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基于HSV颜色空间的自适应窗口局部匹配算法

Adaptive Window Local Matching Algorithm Based on HSV Color Space

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摘要

为提高立体匹配算法的效果和稳定性, 提出了一种基于色调(H)、饱和度(S)和明度(V)颜色空间的自适应聚合区域的引导滤波算法。结合图片的结构和纹理信息, 通过颜色和横向梯度的相互作用计算初始匹配代价。在HSV颜色空间中运用颜色和距离信息计算每一点的自适应支撑臂长, 解决了图片中红、绿、蓝3种颜色变化趋势相近导致无法有效反映图片信息的问题。自适应聚合区域利用中心点纵向臂上各点的横向臂进行构造, 采用引导滤波的方法在自适应聚合区域内聚合代价空间。为避免中心点邻域信息波动造成支撑窗口过小的问题, 设置了臂长的最小范围。后处理过程采用左右一致性检测结合峰比率检测的方法寻找误匹配点, 通过近邻点匹配和加权中值滤波的方法修正视差图。采用Middlebury平台上的标准图片进行实验, 结果表明所提算法的平均匹配误差为5.24%, 比改进前的自适应窗口算法的匹配误差降低了0.92%, 具有更好的边缘保持效果, 算法参数稳健性较好。

Abstract

In order to improve the effect and stability of stereo matching algorithm, we propose a guided filtering algorithm based on adaptive aggregation region in Hue-Saturation-Value (HSV) color space. We calculate initial matching cost by the interaction of color and transverse gradient in combination with the structure and texture information of the image. Then, we calculate the length of the adaptive support arm of each point based on the color and distance information in HSV color space, which solves the problem that the change trend of the red, green and blue colors in the picture is similar and cannot effectively reflect the picture information. We construct the adaptive aggregation region using a transverse arm at each point on the longitudinal arm of the center point, and aggregate the cost space in the adaptive aggregation region by guided filtering. In order to avoid the problem that the support window is too small due to the fluctuation of the neighborhood information, we set the minimum range of the arm length. In the process of post-processing, we use the left and right consistency detection and the peak ratio detection method to find mismatching points, and correct the disparity map by the nearest neighbor matching and weighted median filtering. The experiments are carried out on standard images of Middlebury platform. Results show that average matching error of the proposed algorithm is 5.24%, and the matching error is reduced by 0.92% compared with the that of pre-improvement adaptive window algorithm. The proposed algorithm has better edge preservation effect and the robustness of the algorithm parameters is good.

Newport宣传-MKS新实验室计划
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中图分类号:TP394.41

DOI:10.3788/lop55.031103

所属栏目:成像系统

收稿日期:2017-09-13

修改稿日期:2017-10-11

网络出版日期:--

作者单位    点击查看

苏修:天津大学精密仪器与光电子工程学院光电信息技术教育部重点实验室, 天津 300072
陈晓冬:天津大学精密仪器与光电子工程学院光电信息技术教育部重点实验室, 天津 300072
徐怀远:天津大学精密仪器与光电子工程学院光电信息技术教育部重点实验室, 天津 300072
梁海涛:天津大学精密仪器与光电子工程学院光电信息技术教育部重点实验室, 天津 300072
刘依林:天津大学精密仪器与光电子工程学院光电信息技术教育部重点实验室, 天津 300072
汪毅:天津大学精密仪器与光电子工程学院光电信息技术教育部重点实验室, 天津 300072
李伟锋:天津大学精密仪器与光电子工程学院光电信息技术教育部重点实验室, 天津 300072
郁道银:天津大学精密仪器与光电子工程学院光电信息技术教育部重点实验室, 天津 300072

联系人作者:陈晓冬(xdchen@tju.edu.cn)

备注:苏修(1994-), 男, 硕士研究生, 主要从事三维图像处理方面的研究。E-mail: suxiu@tju.edu.cn

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引用该论文

Su Xiu,Chen Xiaodong,Xu Huaiyuan,Liang Haitao,Liu Yilin,Wang Yi,Li Weifeng,Yu Daoyin. Adaptive Window Local Matching Algorithm Based on HSV Color Space[J]. Laser & Optoelectronics Progress, 2018, 55(3): 031103

苏修,陈晓冬,徐怀远,梁海涛,刘依林,汪毅,李伟锋,郁道银. 基于HSV颜色空间的自适应窗口局部匹配算法[J]. 激光与光电子学进展, 2018, 55(3): 031103

被引情况

【1】张淑芳,朱彤. 一种基于HDR技术的交通标志牌检测和识别方法. 激光与光电子学进展, 2018, 55(9): 91006--1

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