CN107392950A - A kind of across yardstick cost polymerization solid matching method based on weak skin texture detection - Google Patents

Abstract

A kind of across yardstick cost polymerization solid matching method based on weak skin texture detection of the present invention belongs to computer vision field, more particularly to the solid matching method to weak texture image, comprises the following steps：Two width coloured images are inputted, two described width coloured images are respectively left image and right image, and weak skin texture detection and segmentation are carried out to picture using the gradient information of left image；Matching power flow is calculated according to the colouring information and gradient information of left image and right image；On the basis of weak skin texture detection and segmentation result in above-mentioned, the interior yardstick based on gaussian filtering and the polymerization of across yardstick cost are carried out；Take policy calculation parallax entirely using the person of winning；Parallax is refined using left and right consistency detection and the method based on adaptive weighting, exports anaglyph.The present invention is realized on the premise of texture region matching accuracy is ensured, improves weak texture region matching accuracy, obtains the technical purpose of more preferable disparity map.

Description

Cross-scale cost aggregation stereo matching method based on weak texture detection

Technical Field

The invention discloses a cross-scale cost aggregation stereo matching method based on weak texture detection, belongs to the field of computer vision, and particularly relates to a stereo matching method for weak texture images.

Background

Binocular stereo vision (binocular stereo vision) is an important form of computer vision, and is a method for acquiring three-dimensional geometric information of an object by acquiring two images of the object to be measured from different positions by using imaging equipment based on a parallax principle and calculating position deviation between corresponding points of the images. And the quality of the three-dimensional information acquisition mainly depends on the accuracy of the disparity map obtained by stereo matching. At present, the problems of stereo matching mainly include external factors such as uneven illumination, overexposure and the like, and the picture has the picture characteristics such as shading, weak texture, repeated texture and the like which are difficult to distinguish by a computer. Although a large number of scholars have studied stereo matching for many years, matching for weakly textured regions remains a difficulty in the field of image processing. On the premise of ensuring the matching accuracy of the texture region, the method for improving the matching accuracy of the weak texture region and obtaining a better disparity map is a major problem.

Disclosure of Invention

The invention provides a cross-scale cost polymerization stereo matching method based on weak texture detection, which can improve the matching accuracy of a weak texture region and obtain a better disparity map on the premise of ensuring the matching accuracy of the texture region.

The purpose of the invention is realized as follows:

a cross-scale cost aggregation stereo matching method based on weak texture detection comprises the following steps:

step a, inputting two color images, wherein the two color images are a left image and a right image respectively, and performing weak texture detection and segmentation on the images by using gradient information of the left image;

b, calculating matching cost according to the color information and the gradient information of the left image and the right image;

step c, taking the weak texture detection and segmentation result in the step a as a reference, and carrying out inner scale and cross-scale cost aggregation based on Gaussian filtering;

d, calculating the parallax by adopting a win person total-taking strategy;

and e, adopting a left-right consistency detection method and a self-adaptive weight-based method to refine the parallax and output a parallax image.

The cross-scale cost aggregation stereo matching method based on weak texture detection specifically comprises the following steps of:

calculating the gradient value g (x, y) of the pixel point at the coordinate (x, y) of the left image and the gradient threshold value g_TComparing and judging whether the texture is a weak texture area, wherein the calculation formula is as follows:

g(x,y)＜g_T

in the formula, N (x, y) represents a window with a pixel (x, y) as the center, M represents the number of pixels in the window, and I (x, y) represents the gray scale value of the pixel.

In the method for cross-scale cost aggregation stereo matching based on weak texture detection, the calculating of the matching cost in the step b specifically includes:

calculating a left image I of a stereoscopic color image pair_LAnd a right image I_RThe matching cost C (p, d) is calculated by the formula:

C(p,d)＝(1-α)·C_AD(p,d)+α·(C_{grad_x}(p,d)+C_{grad_y}(p,d))

in the formula: p is a point in the left image, where i ═ R, G, B represent the three channels of the color image, respectively, and T_ADAnd T_gradCutoff thresholds representing color and gradient, respectively;representing the gradient operators of the picture in the x and y directions, respectively, α is a balance factor between the color difference and the gradient difference.

