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弱纹理环境双目视觉稠密视差鲁棒估计方法
Alternative TitleRobust estimation method for dense disparity of binocular vision under textureless environment
杜英魁; 刘成; 田丹; 韩晓微; 原忠虎
Department机器人学研究室
Source Publication光学精密工程
ISSN1004-924X
2017
Volume25Issue:4Pages:554-562
Indexed ByEI ; CSCD
EI Accession number20172703864508
CSCD IDCSCD:5975770
Contribution Rank2
Funding Organization辽宁省高等学校创新团队资助项目(LT2013024) ; 机器人学国家重点实验室开放基金资助项目(2015008) ; 辽宁省自然科学基金资助项目(2015020158) ; 辽宁省博士科研启动基金项目(201601213)
Keyword弱纹理环境 双目视觉 视差估计 置信度传播 参数空间投票
Abstract精确稠密视差估计是立体视觉系统恢复观测场景三维信息的关键。从立体视觉在机器人环境感知的实际应用角度出发,提出了对于弱纹理、阴影和遮挡等关键影响因素,具有良好鲁棒性、精度和处理速度的稠密视差图估计算法。针对弱纹理、阴影和深度不连续的问题,设计了基于灰度相似度概率的置信度传播算法,结合视差平滑约束,以期实现较高精度的视差初值快速估计。由视差级数定义的消息向量通过异向平行迭代进行传播,消息向量包含表征像素点灰度相似性和平滑性的能量信息,通过全局能量函数的迭代收敛,快速获得视差初始估计。根据独立连通区域通常具有相似纹理特征和视差一致性的先验知识,提出了基于Mean-Shift聚类分割算法和参数空间投票...
Other AbstractPrecise dense disparity estimation is the key for stereo visual system to recover three-dimensional information of observation scene. From practical application perspective of stereo vision in robot environment perception, a dense disparity figure estimation algorithm having good robustness, accuracy and processing speed to key influence factors (texturelessness, shadow and blocking etc.) was proposed. Aimed at texturelessness, shadow and uncontinuous, belief propagation algorithm based on gray-scale similarity probability had been designed to realize rapid and accurate estimation of initial value of disparity by combining with disparity smoothness constraint. The message vector defined by disparity class was propagated through anisotropic diffusion and parallel iteration. Message vector included energy information representing gray-scale similarity and smoothness of pixel point. Initial estimation of disparity could be gained rapidly through iteration convergence of global energy function. According to the priori knowledge that independent connected area generally had similar textural features and disparity conformance, parameter space voting self-adaption disparity approximation surface estimation algorithm on the basis of Mean-Shift clustering partitioning algorithm was proposed to perform fine optimization estimation of dense disparity. 5 groups of standard test image having different textureless features, 4 groups of actual image under indoor environment, 4 groups of actual image under outdoor environment and 4 groups of actual environment image under special lighting environment through selenographic simulation were utilized to perform test experiment and experimental result shows that the proposed algorithm has good robustness and effectiveness. © 2017, Science Press.
Language中文
Citation statistics
Cited Times:1[CSCD]   [CSCD Record]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/20775
Collection机器人学研究室
Corresponding Author杜英魁
Affiliation1.沈阳大学信息工程学院
2.中国科学院沈阳自动化研究所机器人学国家重点实验室
Recommended Citation
GB/T 7714
杜英魁,刘成,田丹,等. 弱纹理环境双目视觉稠密视差鲁棒估计方法[J]. 光学精密工程,2017,25(4):554-562.
APA 杜英魁,刘成,田丹,韩晓微,&原忠虎.(2017).弱纹理环境双目视觉稠密视差鲁棒估计方法.光学精密工程,25(4),554-562.
MLA 杜英魁,et al."弱纹理环境双目视觉稠密视差鲁棒估计方法".光学精密工程 25.4(2017):554-562.
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