自主移动机器人的运动规划与图像理解研究
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摘要
智能移动机器人的研究体现多学科交叉领域的综合智慧,对它的研究和应用受到国内外学者的高度关注。基于视觉的低层次匹配感知、中层次检测规划和高层次辨识理解是机器人实现真正智能化的关键技术,是最具有挑战性的研究课题之一。作为复杂的系统性问题,立体匹配、运动规划和图像理解需要综合考虑图像的感知表达、行为的规划执行和知识的学习推理所采用的具体技术。本文面向机器人视觉研究了从低层次到高层次的算法和应用。首先概述了相关技术的国内外研究现状,阐述了目前研究的重点问题以及技术发展趋势。然后从三个方面概述本文解决问题的思路：第一方面,面向立体匹配和检测,从匹配代价和优化算法出发,提出兼顾匹配效率和性能的匹配算法,随后设计障碍物检测方法；第二方面,面向运动规划和行为,提出复杂环境下混合式集成交互结构和各模块的技术实现；第三方面,面向目标识别和分割,在融合式特征构建基础上,提出层次式知识推理的目标识别框架和模型推理过程。最后本文通过大量真实场景下的实验分析了所提方法的性能。
从体现感知检测的智能角度,面向基于立体视觉的障碍物检测,本文构造了由粗到精的分层视差偏移框架提高匹配算法效率。设计了自适应二重加权聚合环节提高代价衡量的合理性。在此基础上,提出双向多映射动态规划方法,融合递推过程的不一致性。每向迭代过程同时引入了水平和垂直约束优化,采用多路近优的映射转移结构,提高了后续回溯的全局性。在高可靠控制点指导的寻优过程中,设计的能量函数一方面融入了惩罚和奖励因子,另一方面集成了多扫描行约束的全局信息。横向和纵向比较实验表明所提匹配算法兼顾了精度和效率两方面性能。进而将其应用到障碍物检测中,本文提出了感知-验证结构的两阶段检测方法,真实场景的实验验证了该方法的有效性和实用性。
从体现运动行为的智能角度,面向未知、动态、混杂场景下的运动规划,本文从模块间集成机制和各模块实现技术方面设计了一种混合式运动规划方法。通过综合慎思式智能和反应式评估反馈,提出协商-选择双向交互的集成机制。在慎思式模块中,设计了多层状态格结构和控制集切换机制,构建了弹性的配置空间。进而为加速慎思式产生效率,提出了基于后备树启发式更新结构的多任务并行的搜索算法。在反应式模块中,设计了分阶段评估的动态优化策略以及依赖形势的调整方式,采用分级结构集成这两种模式。在Pioneer 3DX移动机器人平台上的大量实验验证了复杂场景下该方法的有效性、可靠性和鲁棒性。
从体现认知识别的智能角度,面向机器人的图像理解应用,本文在分析了图像特征描述的基础上,通过关键点多刻度多方向的表征并配合PCA降维,构建了一种局部不变特征描述符。基于此特征,以串行融合方式集成多刻度方向梯度直方图综合地表征了局部外观描述。随后,以并行融合方式集成全局空间纹理结构构成了混合式特征观测器。进一步从判别式推理模型方面,提出了一种层叠式条件随机场框架。通过训练的分配函数模型自动由低层随机场节点组建高层随机场节点,表征局部不同级别部件的空间关系。此外,高层随机场的输入增加了置信集的观测,并同时构建了类别共现、相对位置和相对刻度上下文关系。从多类的目标检测和目标分割两个方面,在大量真实场景图像上实验,并通过与PASCAL挑战赛上的代表性方法比较,定量和定性地验证了该方法的性能改善。
Autonomous mobile robot embodies a comprehensive intelligence covering multiple subjects, so related research and applications have attracted increasing attention. Robot vision is a challenging issue in the field of robotics. Research on it is divided into three levels: perception level, application level and cognitive level. Corresponding to the three levels, this dissertation focuses on stereo matching algorithm, motion planning method, and image understanding solution. First of all, this dissertation describes progresses in stereo matching, motion planning and image understanding, and presents key technical issues and development trends. And then, the main contributions are summarized from three aspects. Towards matching and perception, the first is to propose a stereo matching algorithm, which gives consideration to matching efficiency and performance. Based on this, an obstacle detection method is designed. Towards planning and behavior, the second is to propose a hybrid interaction mechanism and the the techniques implemented in each module. Towards reasoning and recognization, the third is to construct a fused feature and then to propose a cascaded framework of inference. Finally, extensive practical experiments are used to verify the proposed methods.
