (7): 704-710.).For example gray scale difference absolute value and (AD), gray scale difference quadratic sum (SD), Adapt Weight is (referring to Yoon K, Kweon S.Locally adaptive support weight approch for visual correspondence search[J] .IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28 (4): 924-931.), Segment Support is (referring to Tombari F, Mattoccia S.Segmentation based adaptive support for accurate stereo correspondence[C] .IEEE Pacific Rim Symposium on Video and Technology, 2007:427-438.) etc. can obtain the matching result of degree of precision for ideal image, but these methods are for by illumination variation, difference in exposure, the picture amplitude distortion that the factors such as the dark angle of camera cause is very responsive, therefore be difficult to use in the coupling of real scene image.The insensitive coupling cost of amplitude distortion is mainly contained to normalized crosscorrelation (NCC), gradient (Gradient) is (referring to Daniel Scharstein.View synthesis using stereo vision.Phd thesis, 1997, 23 (5): 98-109.), Rank and Census convert (referring to Ramin Zabih, John Woodfill.Non-parametric local transforms for computing visual correspondence[C] .Proceedings of European Conference on Computer Vision, 1994:151-158.) etc.

There is the selection problem of mapping window size in traditional stationary window: if window is too little, mates cost discrimination too low, easily occur mistake coupling at low texture region; If window is excessive, can occur mating at degree of depth discontinuity zone again (referring to Zhou Long, Xu Guili, Li Kaiyu etc. based on the Stereo Matching Algorithm [J] of Census conversion and improvement self-adapting window. aviation journal; by mistake 2012,33 (5): 886-892.).Fusiello and Roberto are (referring to Fusiello A, Roberto V.Efficient stereo with multiple windowing[C] .IEEE Computer Society Conference on Computer vision and Pattern Recognition, 1997:858-863.) propose to select optimum window as support window in multiple windows given in advance; Veksler (referring to Veksler O.Fast variable window for stereo correspondence using integral image[C] .IEEE Computer Society Conference on Computer vision and Pattern Recognition, 2003:556-561.) proposes pointwise self-adaptation and chooses shape and the size of support window; Zhang (referring to Zhang K.Cross-based local stereo matching using orthogonal integral images[J] .IEEE Transactions on Circuits and systems for Video Technology, 2009,19 (7): 1073-1079.), according to the adaptively selected arbitrary shape of the color relations of neighbor and big or small support window, obtain good parallax result.

Summary of the invention:

For above-mentioned coupling cost and window selection problem, the present invention proposes a kind of matching process based on improving gradient coupling cost and self-adapting window.First only comprise on the basis of amplitude information in traditional gradient coupling cost, introduce Gradient Phase information, and original match cost is converted, further eliminate exceptional value; Then utilize picture structure and color information to build that self-adapting window carries out cost polymerization and " the victor is a king " (Winner-Takes-All (WTA)) strategy carries out parallax selection; Finally, propose the histogrammic parallax refined method of a kind of local parallax, obtained high-precision disparity map.

The technical problem to be solved in the present invention is:

1. traditional local matching method is lower to picture amplitude distortion-robust, the problem that matching precision reduces rapidly under illumination distortion condition.

2. the cost polymerization of stationary window is difficult to obtain at the low texture of image and the degree of depth discontinuity zone problem of higher matching precision simultaneously.

The technical solution adopted for the present invention to solve the technical problems: based on the matching process that improves gradient coupling cost and self-adapting window, it is characterized in that comprising the following steps:

Step 1: coupling cost is calculated: coupling cost is the tolerance of corresponding point similarity between the image of left and right, utilizes two components of gradient vector in image x, y direction, mould m and the phase angle of definition gradient vector then adopt the linearity of mould and phase angle to be combined as coupling cost function, to utilize to greatest extent gradient information.Finally utilize Geman-McClure function original match cost function to be converted to eliminate the impact of abnormal cost value.

Step 2: adaptive windows outlet structure: treat the polymerization window of a self-adaptation size of each pixel structure of matching image, it is how many that the large young pathbreaker of window directly determines to participate in the neighborhood territory pixel of polymerization.The present invention adopts a kind of improved right-angled intersection self-adapting window generation method, can build self-adapting window according to the color of neighbor and spatial relation.At low texture region, provide larger window to improve matching precision; Produce less window at high texture region, to protect the detailed information such as object edge.

