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Improved census stereo matching algorithm based on window cross-correlation information

A stereo matching and cross-correlation technology, applied in computing, computer components, image analysis, etc., can solve problems such as noise suppression defects, stereo matching algorithm stability discount, etc., and achieve high matching accuracy

Active Publication Date: 2022-04-08
TIANJIN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

External noise, light and other interference are not easy to affect the relative size of the gray value of each point in the window, so it has strong stability and anti-interference ability, but if the gray value of the central pixel is greatly disturbed, the three-dimensional The stability of the matching algorithm will be greatly reduced
Traditional non-parametric transformations mainly include rank transformation and Census transformation, among which Census transformation is more typical and widely used, but it has defects in noise suppression and image details.

Method used

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  • Improved census stereo matching algorithm based on window cross-correlation information
  • Improved census stereo matching algorithm based on window cross-correlation information
  • Improved census stereo matching algorithm based on window cross-correlation information

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Experimental program
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Embodiment 1

[0073] Fig. 3 (a) and Fig. 3 (b) are four groups of standard reference color stereo images of Tsukuba, Teddy, Venus, Cones provided by the authoritative stereo matching algorithm testing platform Middlebury website to it, the present invention uses these four images to match Algorithms are evaluated. The sizes of the four image pairs are 384×288, 450×375, 434×383, and 450×375 pixels, respectively, and the left and right images have been calibrated and corrected. The first to fourth columns are respectively: the left image, the right image, the standard disparity map, the result map of the present invention, and the mismatch point label map.

[0074] It can be seen intuitively from Fig. 3(c) and Fig. 3(d) that the disparity map optimized by the present invention is relatively close to the standard disparity map as a whole, but there are still obvious differences in local details. After marking the mismatching points, it can be clearly seen that the four groups of mismatching p...

Embodiment 2

[0076] For DP (dynamic programming) Fig. 4 (c), AdaptWeight (adaptive weight) Fig. 4 (d), traditional Census Fig. 4 (e), SAD-Census Fig. 4 (f) and the present invention Fig. 4 (g) five The disparity maps generated by the two stereo matching algorithms are compared intuitively. It can be clearly seen that the DP algorithm has a greater disadvantage than other algorithms, and it is easy to cause image distortion and serious loss of edge information. Although the AdaptWeight algorithm has better edge details, there are many parallax holes in the disparity map, and it is necessary to use nearby reliable disparity values ​​to fill the holes later. As a kind of non-parametric transformation, the traditional Census transformation has improved, but the overall effect is still poor. The SAD-Census algorithm further overcomes the noise interference and has richer edge information, but there are more mismatching points in the areas where the image depth changes greatly or the texture in...

Embodiment 3

[0078] By calculating the false matching rates of four test image pairs in Middlebury in different regions, and comparing them with the false matching rates of four stereo matching algorithms: DP, AdaptWeight, traditional Census and SAD-Census. As shown in Table 1 below, Nocc is the false matching rate of non-occluded areas, All is the false matching rate of all areas, Disc is the false matching rate of depth discontinuous areas, and Average represents the average false matching rate. The false match rate refers to the ratio of the calculated parallax value to the real pixel whose error is greater than the parallax tolerance in the entire image, which can be expressed as:

[0079]

[0080] In the above formula, N represents the total number of pixels in the disparity map; g x (x,y) and g s (x, y) represent the disparity value obtained by the test algorithm and the real disparity value respectively, δ thresh Indicates the parallax tolerance, the value is 1.

[0081] It ca...

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Abstract

The invention discloses an improved Census stereo matching algorithm based on window cross-correlation information, comprising the following steps: 1) using a window to traverse the left image captured by a binocular camera, and taking the average value of the gray value of each pixel in the window as a reference value; 2) On the basis of the reference value, a noise threshold is added as another constraint condition, and Census transformation is performed; 3) SAD‑Census is used to calculate the weighted matching cost, and the corresponding matching is found in the right image captured by the binocular camera The window with the smallest value. The invention improves the robustness of the algorithm, reduces the noise interference, enriches the detail information of the image, reduces the false matching rate, and has obvious improvement effect in the depth discontinuous area.

Description

technical field [0001] The invention relates to the field of digital image processing, in particular to an improved Census stereo matching algorithm based on window mutual correlation information. Background technique [0002] The acquisition of distance information can be done through binocular vision, and binocular stereo matching is the core and key of binocular stereo vision. The stereo matching algorithm first needs to construct an energy cost function, and then complete the estimation of the pixel disparity value by solving the minimum value of the energy cost function, obtain the disparity map, and obtain the depth information [see: Song Wei, Na Pengyu, Liu Ningning. Research on Moving Target Tracking Algorithm Based on Binocular Vision System [J]. Journal of Beijing Jiaotong University, 2013, 37(6): 13-17]. The essence of the stereo matching algorithm is to obtain the optimal value of the function, set an appropriate energy cost function, and add constraints, and fi...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/33G06V10/74G06K9/62
CPCG06T7/33G06F18/22
Inventor 薛俊韬黑俊杰
Owner TIANJIN UNIV