From the perspective of perception intelligence, a hierarchical stereo maching algorithm for obstacle detection in the pyramid disparity-offset space is presented. An adaptive dual weighted cost aggregation is designed to improve the rationality of cost calculation. Based on this, bidirectional dynamic programming with multiple transitions structure is presented to integrate the inconsistency in recursive processes. In the forward or backward step, horizontal and vertical optimizations are considered simutanously, and multiple almost-optimal transitions structure is used to multi-candidate backtrack. In the optimization process, ground control points are not only adopted, but both global information of multiple scanline constraints and punitive and incentive measures are integrated into a unified energy function. A series of experiments and comparisons verify both accuracy and efficiency of our algorithm. Furthermore, a two-stage approach with perception-confirmation structure is proposed to detect obstacles. Experiments based on real robot platform in different environments validate effectiveness and practicability of the proposed method.
From the perspective of behavior intelligence, an interactive mechanism and modular approaches are proposed for hybrid motion planning in unknown, dynamic and cluttered environments. A bidirectional interaction is designed by deliberative candidates negotiating with the feedback on reactive evaluation. In the deliberative module, a multilayer structure and a switching mode in control sets are designed to construct a concise and flexible state lattices. Furthermore, a multitask-parallel algorithm is proposed to heuristically construct a search tree of the reachable graph to improve search efficiency. In the reactive module, a hierarchical structure is designed to integrate the reaction optimization and situation-dependent adjustment. Based on manifold correlations, piecewise criterions rather than a single function are proposed to cater to different stages of planning. Extensive experiments using Pioneer 3DX platform verify the efficacy, reliability and robustness of our approach in complex environments.
From the perspective of cognitive intelligence, a hybrid fused feature detector and cascaded discriminative framework are proposed for image understanding. A local invariant feature is extracted by multiscale and multi-orientation description and dimension reduction. Based on this feature, in serial fusion mode, multiscale histogram of gradient is integrated to comprehensively characterize the local appearance description. Subsequently, in parallel fusion mode, spatial texture structure is integrated to construct a hybrid feature description. Furthermore, in terms of reasoning using discriminative model, a cascaded conditional random field is presented. Some nodes of low layer are adaptively aggregated to form the node of high layer using the trained partition model. This structure can represent local spatial relationships of different levels of components. In addition, the confidence set in low layer is added to the input of node in the high layer. Moreover, the pairwise potential of high layer incorporates contextual information, including coocurrence, relative spatial location, and relative scale, simultaneously. From multi-class object detection and segmentation aspects, extensive experiments in real scene images and comparison with representative methods in PASCAL VOC Challenge verify that our method achieves significant improvement.