Step 3: cost polymerization: after determining the self-adapting window of each pixel, need carry out polymerization to the original match cost of each single pixel in window and obtain total cost, finally select to make parallax value that total Least-cost is corresponding as initial matching result.

Step 4: parallax is refined: the initial parallax obtaining by above-mentioned steps also exists some Mismatching points and insincere value with true parallax, need to carry out the parallax processing of refining.The present invention proposes one and based on the histogrammic parallax refined method of local parallax, initial parallax figure is further processed.Then, adopt left and right consistency check to detect the Mismatching point still existing, utilize the value that in adjacent available point, parallax is less to carry out assignment to Mismatching point.

Brief description of the drawings:

Fig. 1 is the process flow diagram of the inventive method.

Fig. 2 is self-adapting window construction process schematic diagram.

Fig. 3 (a) is the right experimental result of Tsukuba image.

Fig. 3 (b) is the right experimental result of Venus image.

Fig. 3 (c) is the right experimental result of Teddy image.

Fig. 3 (d) is the right experimental result of Cones image.

Fig. 4 (a) is the experimental result under same light photograph and conditions of exposure.

Fig. 4 (b) is the experimental result under different illumination conditions.

Fig. 4 (c) is the experimental result under different exposure.

Fig. 4 (d) is the experimental result under different light and conditions of exposure.

Embodiment:

Be described in further detail the present invention below in conjunction with the drawings and the specific embodiments.

The present invention proposes a kind of matching process based on improving gradient coupling cost and self-adapting window, mainly contain four steps.

Step 1: coupling cost is calculated.Concrete methods of realizing is as follows:

Image gradient is defined as the single order partial derivative of image along x and y direction:

$G=\left(\begin{array}{c}{G}_x\\ G_y\end{array}\right)=\left(\begin{array}{c}\frac{\∂I}{\∂x}\\ \frac{\∂I}{\∂y}\end{array}\right)---\left(1\right)$

Wherein I is gradation of image, in practical application, and can be by the formwork calculation gradient vector of horizontal direction and vertical direction.Like this, we just can obtain the gradient map G of left and right image _l=(G _lx, G _ly) ^t, G _r=(G _rx, G _ry) ^t; Consider the image after proofreading and correct, establishing p (x, y) is a bit on left image, and on right image, the match point of corresponding parallax d is pd (x-d, y).

Utilize two components of gradient vector in x, y direction, mould and the phase angle of definition gradient vector:

$m=\sqrt{G_x^2+G_y^2}---\left(2\right)$

The mould m of gradient characterizes rate of gray level, phase angle direction while characterizing rate of gray level maximum, they provide the different information of neighborhood of pixels, and illumination distortion is had to different unchangeability.Input picture can affect gradient-norm to gain distortions, and phase angle can not change, but they can not be subject to bias distortion impact.Thereby, the mould of gradient and phase angle are separately considered to be more conducive to the susceptibility of control method to noise.The present invention adopts the linearity of mould and phase angle to be combined as coupling cost function, to utilize to greatest extent gradient information.Be expressed as follows:

M in formula _c, represent respectively corresponding to coloured image R, G, gradient mode and the phase angle of tri-passages of B, α is weighting coefficient.Because phase angle is taking π as the cycle, need to be normalized in the monocycle, therefore definition f:

$f\left(x\right)=\left\{\begin{array}{cc}x& 0\&le;x\&le;\mathrm{\&pi;}\\ 2\mathrm{\&pi;}-x& \mathrm{\&pi;}<x<2\mathrm{\&pi;}\end{array}\right.---\left(5\right)$

Owing to having introduced weighting coefficient α, we can be by adjusting the value change method of the parameter alpha robustness to illumination distortion and noise.α is less, and the impact of phase place is larger, and α is larger, and the impact of mould value is larger.Because different images has illumination distortion in various degree, in reality, need to come by experiment to determine the reasonable value scope of α.

Due to e (p, d) represent be the original match cost of single pixel, in actual conditions, still can there are some excessive exceptional values, need to get rid of to improve matching precision.Conventional method is to adopt a truncated function, compares with a constant by e (p, d), gets its minimum value as coupling cost.The method is very little to the improvement of result, and the present invention adopts Geman-McClure function to process exceptional value:

$\mathrm{\&rho;}\left(x\right)=\frac{x^2}{x^2+{\mathrm{\&sigma;}}^2}---\left(6\right)$

When input, x exceedes after certain value, and it will drop to 0 smoothly on the impact of output valve, and the coupling cost after conversion will converge to 1, and can be controlled by parameter σ.Thereby, no matter input original match cost much, after Geman-McClure functional transformation, its output valve will can not exceed 1.