The cross-scale cost aggregation stereo matching method based on weak texture detection is characterized in that the cost aggregation in the step c specifically comprises the following steps:

wherein,indicating match after aggregationThe cost is that z is an expected optimization target value, W is a Gaussian filter kernel, N is a neighborhood window of a pixel p, q is a neighborhood pixel point of p, S ∈ {0, 1.., S } is a scale parameter, and when S is 0, C is⁰Representing the original scale matching cost of the image;an aggregate cost representing S +1 scales of the image;

in the formula, lambda is a regularization factor,by usingAn optimization objective function representing equation (11), andcomprises the following steps:

wherein, T_highAnd T_lowRespectively representing the texture region and the weak texture region detected in the text; c₁And C_1/2Respectively representing the matching cost of the original image scale and the half scale, carrying out Gaussian filtering by using windows with different sizes, and obtaining the final matching cost after fusion.

The cross-scale cost aggregation stereo matching method based on weak texture detection includes the following steps:

|D'_L(P)-D'_R(P-D'_R(P))|＜

D_LRC(P)＝min(D'(PL),D'(PR))

wherein, the left chart parallax value D 'of one point p in the parallax chart'_L(p) and Right View disparity value D'_R(p-D'_L(p)), a threshold for LRC; d' (PL) is the disparity value of the first non-occlusion point on the left side, and D (PR) is the disparity value of the first non-occlusion point on the right side; WB (wideband weight division multiple Access)_pq(I_L) As a function of the left image, Δ c_pqAnd Δ s_pqRespectively the color difference and the spatial euclidean distance of points p and q in the left image,andadjustment parameters of color difference and distance difference respectively; d_w(p) a filtered image.

Has the advantages that:

the invention discloses a cross-scale cost polymerization stereo matching method based on weak texture detection, and provides a novel stereo matching algorithm.

The stereo matching image pair processed by the algorithm of the embodiment can obtain better effect in the texture region and the weak texture region of the image, and the error matching rate is reduced (5% lower than that of the stereo matching image pair without the weak texture region segmentation algorithm). The algorithm of the embodiment can improve the matching accuracy of the weak texture region and obtain a better disparity map on the premise of ensuring the matching accuracy of the texture region.

Drawings

Fig. 1 is a flowchart of a cross-scale cost aggregation stereo matching method based on weak texture detection.

Fig. 2 is a Bowling1 disparity map.

Fig. 3 is a Lampshade1 disparity map.

Fig. 4 is a Monopoly disparity map.

FIG. 5 is a Plastic disparity map.

Detailed Description

The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.

Detailed description of the preferred embodiment

A cross-scale cost aggregation stereo matching method based on weak texture detection is disclosed, as shown in FIG. 1, and includes the following steps:

step a, inputting two color images, wherein the two color images are a left image and a right image respectively, and performing weak texture detection and segmentation on the images by using gradient information of the left image;

b, calculating matching cost according to the color information and the gradient information of the left image and the right image;

step c, taking the weak texture detection and segmentation result in the step a as a reference, and carrying out inner scale and cross-scale cost aggregation based on Gaussian filtering;

d, calculating the parallax by adopting a win person total-taking strategy;

and e, adopting a left-right consistency detection method and a self-adaptive weight-based method to refine the parallax and output a parallax image.

According to the above steps, four pictures are selected for comparison, as shown in figures 2, 3, 4 and 5,

in FIG. 2, FIG. 2(a) is the left image of Bowling 1; FIG. 2(b) is a Bowling1 true disparity map; FIG. 2(c) is Bowling1 weak texture detection results; FIG. 2(d) is Bowling1 final disparity map; fig. 2(e) is a disparity map in which no weak texture detection is performed by Bowling 1.

In fig. 3, fig. 3(a) is a left image of Lampshade 1; fig. 3(b) is a real disparity map of Lampshade 1; FIG. 3(c) is the result of the weak texture detection of Lampshade 1; fig. 3(d) is a final disparity map of Lampshade 1; fig. 3(e) is a disparity map in which Lampshade1 does not perform weak texture detection.