从体现感知检测的智能角度,面向基于立体视觉的障碍物检测,本文构造了由粗到精的分层视差偏移框架提高匹配算法效率。设计了自适应二重加权聚合环节提高代价衡量的合理性。在此基础上,提出双向多映射动态规划方法,融合递推过程的不一致性。每向迭代过程同时引入了水平和垂直约束优化,采用多路近优的映射转移结构,提高了后续回溯的全局性。在高可靠控制点指导的寻优过程中,设计的能量函数一方面融入了惩罚和奖励因子,另一方面集成了多扫描行约束的全局信息。横向和纵向比较实验表明所提匹配算法兼顾了精度和效率两方面性能。进而将其应用到障碍物检测中,本文提出了感知-验证结构的两阶段检测方法,真实场景的实验验证了该方法的有效性和实用性。
从体现运动行为的智能角度,面向未知、动态、混杂场景下的运动规划,本文从模块间集成机制和各模块实现技术方面设计了一种混合式运动规划方法。通过综合慎思式智能和反应式评估反馈,提出协商-选择双向交互的集成机制。在慎思式模块中,设计了多层状态格结构和控制集切换机制,构建了弹性的配置空间。进而为加速慎思式产生效率,提出了基于后备树启发式更新结构的多任务并行的搜索算法。在反应式模块中,设计了分阶段评估的动态优化策略以及依赖形势的调整方式,采用分级结构集成这两种模式。在Pioneer 3DX移动机器人平台上的大量实验验证了复杂场景下该方法的有效性、可靠性和鲁棒性。
从体现认知识别的智能角度,面向机器人的图像理解应用,本文在分析了图像特征描述的基础上,通过关键点多刻度多方向的表征并配合PCA降维,构建了一种局部不变特征描述符。基于此特征,以串行融合方式集成多刻度方向梯度直方图综合地表征了局部外观描述。随后,以并行融合方式集成全局空间纹理结构构成了混合式特征观测器。进一步从判别式推理模型方面,提出了一种层叠式条件随机场框架。通过训练的分配函数模型自动由低层随机场节点组建高层随机场节点,表征局部不同级别部件的空间关系。此外,高层随机场的输入增加了置信集的观测,并同时构建了类别共现、相对位置和相对刻度上下文关系。从多类的目标检测和目标分割两个方面,在大量真实场景图像上实验,并通过与PASCAL挑战赛上的代表性方法比较,定量和定性地验证了该方法的性能改善。
Autonomous mobile robot embodies a comprehensive intelligence covering multiple subjects, so related research and applications have attracted increasing attention. Robot vision is a challenging issue in the field of robotics. Research on it is divided into three levels: perception level, application level and cognitive level. Corresponding to the three levels, this dissertation focuses on stereo matching algorithm, motion planning method, and image understanding solution. First of all, this dissertation describes progresses in stereo matching, motion planning and image understanding, and presents key technical issues and development trends. And then, the main contributions are summarized from three aspects. Towards matching and perception, the first is to propose a stereo matching algorithm, which gives consideration to matching efficiency and performance. Based on this, an obstacle detection method is designed. Towards planning and behavior, the second is to propose a hybrid interaction mechanism and the the techniques implemented in each module. Towards reasoning and recognization, the third is to construct a fused feature and then to propose a cascaded framework of inference. Finally, extensive practical experiments are used to verify the proposed methods.
From the perspective of perception intelligence, a hierarchical stereo maching algorithm for obstacle detection in the pyramid disparity-offset space is presented. An adaptive dual weighted cost aggregation is designed to improve the rationality of cost calculation. Based on this, bidirectional dynamic programming with multiple transitions structure is presented to integrate the inconsistency in recursive processes. In the forward or backward step, horizontal and vertical optimizations are considered simutanously, and multiple almost-optimal transitions structure is used to multi-candidate backtrack. In the optimization process, ground control points are not only adopted, but both global information of multiple scanline constraints and punitive and incentive measures are integrated into a unified energy function. A series of experiments and comparisons verify both accuracy and efficiency of our algorithm. Furthermore, a two-stage approach with perception-confirmation structure is proposed to detect obstacles. Experiments based on real robot platform in different environments validate effectiveness and practicability of the proposed method.
From the perspective of behavior intelligence, an interactive mechanism and modular approaches are proposed for hybrid motion planning in unknown, dynamic and cluttered environments. A bidirectional interaction is designed by deliberative candidates negotiating with the feedback on reactive evaluation. In the deliberative module, a multilayer structure and a switching mode in control sets are designed to construct a concise and flexible state lattices. Furthermore, a multitask-parallel algorithm is proposed to heuristically construct a search tree of the reachable graph to improve search efficiency. In the reactive module, a hierarchical structure is designed to integrate the reaction optimization and situation-dependent adjustment. Based on manifold correlations, piecewise criterions rather than a single function are proposed to cater to different stages of planning. Extensive experiments using Pioneer 3DX platform verify the efficacy, reliability and robustness of our approach in complex environments.