Step 2: adaptive windows outlet structure.Concrete methods of realizing is as follows:

The core of method therefor of the present invention is to build self-adapting window according to the color of neighbor and spatial relation, and concrete construction process as shown in Figure 2.First, determine a right-angled intersection region of current pixel p to be matched according to picture structure and color information, this right-angled intersection district inclusion horizontal and vertical direction, uses respectively H (p) and V (p) to represent, the size in region is by the brachium of 4 directions determine, and can be according to the structure of image and color information adaptively modifying.With for example, the criterion of brachium is as follows:

1.D _c(p _i, p) < τ ₁and D _c(p _i, p _i+ (1,0)) < τ ₁;

2.D _s(p _i,p)<L ₁；

3.D _c(p _i,p)<τ ₂,L ₂<D _s(p _i,p)<L ₁。

Wherein, D _s(p _i, p) be pixel p _ipoor with the space length of p; D _c(p _i, p) be color difference, be defined as τ ₁> τ ₂, L ₁>L ₂, be default color threshold value and distance threshold.Criterion 1 not only defines p _ithe color difference opposite sex with p requires p simultaneously _iwith its right side neighbor p _ithe color difference opposite sex of+(1,0) is less than τ ₁, avoided brachium to stride across borderline region; Criterion 2 and 3 has been relaxed brachium scope, uses larger distance threshold L at low texture region ₁can obtain larger window; And in the time that brachium exceedes preset value L ₂time, will adopt stricter threshold tau ₂ensure that brachium is only in the very close low texture region expansion of color, make high texture region and the degree of depth discontinuity zone window can be not excessive.

Utilize said method can determine respectively 4 brachiums and then obtain orthogonal right-angled intersection region H (p) and V (p):

$\left\{\begin{array}{c}H\left(p\right)=\left\{(x,y)\right|x\&Element;[x_p-h_p^{-},x_p+h_p^{+}],y=y_p\}\\ V\left(p\right)=\left\{(x,y)\right|x=x_p,y\&Element;[y_p-v_p^{-},y_p+v_p^{+}]\}\end{array}\right.---\left(7\right)$

Finally, along vertical direction, each pixel q in V (p) is repeated to said process, the adaptive region of trying to achieve any pixel p in image is:

$U\left(p\right)=\underset{q\&Element;V\left(p\right)}{\&cup;}H\left(q\right)---\left(8\right)$

Step 3: cost polymerization.Concrete methods of realizing is as follows:

The present invention will consider left and right image local support area separately symmetrically.For two corresponding match point p (x in the image of left and right, and pd (x-d y), y), utilize said method can generate respectively adaptive region U (p) and U'(pd), their associating public domain is defined as final support area by we:

U _d(p)＝{(x,y)|(x,y)∈U(p),(x-d,y)∈U'(pd)} (9)

Then, in associating support area, original single pixel matching cost is carried out to polymerization, tries to achieve total cost in region:

$E_d\left(p\right)=\frac{1}{N}\underset{q\&Element;U_d\left(p\right)}{\mathrm{\&Sigma;}}e(q,d)---\left(10\right)$

In formula, N is zone of convergency U _d(p) the total number of pixel in.Finally adopt " the victor is a king " (Winner-Takes-All (WTA)) strategy, in parallax interval, select the point of coupling Least-cost to carry out parallax selection as matching double points p point, obtain initial parallax:

$d_p^0=\arg \underset{0\&le;d\&le;d_{\max }}{\min }{E}_d\left(p\right)---\left(11\right)$

Wherein d represents the possible parallax in disparity space, and its value is generally 0 to maximum disparity d _maxbetween integer.

Step 4: parallax is refined.Concrete methods of realizing is as follows:

The present invention proposes one and based on the histogrammic parallax refined method of local parallax, initial parallax figure is further processed.For certain pixel p in disparity map, we construct a local parallax histogram centered by it within the scope of its neighborhood the number of times that in statistics field, each parallax value occurs.In histogram, will there is a peak value, represent the maximum times that parallax occurs.The parallax value that this peak value is corresponding is the optimum parallax value in statistical significance the present invention adopts this optimal value to replace the initial parallax of pixel p

This process has been utilized the self-adapting window that generates in the previous step neighborhood as pixel, thereby does not produce extra computation burden.In addition, the present invention carries out iteration three times to this process, makes optimum parallax value more accurate.