In FIG. 4, FIG. 4(a) is a left image of a Monochromatic original image; FIG. 4(b) is a Monochromatic real disparity map; FIG. 4(c) is the Monopoly weak texture detection result; FIG. 4(d) is a Monochromatic final disparity map; fig. 4(e) is a parallax map in which Monopoly does not perform weak texture detection.

In FIG. 5, FIG. 5(a) is a left view of the original image of the Plastic; FIG. 5(b) is a Plastic true disparity map; FIG. 5(c) shows the result of the weak texture test of Plastic; FIG. 5(d) is a final disparity map of Plastic; fig. 5(e) is a parallax map in which Plastic does not perform weak texture detection.

The parallax images in fig. 2(a) to 2(e), fig. 3(a) to 3(e), fig. 4(a) to 4(e), and fig. 5(a) to 5(e) are subjectively evaluated in terms of visual effect. In fig. 2 to 5(c), the black portion indicates the detected weak texture region, and the white portion indicates the texture region. Comparing the disparity maps, it can be seen that in the weak texture region, the disparity map result obtained by applying the algorithm of the present embodiment is much better than the disparity map result obtained by the algorithm without weak texture detection.

The method of the invention is evaluated from objective evaluation indexes.

Table 1 shows the false match rate for processing 4 image pairs of the midlineby image set with distinct weak texture regions using two algorithms.

TABLE 1

As can be seen from table 1, in the test result of processing the stereo matching image pair by the two algorithms, the algorithm mismatching rate of the image pair processed by the algorithm of the present embodiment is reduced by 5% compared to the algorithm without weak texture detection and segmentation. The algorithm of the embodiment can improve the matching accuracy of the weak texture region and obtain a better disparity map on the premise of ensuring the matching accuracy of the texture region.

Detailed description of the invention

The cross-scale cost aggregation stereo matching method based on weak texture detection specifically comprises the following steps of:

calculating the gradient value g (x, y) of the pixel point at the coordinate (x, y) of the left image and the gradient threshold value g_TComparing and judging whether the texture is a weak texture area, wherein the calculation formula is as follows:

g(x,y)＜g_T

in the formula, N (x, y) represents a window with a pixel (x, y) as the center, M represents the number of pixels in the window, and I (x, y) represents the gray scale value of the pixel.

In the method for cross-scale cost aggregation stereo matching based on weak texture detection, the calculating of the matching cost in the step b specifically includes:

calculating a left image I of a stereoscopic color image pair_LAnd a right image I_RThe matching cost C (p, d) is calculated by the formula:

C(p,d)＝(1-α)·C_AD(p,d)+α·(C_{grad_x}(p,d)+C_{grad_y}(p,d))

in the formula: p is a point in the left image, where i ═ R, G, B represent the three channels of the color image, respectively, and T_ADAnd T_gradCutoff thresholds representing color and gradient, respectively;representing the gradient operators of the picture in the x and y directions, respectively, α is a balance factor between the color difference and the gradient difference.

The cross-scale cost aggregation stereo matching method based on weak texture detection is characterized in that the cost aggregation in the step c specifically comprises the following steps:

wherein,representing the aggregated matching cost, wherein z is an expected optimization target value, W is a Gaussian filter kernel, N is a neighborhood window of a pixel p, q is a neighborhood pixel point of p, S ∈ {0, 1.., S } is a scale parameter, and when S is 0, C is the value⁰Representing the original scale matching cost of the image;an aggregate cost representing S +1 scales of the image;

in the formula, lambda is a regularization factor,by usingAn optimization objective function representing equation (11), andcomprises the following steps:

wherein, T_highAnd T_lowRespectively representing the texture region and the weak texture region detected in the text; c₁And C_1/2Respectively representing the matching cost of the original image scale and the half scale, carrying out Gaussian filtering by using windows with different sizes, and obtaining the final matching cost after fusion.