From the perspective of cognitive intelligence, a hybrid fused feature detector and cascaded discriminative framework are proposed for image understanding. A local invariant feature is extracted by multiscale and multi-orientation description and dimension reduction. Based on this feature, in serial fusion mode, multiscale histogram of gradient is integrated to comprehensively characterize the local appearance description. Subsequently, in parallel fusion mode, spatial texture structure is integrated to construct a hybrid feature description. Furthermore, in terms of reasoning using discriminative model, a cascaded conditional random field is presented. Some nodes of low layer are adaptively aggregated to form the node of high layer using the trained partition model. This structure can represent local spatial relationships of different levels of components. In addition, the confidence set in low layer is added to the input of node in the high layer. Moreover, the pairwise potential of high layer incorporates contextual information, including coocurrence, relative spatial location, and relative scale, simultaneously. From multi-class object detection and segmentation aspects, extensive experiments in real scene images and comparison with representative methods in PASCAL VOC Challenge verify that our method achieves significant improvement.
引文
[1]Siegwart R, Nourbakhsh I R. Introduction to autonomous mobile robots [M]. Massachusetts:The MIT Press,2004.
[2]Pauli J. Learning-based robot vision:principles and applications [M].1st ed. Springer:Lecture Notes in Computer Science,2001.
[3]Davies E R. Machine vision theory, algorithms, practicalities [M]. San Fransisco: Morgan Kaufmann Publishers,2005.
[4]蔡自兴.机器人学(第二版)[M].北京：清华大学出版社,2009.
[5]Guilherme N D, Avinash C K. Vision for mobile robot navigation:A survey [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(2):237-267.
[6]Marr D. Vision:A computational investigation into the human representation and processing of visual information [M]. Massachusetts:The MIT Press,2010.
[7]Barnard S T, Fischler M A. Computational stereo [J]. ACM Computing Surveys,1982,14(4)：553-572.
[8]Dhond U R, Aggarwal J K. Structure from stereo-a review [J]. IEEE Transactions on Systems, Man, and Cybernetics,1989,19(6):1489-1510.
[9]Faugeras 0. What can be seen in three dimensions with an uncalibrated stereo rig [C]. Proceedings of the Second European Conference Computer Vision, Santa Margherita Ligure, Italy,1992:563-578.
[10]Koschan A. What is new in computational stereo since 1989:A survey of current stereo papers [R]. Technical Report 93-22, Germany:Technical University of Berlin,1993.
[11]Scharstein D, Szeliski R. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms [J]. International Jounal of Computer Vision,2002,47(1)：7-42.
[12]Brown M Z, Burschka D, Hager G D. Advances in computational stereo [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(8):993-1008.
[13]白明,庄严,王伟.双目立体匹配算法的研究与进展[J].控制与决策,2008,23(7)：721-729.
[14]Szeliski R, Zabih R, Scharstein D, et al. A comparative study of energy minimization methods for markov random fields with smoothness based priors [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2008,30(6):1068-1080.
[15]Birchfield S, Tomasi C. Depth discontinuities by pixel-to-pixel stereo [C]. Proceedings of IEEE International Conference of Computer Vision, Bombay, India, 1998:1073-1080.
[16]Cox I J, Hingorani S L, Rao S B, et al. A maximum likelihood stereo algorithm [J]. Computer Vision and Image Understanding,1996,63(3):542-567.
[17]BobickAF, IntilleSS. Large occlusion sereo [J]. International Journal of Computer Vision,1999,33(3):181-200.
[18]Gong M, Yang Y H. Fast unambiguous stereo matching using reliability-based dynamic programming [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005,27(6):998-1003.
[19]Lei C, Selzer J, Yang Y H. Region-tree based stereo using dynamic programming optimization [C]. Proceedings of IEEE Conference of Computer Vision and Pattern Recognition, New York, USA,2006:378-385.
[20]Cox I J, Roy S. A maximum-flow formulation of the N-camera stereo correspondence problem [C]. Proceedings of the Sixth International Conference on Computer Vision, Bombay, India,1998:492-499.
[21]Kolmogorov V, Zabin R. What energy functions can be minimized via graph cuts [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2004, 26 (2):147-159.
[22]Boykov Y, Veksler O, Zabih R. Fast approximate energy minimization via graph cuts [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2001, 23(11):1222-1239.
