基于图像分割的自适应窗口双目立体匹配算法研究

doi:10.11896/jsjkx.201200264

摘要/Abstract

摘要： 针对传统双目立体匹配算法采用固定窗口导致弱纹理区域匹配精度较低的问题,提出了一种基于图像分割的自适应窗口立体匹配算法。首先,采用Mean-shift算法对图像进行分割,之后对分割图像进行局部子区灰度标准差统计,在此基础上提出了一种根据纹理丰富程度进行窗口大小自适应设定的算子。基于自适应窗口大小设定,组合使用Census变换和梯度值计算匹配代价,并分别通过自适应权重代价聚合及“胜者为王”策略进行初始视差计算,最后利用左右视差一致性原则和加权中值滤波得到稠密视差图。采用提出的自适应窗口匹配算法与固定窗口匹配算法对Middlebury数据集上的标准图片进行匹配实验,实验结果表明,所提算法的平均匹配错误率为2.04%,相比对比算法,所提方法的匹配错误率分别降低了4.5%和7.9%。

关键词: 立体匹配, 弱纹理, 图像分割, 自适应窗口, 自适应权重

Abstract: Aiming at the problem that the traditional binocular stereo matching algorithm uses fixed window,which leads to low matching accuracy in weak texture regions,an adaptive window stereo matching algorithm based on image segmentation is proposed.Firstly,the mean shift algorithm is used to segment the image,and then the gray standard deviation of local sub regions is calculated.Based on this,an adaptive window size setting operator is proposed according to the texture richness.Based on the adaptive window size setting,the matching cost is calculated by combining census transform and gradient value,and the initial disparity is calculated by adaptive weight cost aggregation and “winner takeall” strategy respectively.Finally,the dense disparity map is obtained by using the principle of left and right disparity consistency and weighted median filtering.The adaptive window matching algorithm and fixed window matching algorithm proposed in this paper are used to match standard images on Middlebury dataset.The experimental results show that the average matching error rate of the proposed algorithm is 2.04%,which is 4.5% and 7.9% lower than that of the contrast algorithm.

Key words: Adaptive weight, Adaptive window, Image segmentation, Stereo matching, Weak texture

中图分类号:

TP311.5

曹林, 于威威. 基于图像分割的自适应窗口双目立体匹配算法研究[J]. 计算机科学, 2021, 48(11A): 314-318. https://doi.org/10.11896/jsjkx.201200264

CAO Lin, YU Wei-wei. Adaptive Window Binocular Stereo Matching Algorithm Based on Image Segmentation[J]. Computer Science, 2021, 48(11A): 314-318. https://doi.org/10.11896/jsjkx.201200264

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