The cross-scale cost aggregation stereo matching method based on weak texture detection includes the following steps:

|D'_L(P)-D'_R(P-D'_R(P))|＜

D_LRC(P)＝min(D'(PL),D'(PR))

wherein, the left chart parallax value D 'of one point p in the parallax chart'_L(p) and Right View disparity value D'_R(p-D'_L(p)), a threshold for LRC; d '(PL) is the disparity value for the first non-occluded point on the left, and D' (PR) is the disparity value for the first non-occluded point on the right; WB (wideband weight division multiple Access)_pq(I_L) As a function of the left image, Δ c_pqAnd Δ s_pqRespectively the color difference and the spatial euclidean distance of points p and q in the left image,andadjustment parameters of color difference and distance difference respectively; d_w(p) a filtered image.

Claims (5)

1. A cross-scale cost aggregation stereo matching method based on weak texture detection is characterized by comprising the following steps:

step a, inputting two color images, wherein the two color images are a left image and a right image respectively, and performing weak texture detection and segmentation on the images by using gradient information of the left image;

b, calculating matching cost according to the color information and the gradient information of the left image and the right image;

step c, taking the weak texture detection and segmentation result in the step a as a reference, and carrying out inner scale and cross-scale cost aggregation based on Gaussian filtering;

d, calculating the parallax by adopting a win person total-taking strategy;

and e, adopting a left-right consistency detection method and a self-adaptive weight-based method to refine the parallax and output a parallax image.

2. The method for cross-scale cost aggregation stereo matching based on weak texture detection as claimed in claim 1, wherein the weak texture detection and segmentation of the picture in the step a specifically comprises:

calculating the gradient value g (x, y) of the pixel point at the coordinate (x, y) of the left image and the gradient threshold value g_TComparing and judging whether the texture is a weak texture area, wherein the calculation formula is as follows:

g(x,y)＜g_T

<mrow> <mi>g</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mn>1</mn> <mi>M</mi> </mfrac> <mo>&amp;CenterDot;</mo> <munder> <mo>&amp;Sigma;</mo> <mrow> <mo>(</mo> <mi>u</mi> <mo>,</mo> <mi>v</mi> <mo>)</mo> <mo>&amp;Element;</mo> <mi>N</mi> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> </munder> <mrow> <mo>(</mo> <mo>|</mo> <mi>I</mi> <mo>(</mo> <mrow> <mi>u</mi> <mo>,</mo> <mi>v</mi> </mrow> <mo>)</mo> <mo>-</mo> <mi>I</mi> <mo>(</mo> <mrow> <mi>u</mi> <mo>+</mo> <mn>1</mn> <mo>,</mo> <mi>v</mi> </mrow> <mo>)</mo> <mo>+</mo> <mo>|</mo> <mi>I</mi> <mo>(</mo> <mrow> <mi>u</mi> <mo>,</mo> <mi>v</mi> </mrow> <mo>)</mo> <mo>-</mo> <mi>I</mi> <mo>(</mo> <mrow> <mi>u</mi> <mo>,</mo> <mi>v</mi> <mo>+</mo> <mn>1</mn> </mrow> <mo>)</mo> <mo>|</mo> <mo>)</mo> </mrow> </mrow>

in the formula, N (x, y) represents a window with a pixel (x, y) as the center, M represents the number of pixels in the window, and I (x, y) represents the gray scale value of the pixel.

3. The method for cross-scale cost aggregation stereo matching based on weak texture detection as claimed in claim 1, wherein the calculating the matching cost in the step b specifically comprises:

calculating a left image I of a stereoscopic color image pair_LAnd a right image I_RThe matching cost C (p, d) is calculated by the formula:

C(p,d)＝(1-α)·C_AD(p,d)+α·(C_{grad_x}(p,d)+C_{grad_y}(p,d))

<mrow> <msub> <mi>C</mi> <mrow> <mi>A</mi> <mi>D</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>p</mi> <mo>,</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>=</mo> <mi>m</mi> <mi>i</mi> <mi>n</mi> <mrow> <mo>(</mo> <mfrac> <mn>1</mn> <mn>3</mn> </mfrac> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mi>R</mi> <mo>,</mo> <mi>G</mi> <mo>,</mo> <mi>B</mi> </mrow> </munder> <mo>|</mo> <msubsup> <mi>I</mi> <mi>L</mi> <mi>i</mi> </msubsup> <mo>(</mo> <mi>p</mi> <mo>)</mo> <mo>-</mo> <msubsup> <mi>I</mi> <mi>R</mi> <mi>i</mi> </msubsup> <mo>(</mo> <mrow> <mi>p</mi> <mo>,</mo> <mi>d</mi> </mrow> <mo>)</mo> <mo>|</mo> <mo>,</mo> <msub> <mi>T</mi> <mrow> <mi>A</mi> <mi>D</mi> </mrow> </msub> <mo>)</mo> </mrow> </mrow>