[23]王年,范益政,鲍文霞,等.基于图割的图像匹配算法[J].电子学报,2006,34(2)：232-236.
[24]Boykov Y, Kolmogorov V. An experimental comparison of min-cut/max-f low algorithms for energy minimization in vision [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,26(9):1124-1137.
[25]Wang Z F, Zheng Z G. A region based stereo matching algorithm using cooperative optimization [C]. Proceedings of 2008 IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA,2008:1-8.
[26]Min D, Sohn K. Cost aggregation and occlusion handling with WLS in stereo matching [J]. IEEE Transactions on Image Processing,2008,17(8):1431-1442.
[27]Sun J, Zheng N N, Shum H Y. Stereo matching using belief propagation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(7):787-800.
[28]Felzenszwalb P F, Huttenlocher D P. Efficient belief propagation for early vision [C]. Proceedings of IEEE Conference of Computer Vision and Pattern Recognition, Washington DC, USA,2004:261-268.
[29]Klaus A, Sormann M, Karner K. Segment-based stereo matching using belief propagation and a self-adapting dissimilarity measure [C]. Proceedings of the 18th International Conference on Pattern Recognition, Hong Kong, China,2006:15-18.
[30]Ruichek Y. Multilevel- and neural-network-based stereo-matching method for real-time obstacle detection using linear cameras [J]. IEEE Transactions on Intelligent Transportation Systems,2005,6(1):54-62.
[31]Hua X J, Yokomichi M, Kono M. Stereo correspondence using color based on competitive-cooperative neural networks [C]. Proceedings of the Sixth International Conference on Parallel and Distributed Computing, Applications and Technologies, Denver, Colorado,2005:856-860.
[32]Wang B, Chung R, Shen C. Genetic algorithm-based stereo vision with no block-partitioning of input images [A]. Proceedings of the IEEE International Symposium on Computational Intelligence in Robotics and Automation [C]. Kobe, Japan, 2003:830-836.
[33]Gong M, Yang Y H. Genetic-based stereo algorithm and disparity map evaluation [J]. International Journal of Computer Vision,2002,47(1-3):63-77.
[34]Ruichek Y, Issa H, Postaire J G, et al. Towards real-time obstacle detection using a hierarchical decomposition methodology for stereo matching with a genetic algorithm [A]. Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence [C]. Boca Raton,2004:138-147.
[35]Choset H, Lynch K M, Hutchinson S, et al. Principles of robot motion:Theory, algorithms, and implementations [M]. Massachusetts:MIT Press, Cambridge,2005.
[36]Swaminathan G. Robot motion planning [R]. Canada:School of Engineering Science, Simon Fraser University,2006.
[37]Brook 0, Hasegawa T, Lavalle S, et al. Algorithms for planning and control of robot motions [J]. IEEE Robotics & Automation Magazine,2009,16(1):14-15.
[38]Glavaski D, Volf M, Bonkovic M. Mobile robot path planning using exact cell decomposition and potential field methods [J]. WSEAS Transactions on Circuits and Systems,2009,8(9):789-800.
[39]郑效光,庄严,王伟.基于导航评价函数的非完整轮式移动机器人路径规划[C].第二十六届中国控制会议,中南大学,湖南,张家界,2007：103-107.
[40]Wang C K, Botea A. Scalable multi-agent pathfinding on grid maps with tractability and completeness guarantees [C]. Proceeding of the 19th European Conference on Artificial Intelligence, Lisboa, Portugal,2010:977-978.
[41]Lindemann S R, LaValle S M. A multiresolution approach for motion planning under differential constraints [C]. Proceeding of the IEEE International Conference on Robotics and Automation, Orlando, Florida,2006:139-144.
[42]Yang A, Niu Q, Zhao W Q, et al. An efficient algorithm for grid-based robotic path planning based on priority sorting of direction vectors [C]. Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, Wuxi, China 2010:456-466.
[43]Kala P, Shukla A, Tiwari R. Fusion of probabilistic A* algorithm and fuzzy inference system for robotic path planning [J]. Artificial Intelligence Review,33(4), 2010:307-327.
