Improved Feature Stereo Matching Method Based on Binocular Vision

WANG Di; HU Liao-lin

doi:10.12263/DZXB.20200919

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Improved Feature Stereo Matching Method Based on Binocular Vision

PAPERS | 更新时间：2025-12-08

- Improved Feature Stereo Matching Method Based on Binocular Vision
- ACTA ELECTRONICA SINICA Vol. 50, Issue 1, Pages: 157-166(2022)
- 作者机构：
  
  西安理工大学机械与精密仪器工程学院, 陕西西安 710048
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20200919
  CLC： TP391;
- Received：21 August 2020，
  
  Revised：2021-01-04，
  
  Published：25 January 2022
- 稿件说明：
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王笛,胡辽林.基于双目视觉的改进特征立体匹配方法[J].电子学报,2022,50(01):157-166.

WANG Di,HU Liao-lin.Improved Feature Stereo Matching Method Based on Binocular Vision[J].ACTA ELECTRONICA SINICA,2022,50(01):157-166.
王笛,胡辽林.基于双目视觉的改进特征立体匹配方法[J].电子学报,2022,50(01):157-166. DOI： 10.12263/DZXB.20200919.

WANG Di,HU Liao-lin.Improved Feature Stereo Matching Method Based on Binocular Vision[J].ACTA ELECTRONICA SINICA,2022,50(01):157-166. DOI： 10.12263/DZXB.20200919.

摘要

针对特征立体匹配方法只能得到稀疏视差以及弱纹理匹配率低以及视差精度不足导致视差连续处不平滑而呈阶梯状等问题，提出一种改进的特征立体匹配算法. 对预处理后的左右图像提取特征并进行特征匹配，再经过筛选获取准确的匹配点对；将得到的稀疏匹配点对作为种子点，依据视差连续性与极线约束准则建立一维搜索空间，利用积分图简化的快速零均值归一化互相关系数作为相似度量函数，通过双向匹配策略实现区域生长，大大提升匹配准确性的同时降低算法复杂度；通过亚像素拟合和加权中值滤波后处理提升视差精度，去除视差阶梯分层、噪声和条纹现象. Middlebury数据集实验结果表明，本算法得到了准确性更高且稠密的视差，提高了弱纹理区与深度不连续处的匹配效果以及整体视差精度，同时具有很强的鲁棒性，能抑制一定亮度差异和噪声的影响.

Abstract

The feature-based stereo matching method can only get sparse disparity

and the low matching rate of weak texture areas and insufficient disparity accuracy lead to problems such as unsmooth continuity and stepped disparity. An improved features stereo matching algorithm is proposed. Extract features from the left and right images after preprocessing and perform feature matching

and then filter to obtain accurate matching point pairs; The obtained sparse matching point pairs are used as seed points

the search space is established according to the disparity continuity and the extreme line constraint criterion

and the fast zero-mean normalized cross-correlation simplified by integral graph is used as the similarity measurement function to achieve region growth through a two-way matching strategy

which greatly improves matching accuracy and reduces matching complexity; Sub-pixel fitting and weighted median filtering are used to improve the accuracy of disparity

remove disparity step layering

noise and streaks. The experimental results of the Middlebury data set show that this algorithm obtains a highly accurate and dense disparity

improves the matching effect of weak texture areas and depth discontinuities

and the accuracy of disparity. At the same time

it has strong robustness and can suppress the influence of certain brightness differences and noise.

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Keywords

references

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