C_{grad_x}(p,d)＝min(|▽_xI_L(p)-▽_xI_R(p,d)|,T_grad)

C_{grad_y}(p,d)＝min(|▽_yI_L(p)-▽_yI_R(p,d)|,T_grad)

in the formula: p is a point in the left image, where i ═ R, G, B represent the three channels of the color image, respectively, and T_ADAnd T_gradRepresenting the cut-off thresholds for color and gradient, respectively, ▽_x、▽_yRepresenting the gradient operators of the picture in the x and y directions, respectively, α is a balance factor between the color difference and the gradient difference.

4. The method for cross-scale cost aggregation stereo matching based on weak texture detection as claimed in claim 1, wherein the cost aggregation in the step c is specifically:

<mrow> <mover> <mi>C</mi> <mo>~</mo> </mover> <mrow> <mo>(</mo> <mi>p</mi> <mo>,</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>=</mo> <munder> <mi>argmin</mi> <mi>z</mi> </munder> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>q</mi> <mo>&amp;Element;</mo> <mi>N</mi> </mrow> </munder> <mi>W</mi> <mrow> <mo>(</mo> <mi>p</mi> <mo>,</mo> <mi>q</mi> <mo>)</mo> </mrow> <mo>|</mo> <mo>|</mo> <mi>z</mi> <mo>-</mo> <mi>C</mi> <mrow> <mo>(</mo> <mi>q</mi> <mo>,</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>|</mo> <msup> <mo>|</mo> <mn>2</mn> </msup> </mrow>

<mrow> <mover> <mi>C</mi> <mo>~</mo> </mover> <mrow> <mo>(</mo> <mi>p</mi> <mo>,</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>=</mo> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>q</mi> <mo>&amp;Element;</mo> <mi>N</mi> </mrow> </munder> <mi>W</mi> <mrow> <mo>(</mo> <mi>p</mi> <mo>,</mo> <mi>q</mi> <mo>)</mo> </mrow> <mo>&amp;CenterDot;</mo> <mi>C</mi> <mrow> <mo>(</mo> <mi>q</mi> <mo>,</mo> <mi>d</mi> <mo>)</mo> </mrow> </mrow>

<mrow> <mover> <mi>v</mi> <mo>~</mo> </mover> <mo>=</mo> <munder> <mrow> <mi>a</mi> <mi>r</mi> <mi>g</mi> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> <msubsup> <mrow> <mo>{</mo> <msup> <mi>z</mi> <mi>s</mi> </msup> <mo>}</mo> </mrow> <mrow> <mi>s</mi> <mo>=</mo> <mn>0</mn> </mrow> <mi>S</mi> </msubsup> </munder> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>s</mi> <mo>=</mo> <mn>0</mn> </mrow> <mi>S</mi> </munderover> <munder> <mo>&amp;Sigma;</mo> <mrow> <msup> <mi>q</mi> <mi>s</mi> </msup> <mo>&amp;Element;</mo> <msub> <mi>N</mi> <mi>s</mi> </msub> </mrow> </munder> <mi>W</mi> <mrow> <mo>(</mo> <msup> <mi>p</mi> <mi>s</mi> </msup> <mo>,</mo> <msup> <mi>q</mi> <mi>s</mi> </msup> <mo>)</mo> </mrow> <mo>|</mo> <mo>|</mo> <msup> <mi>z</mi> <mi>s</mi> </msup> <mo>-</mo> <msup> <mi>C</mi> <mi>s</mi> </msup> <mrow> <mo>(</mo> <msup> <mi>q</mi> <mi>s</mi> </msup> <mo>,</mo> <msup> <mi>d</mi> <mi>s</mi> </msup> <mo>)</mo> </mrow> <mo>|</mo> <msup> <mo>|</mo> <mn>2</mn> </msup> </mrow>

wherein,representing the aggregated matching cost, wherein z is an expected optimization target value, W is a Gaussian filter kernel, N is a neighborhood window of a pixel p, q is a neighborhood pixel point of p, S ∈ {0, 1.., S } is a scale parameter, and when S is 0, C is the value⁰Representing the original scale matching cost of the image;an aggregate cost representing S +1 scales of the image;