Then, adopt left and right consistency check to detect the Mismatching point still existing, be labeled as available point by the point of consistency check, otherwise be Null Spot.For the Mismatching point detecting, first available point of horizontal scan direction left and right, and utilize the value that in both, parallax is less to carry out assignment to Mismatching point.

For the validity of verification method, on VS2008 platform, to the inventive method realization of programming, and the standard stereotome of being issued by Middlebury website that adopts academic circles at present to generally acknowledge is tested and is evaluated and tested method.This website provides 4 groups of reference color images to Tsukuba, Venus, Teddy and Cones, and corresponding real depth map.Experimental result and true disparity map (Ground truth) relatively can be obtained to the matching error quantizing, thus evaluation method precision objectively.Table 1 is that the inventive method is at limits of error δ _dthe mistake matched pixel number percent data of=1 o'clock.The row at Noocc, All, Disc place be respectively unshielding region mistake matched pixel ratio, always by mistake matched pixel than and degree of depth discontinuity zone mistake matched pixel ratio.And with GC+occ (referring to Kolmogorov V, Rabih R.Computing visual correspondence with occlusions using graph cuts[C] .IEEE Conference on Computer Vision, 2001.), SemiGlob (referring to Hirschm ü ller H.Accurate and efficient stereo processing by semi-global matching and mutual information[J] .IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008,30 (2): 328-341.), AdaptWeight is (referring to Yoon K, Kweon S.Locally adaptive support weight approch for visual correspondence search[J] .IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006,28 (4): 924-931.) and Enhanced BP (referring to Larsen S, Mordohai P, Pollefeys M et al.Temporally consistent reconstruction from multiple video streams using enhanced belief propagation[C] .IEEE International Conference on Computer Vision, 2007:1-8.) method contrasts.

Table 1 matching result objective evaluation

Fig. 3 has reacted the precision of the inventive method operation result intuitively.(a), (b), (c), (d) are followed successively by Tsukuba, Venus, Teddy and the right experimental result of Cones image; In figure, the 1st classifies original image to be matched as; The 2nd classifies corresponding true disparity map as; The 3rd classifies the disparity map that the inventive method obtains as; The 4th classifies the mistake matched pixel figure of the inventive method as, and in figure, white bulk zone is the point that coupling is correct, and gray area and black region represent respectively the Mismatching point in occlusion area and unobstructed region.

Fig. 4 is the operation result of the present invention under illumination distortion condition, and is that the method for mating cost compares with using traditional gray scale absolute difference (AD), to verify the robustness of the inventive method to illumination distortion.As shown in Figure 4, in figure, (a), (b), (c), (d) are respectively the experimental result under same light photograph and exposure, different light, different exposure and different light and conditions of exposure to experimental result.The 1st of every group of result classified original left image as, and the 2nd classifies original right image as, and the 3rd classifies the Variable Cross method matching result that adopts SAD coupling cost as, and the 4th classifies the inventive method matching result as.Experimental result shows, the present invention, owing to having adopted the gradient information that amplitude distortion is had to repellence as coupling cost, in the situation that having illumination distortion, still can obtain the matching result that precision is higher, has good robustness.

Claims (4)

1. the solid matching method based on improving gradient and self-adapting window, it is characterized in that: the method only comprises on the basis of amplitude information in traditional gradient coupling cost, introduce Gradient Phase information, and original match cost is converted, further eliminate exceptional value; Then utilize picture structure and color information to build that self-adapting window carries out cost polymerization and " the victor is a king " (Winner-Takes-All (WTA)) strategy carries out parallax selection; Finally, propose the histogrammic parallax refined method of a kind of local parallax, obtained high-precision disparity map.Method concrete steps are as follows:

Step 1: coupling cost is calculated: coupling cost is the tolerance of corresponding point similarity between the image of left and right, utilizes two components of gradient vector in image x, y direction, mould m and the phase angle of definition gradient vector then adopt the linearity of mould and phase angle to be combined as coupling cost function, to utilize to greatest extent gradient information.Finally utilize Geman-McClure function original match cost function to be converted to eliminate the impact of abnormal cost value.