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<mrow> <mover> <mi>v</mi> <mo>~</mo> </mover> <mo>=</mo> <munder> <mrow> <mi>a</mi> <mi>r</mi> <mi>g</mi> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> <msubsup> <mrow> <mo>{</mo> <msup> <mi>z</mi> <mi>s</mi> </msup> <mo>}</mo> </mrow> <mrow> <mi>s</mi> <mo>=</mo> <mn>0</mn> </mrow> <mi>S</mi> </msubsup> </munder> <mrow> <mo>(</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>s</mi> <mo>=</mo> <mn>0</mn> </mrow> <mi>S</mi> </munderover> <munder> <mo>&amp;Sigma;</mo> <mrow> <msup> <mi>q</mi> <mi>s</mi> </msup> <mo>&amp;Element;</mo> <msub> <mi>N</mi> <mi>s</mi> </msub> </mrow> </munder> <mi>W</mi> <mo>(</mo> <mrow> <msup> <mi>p</mi> <mi>s</mi> </msup> <mo>,</mo> <msup> <mi>q</mi> <mi>s</mi> </msup> </mrow> <mo>)</mo> <mo>|</mo> <mo>|</mo> <msup> <mi>z</mi> <mi>s</mi> </msup> <mo>-</mo> <msup> <mi>C</mi> <mi>s</mi> </msup> <mo>(</mo> <mrow> <msup> <mi>q</mi> <mi>s</mi> </msup> <mo>,</mo> <msup> <mi>d</mi> <mi>s</mi> </msup> </mrow> <mo>)</mo> <mo>|</mo> <msup> <mo>|</mo> <mn>2</mn> </msup> <mo>+</mo> <mi>&amp;lambda;</mi> <mo>&amp;CenterDot;</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>s</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>S</mi> </munderover> <mo>|</mo> <mo>|</mo> <msup> <mi>z</mi> <mi>s</mi> </msup> <mo>-</mo> <msup> <mi>z</mi> <mrow> <mi>s</mi> <mo>-</mo> <mn>1</mn> </mrow> </msup> <mo>|</mo> <msup> <mo>|</mo> <mn>2</mn> </msup> <mo>)</mo> </mrow> </mrow>

in the formula, lambda is a regularization factor,by usingAn optimization objective function representing equation (11), andcomprises the following steps:

<mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mo>(</mo> <mn>1</mn> <mo>+</mo> <mi>&amp;lambda;</mi> <mo>)</mo> <msup> <mi>z</mi> <mi>s</mi> </msup> <mo>-</mo> <msup> <mi>&amp;lambda;z</mi> <mrow> <mi>s</mi> <mo>+</mo> <mn>1</mn> </mrow> </msup> <mo>=</mo> <msup> <mover> <mi>C</mi> <mo>~</mo> </mover> <mi>s</mi> </msup> <mo>(</mo> <msup> <mi>p</mi> <mi>s</mi> </msup> <mo>,</mo> <msup> <mi>d</mi> <mi>s</mi> </msup> <mo>)</mo> <mo>,</mo> <mi>s</mi> <mo>=</mo> <mn>0</mn> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>-</mo> <msup> <mi>&amp;lambda;z</mi> <mrow> <mi>s</mi> <mo>-</mo> <mn>1</mn> </mrow> </msup> <mo>+</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>+</mo> <mn>2</mn> <mi>&amp;lambda;</mi> <mo>)</mo> </mrow> <msup> <mi>z</mi> <mi>s</mi> </msup> <mo>-</mo> <msup> <mi>&amp;lambda;z</mi> <mrow> <mi>s</mi> <mo>+</mo> <mn>1</mn> </mrow> </msup> <mo>=</mo> <msup> <mover> <mi>C</mi> <mo>~</mo> </mover> <mi>s</mi> </msup> <mrow> <mo>(</mo> <msup> <mi>p</mi> <mi>s</mi> </msup> <mo>,</mo> <msup> <mi>d</mi> <mi>s</mi> </msup> <mo>)</mo> </mrow> <mo>,</mo> <mi>s</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mi>S</mi> <mo>-</mo> <mn>1</mn> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>-</mo> <msup> <mi>&amp;lambda;z</mi> <mrow> <mi>s</mi> <mo>-</mo> <mn>1</mn> </mrow> </msup> <mo>+</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>+</mo> <mn>2</mn> <mi>&amp;lambda;</mi> <mo>)</mo> </mrow> <msup> <mi>z</mi> <mi>s</mi> </msup> <mo>=</mo> <msup> <mover> <mi>C</mi> <mo>~</mo> </mover> <mi>s</mi> </msup> <mrow> <mo>(</mo> <msup> <mi>p</mi> <mi>s</mi> </msup> <mo>,</mo> <msup> <mi>d</mi> <mi>s</mi> </msup> <mo>)</mo> </mrow> <mo>,</mo> <mi>s</mi> <mo>=</mo> <mi>S</mi> </mrow> </mtd> </mtr> </mtable> </mfenced>

<mrow> <mi>A</mi> <mover> <mi>v</mi> <mo>^</mo> </mover> <mo>=</mo> <mover> <mi>v</mi> <mo>~</mo> </mover> </mrow>

<mrow> <mi>A</mi> <mo>=</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <mrow> <mn>1</mn> <mo>+</mo> <mi>&amp;lambda;</mi> </mrow> </mtd> <mtd> <mrow> <mo>-</mo> <mi>&amp;lambda;</mi> </mrow> </mtd> <mtd> <mrow></mrow> </mtd> <mtd> <mrow></mrow> </mtd> <mtd> <mrow></mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>-</mo> <mi>&amp;lambda;</mi> </mrow> </mtd> <mtd> <mrow> <mn>1</mn> <mo>+</mo> <mn>2</mn> <mi>&amp;lambda;</mi> </mrow> </mtd> <mtd> <mrow> <mo>-</mo> <mi>&amp;lambda;</mi> </mrow> </mtd> <mtd> <mrow></mrow> </mtd> <mtd> <mrow></mrow> </mtd> </mtr> <mtr> <mtd> <mrow></mrow> </mtd> <mtd> <mo>...</mo> </mtd> <mtd> <mo>...</mo> </mtd> <mtd> <mrow></mrow> </mtd> <mtd> <mrow></mrow> </mtd> </mtr> <mtr> <mtd> <mrow></mrow> </mtd> <mtd> <mrow></mrow> </mtd> <mtd> <mo>...</mo> </mtd> <mtd> <mo>...</mo> </mtd> <mtd> <mrow></mrow> </mtd> </mtr> <mtr> <mtd> <mrow></mrow> </mtd> <mtd> <mrow></mrow> </mtd> <mtd> <mrow> <mo>-</mo> <mi>&amp;lambda;</mi> </mrow> </mtd> <mtd> <mrow> <mn>1</mn> <mo>+</mo> <mn>2</mn> <mi>&amp;lambda;</mi> </mrow> </mtd> <mtd> <mrow> <mo>-</mo> <mi>&amp;lambda;</mi> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow></mrow> </mtd> <mtd> <mrow></mrow> </mtd> <mtd> <mrow></mrow> </mtd> <mtd> <mrow> <mo>-</mo> <mi>&amp;lambda;</mi> </mrow> </mtd> <mtd> <mrow> <mn>1</mn> <mo>+</mo> <mi>&amp;lambda;</mi> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow>

<mrow> <mover> <mi>v</mi> <mo>^</mo> </mover> <mo>=</mo> <msup> <mi>A</mi> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msup> <mover> <mi>v</mi> <mo>~</mo> </mover> </mrow>

<mrow> <msub> <mover> <mi>C</mi> <mo>~</mo> </mover> <mrow> <mi>f</mi> <mi>i</mi> <mi>n</mi> <mi>a</mi> <mi>l</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>p</mi> <mo>,</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mi>T</mi> <mrow> <mi>h</mi> <mi>i</mi> <mi>g</mi> <mi>h</mi> </mrow> </msub> <mo>&amp;CenterDot;</mo> <munder> <mi>&amp;Sigma;</mi> <mrow> <mi>q</mi> <mo>&amp;Element;</mo> <mi>N</mi> </mrow> </munder> <msub> <mi>W</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>p</mi> <mo>,</mo> <mi>q</mi> <mo>)</mo> </mrow> <msub> <mi>C</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>q</mi> <mo>,</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>T</mi> <mrow> <mi>l</mi> <mi>o</mi> <mi>w</mi> </mrow> </msub> <mo>&amp;CenterDot;</mo> <munder> <mi>&amp;Sigma;</mi> <mrow> <mi>q</mi> <mo>&amp;Element;</mo> <mi>N</mi> </mrow> </munder> <msub> <mi>W</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>p</mi> <mo>,</mo> <mi>q</mi> <mo>)</mo> </mrow> <msub> <mi>C</mi> <mrow> <mn>1</mn> <mo>/</mo> <mn>2</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>q</mi> <mo>,</mo> <mi>d</mi> <mo>)</mo> </mrow> </mrow>

wherein, T_highAnd T_lowRespectively representing the texture region and the weak texture region detected in the text; c₁And C_1/2Respectively representing the matching cost of the original image scale and the half scale, carrying out Gaussian filtering by using windows with different sizes, and obtaining the final matching cost after fusion.

5. The method for cross-scale cost aggregation stereo matching based on weak texture detection as claimed in claim 1, wherein the disparity refinement in step e specifically comprises:

|D'_L(P)-D'_R(P-D'_R(P))|＜

D_LRC(P)＝min(D'(PL),D'(PR))

<mrow> <msub> <mi>D</mi> <mi>w</mi> </msub> <mrow> <mo>(</mo> <mi>p</mi> <mo>)</mo> </mrow> <mo>=</mo> <munder> <mo>&amp;Sigma;</mo> <mi>q</mi> </munder> <msub> <mi>WB</mi> <mrow> <mi>p</mi> <mi>q</mi> </mrow> </msub> <mrow> <mo>(</mo> <msub> <mi>I</mi> <mi>L</mi> </msub> <mo>)</mo> </mrow> <msub> <mi>D</mi> <mrow> <mi>L</mi> <mi>R</mi> <mi>C</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>q</mi> <mo>)</mo> </mrow> </mrow>

<mrow> <msub> <mi>WB</mi> <mrow> <mi>p</mi> <mi>q</mi> </mrow> </msub> <mo>=</mo> <mi>exp</mi> <mrow> <mo>(</mo> <mo>-</mo> <mfrac> <mrow> <msub> <mi>&amp;Delta;c</mi> <mrow> <mi>p</mi> <mi>q</mi> </mrow> </msub> </mrow> <msubsup> <mi>&amp;sigma;</mi> <mi>c</mi> <mn>2</mn> </msubsup> </mfrac> <mo>+</mo> <mfrac> <mrow> <msub> <mi>&amp;Delta;s</mi> <mrow> <mi>p</mi> <mi>q</mi> </mrow> </msub> </mrow> <msubsup> <mi>&amp;sigma;</mi> <mi>s</mi> <mn>2</mn> </msubsup> </mfrac> <mo>)</mo> </mrow> </mrow>

wherein, the left chart parallax value D 'of one point p in the parallax chart'_L(p) and Right View disparity value D'_R(p-D'_L(p)), a threshold for LRC; d '(PL) is the disparity value for the first non-occluded point on the left, and D' (PR) is the disparity value for the first non-occluded point on the right; WB (wideband weight division multiple Access)_pq(I_L) As a function of the left image, Δ c_pqAnd Δ s_pqRespectively the color difference and the spatial euclidean distance of points p and q in the left image,andadjustment parameters of color difference and distance difference respectively; d_w(p) a filtered image.