Step 2: adaptive windows outlet structure: treat the polymerization window of a self-adaptation size of each pixel structure of matching image, it is how many that the large young pathbreaker of window directly determines to participate in the neighborhood territory pixel of polymerization.The present invention adopts a kind of improved right-angled intersection self-adapting window generation method, can build self-adapting window according to the color of neighbor and spatial relation.At low texture region, provide larger window to improve matching precision; Produce less window at high texture region, to protect the detailed information such as object edge.

Step 3: cost polymerization: after determining the self-adapting window of each pixel, need carry out polymerization to the original match cost of each single pixel in window and obtain total cost, finally select to make parallax value that total Least-cost is corresponding as initial matching result.

Step 4: parallax is refined: the initial parallax obtaining by above-mentioned steps also exists some Mismatching points and insincere value with true parallax, need to carry out the parallax processing of refining.The present invention proposes one and based on the histogrammic parallax refined method of local parallax, initial parallax figure is further processed.Then, adopt left and right consistency check to detect the Mismatching point still existing, utilize the value that in adjacent available point, parallax is less to carry out assignment to Mismatching point.

2. according to a kind of solid matching method based on improving gradient and self-adapting window described in claim 1, it is characterized in that: the gradient cost of the coupling cost application enhancements of described step 1, concrete computation process is as follows:

Image gradient is defined as the single order partial derivative of image along x and y direction:

$G=\left(\begin{array}{c}{G}_x\\ G_y\end{array}\right)=\left(\begin{array}{c}\frac{\&PartialD;I}{\&PartialD;x}\\ \frac{\&PartialD;I}{\&PartialD;y}\end{array}\right)$

Wherein I is gradation of image, in practical application, and can be by the formwork calculation gradient vector of horizontal direction and vertical direction.Like this, we just can obtain the gradient map G of left and right image _l=(G _lx, G _ly) ^t, G _r=(G _rx, G _ry) ^t; Consider the image after proofreading and correct, establishing p (x, y) is a bit on left image, and on right image, the match point of corresponding parallax d is pd (x-d, y).

Utilize two components of gradient vector in x, y direction, mould and the phase angle of definition gradient vector:

$m=\sqrt{G_x^2+G_y^2}$

The mould m of gradient characterizes rate of gray level, phase angle direction while characterizing rate of gray level maximum, they provide the different information of neighborhood of pixels, and illumination distortion is had to different unchangeability.Input picture can affect gradient-norm to gain distortions, and phase angle can not change, but they can not be subject to bias distortion impact.Thereby, the mould of gradient and phase angle are separately considered to be more conducive to the susceptibility of control method to noise.The present invention adopts the linearity of mould and phase angle to be combined as coupling cost function, to utilize to greatest extent gradient information.Be expressed as follows:

M in formula _c, represent respectively corresponding to coloured image R, G, gradient mode and the phase angle of tri-passages of B, α is weighting coefficient.Because phase angle is taking π as the cycle, need to be normalized in the monocycle, therefore definition f (x):

$f\left(x\right)=\left\{\begin{array}{cc}x& 0\&le;x\&le;\mathrm{\&pi;}\\ 2\mathrm{\&pi;}-x& \mathrm{\&pi;}<x<2\mathrm{\&pi;}\end{array}\right.$

Owing to having introduced weighting coefficient α, we can be by adjusting the value change method of the parameter alpha robustness to illumination distortion and noise.α is less, and the impact of phase place is larger, and α is larger, and the impact of mould value is larger.Because different images has illumination distortion in various degree, in reality, need to come by experiment to determine the reasonable value scope of α.

Due to e (p, d) represent be the original match cost of single pixel, in actual conditions, still can there are some excessive exceptional values, need to get rid of to improve matching precision.Conventional method is to adopt a truncated function, compares with a constant by e (p, d), gets its minimum value as coupling cost.The method is very little to the improvement of result, and the present invention adopts Geman-McClure function to process exceptional value:

$\mathrm{\&rho;}\left(x\right)=\frac{x^2}{x^2+{\mathrm{\&sigma;}}^2}$

When input, x exceedes after certain value, and it will drop to 0 smoothly on the impact of output valve, and the coupling cost after conversion will converge to 1, and can be controlled by parameter σ.Thereby, no matter input original match cost much, after Geman-McClure functional transformation, its output valve will can not exceed 1.

3. according to a kind of solid matching method based on improving gradient and self-adapting window described in claim 1, it is characterized in that: the right-angled intersection self-adapting window generation method of described step 2 and step 3 application enhancements, detailed process is as follows:

First, determine a right-angled intersection region of current pixel p to be matched according to picture structure and color information, this right-angled intersection district inclusion horizontal and vertical direction, uses respectively H (p) and V (p) to represent, the size in region is by the brachium of 4 directions determine, and can be according to the structure of image and color information adaptively modifying.With for example, the criterion of brachium is as follows:

1.D _c(p _i, p) < τ ₁and D _c(p _i, p _i+ (1,0)) < τ ₁;

2.D _s(p _i,p)<L ₁；

3.D _c(p _i,p)<τ ₂,L ₂<D _s(p _i,p)<L ₁。

Wherein, D _s(p _i, p) be pixel p _ipoor with the space length of p; D _c(p _i, p) be color difference, be defined as τ ₁> τ ₂, L ₁>L ₂, be default color threshold value and distance threshold.Criterion 1 not only defines p _ithe color difference opposite sex with p requires p simultaneously _iwith its right side neighbor p _ithe color difference opposite sex of+(1,0) is less than τ ₁, avoided brachium to stride across borderline region; Criterion 2 and 3 has been relaxed brachium scope, uses larger distance threshold L at low texture region ₁can obtain larger window; And in the time that brachium exceedes preset value L ₂time, will adopt stricter threshold tau ₂ensure that brachium is only in the very close low texture region expansion of color, make high texture region and the degree of depth discontinuity zone window can be not excessive.

Utilize said method can determine respectively 4 brachiums and then obtain orthogonal right-angled intersection region H (p) and V (p):

$\left\{\begin{array}{c}H\left(p\right)=\left\{(x,y)\right|x\&Element;[x_p-h_p^{-},x_p+h_p^{+}],y=y_p\}\\ V\left(p\right)=\left\{(x,y)\right|x=x_p,y\&Element;[y_p-v_p^{-},y_p+v_p^{+}]\}\end{array}\right.$

Finally, along vertical direction, each pixel q in V (p) is repeated to said process, the adaptive region of trying to achieve any pixel p in image is:

$U\left(p\right)=\underset{q\&Element;V\left(p\right)}{\&cup;}H\left(q\right)$

The present invention will consider left and right image local support area separately symmetrically.For two corresponding match point p (x in the image of left and right, and pd (x-d y), y), utilize said method can generate respectively adaptive region U (p) and U'(pd), their associating public domain be defined as to final support area:

U _d(p)＝{(x,y)|(x,y)∈U(p),(x-d,y)∈U'(pd)}

Then, in associating support area, original single pixel matching cost is carried out to polymerization, tries to achieve total cost in region:

$E_d\left(p\right)=\frac{1}{N}\underset{q\&Element;U_d\left(p\right)}{\mathrm{\&Sigma;}}e(q,d)$

In formula, N is zone of convergency U _d(p) the total number of pixel in.Finally adopt " the victor is a king " (Winner-Takes-All (WTA)) strategy, in parallax interval, select the point of coupling Least-cost to carry out parallax selection as matching double points p point, obtain initial parallax:

$d_p^0=\arg \underset{0\&le;d\&le;d_{\max }}{\min }{E}_d\left(p\right)$

Wherein d represents the possible parallax in disparity space, and its value is generally 0 to maximum disparity d _maxbetween integer.

4. according to a kind of solid matching method based on improving gradient and self-adapting window described in claim 1, it is characterized in that: described step 4 has proposed based on the histogrammic parallax refined method of local parallax, concrete computation process is as follows:

For certain pixel p in disparity map, centered by it, within the scope of its neighborhood, construct a local parallax histogram the number of times that in statistics field, each parallax value occurs.In histogram, will there is a peak value, represent the maximum times that parallax occurs.The parallax value that this peak value is corresponding is the optimum parallax value in statistical significance the present invention adopts this optimal value to replace the initial parallax of pixel p

This process has been utilized the self-adapting window that generates in the previous step neighborhood as pixel, thereby does not produce extra computation burden.In addition, the present invention carries out iteration three times to this process, makes optimum parallax value more accurate.

Then, adopt left and right consistency check to detect the Mismatching point still existing, be labeled as available point by the point of consistency check, otherwise be Null Spot.For the Mismatching point detecting, first available point of horizontal scan direction left and right, and utilize the value that in both, parallax is less to carry out assignment to Mismatching point.