[44]Kang H I, Lee B, Kim K. Path planning algorithm using the particle swarm optimization and the improved Dijkstra algorithm [C]. Proceedings of the IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, Wuhan, China, 2008:1002-1004.
[45]Henrique L, Rios 0, Chaimowicz L. A survey and classification of A* based best-first heuristic search algorithms [C]. Proceedings of the 20th Brazilian Symposium on Artificial Intelligence, Sao Bernardo do Campo, Brazil,2010:253-262.
[46]闫飞,庄严,白明,等.基于拓扑高程模型的室外三维环境建模与路径规划[J].自动化学报,2010,36(11)：1493-1501.
[47]邓方安,雍龙泉,周涛,等.基于“矩阵乘法”的网络最短路径算法[J].电子学报,2009,37(7)：1594-1598.
[48]Meng R, Su W J, Lian X F. Mobile robot path planning based on dynamic fuzzy artificial potential field method [J]. Computer Engineering and Design,2010,31 (7):1558-1561.
[49]樊晓平,李双艳,陈特放.基于新人工势场函数的机器人动态避障规划[J].控制理论与应用.2005,22(5)：703-707.
[50]Qi N N, Ma B J, Liu X E. A modified artificial potential field algorithm for mobile robot path planning [C]. Proceedings of the Seventh World Congress on Intelligent Control and Automation, Chongqing, China,2008:2603-2607.
[51]Burns B, and Brock 0. Sampling-based motion planning with sensing uncertainty [C]. Proceedings of the IEEE International Conference on Robotics and Automation, Roma, Italy,2007:3313-3318.
[52]Kavraki L, Svestka P, Latombe J C. Probabilistic roadmaps for path planning in high-dimensional configuration space [J]. IEEE Transaction on Robotics and Automation,1996,12(4):566-580.
[53]LaValle S M, Branicky M, Lindemann S. On the relationship between classical grid search and probabilistic roadmaps [J]. International Journal of Robotics Research, 2004,23(7-8):673-692.
[54]Hsu D J, Latombe C, Kurniawati H. On the probabilistic foundations of probabilistic roadmap planning [J]. International Journal of Robotics Research,2006, 25(7):627-643.
[55]LaValle S M, Kuffner J J. Rapidly-exploring random trees:progress and prospects [C]. Algorithmic and Computational Robotics:New Directions:the 4th Workshop on the Algorithmic Foundations of Robotics, A K Peters, Ltd., Wellesley, USA, 2001:293-308.
[56]Yershova A, Jaillet L, Simeon T, et al. Dynamic-domain RRTs:efficient exploration by controlling the sampling domain [C]. Proceedings of the IEEE International Conference on Robotics and Automation, Barcelona, Spain,2005:3856-3861.
[57]Melchior N A, Simmons R. Particle RRT for path planning with uncertainty [C]. Proceedings of the IEEE International Conference on Robotics and Automation, Roma, Italy,2007:1617-1624.
[58]Jaillet L, Cortes J, Simeon T. Transition-based RRT for path planning in continuous cost spaces [C]. Proceedings of the IEEE/RSJ International Conference on Intelligent Robot Systems, Nice, France,2008:2145-2150.
[59]Bruce J, Veloso M M. Real-time randomized path planning for robot navigation [C]. Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Switzerland,2002:2383-2388.
[60]Branicky M, LaValle S M, Olson K. Quasi-randomized path planning [C]. Proceedings of the IEEE Conference on Robotics and Automation, Seoul, Korea,2001:1481-1487.
[61]Pivtoraiko M, Knepper R A, Kelly A. Optimal, smooth, nonholonomic mobile robot motion planning in state lattices, Technology Report CMU-RI-TR-07-15, Robotics Institute, Carnegie Mellon University, Pittsburgh,2007.
[62]Ulrich I, Borenstein J. VFH+:reliable obstacle avoidance for fast mobile robots [C]. Proceedings of IEEE International Conference on Robotics and Automation, Leuven, Belgium,1998:1572-1577.
[63]Minguez J, Montano L. Nearness diagram (ND) navigation:collision avoidance in troublesome scenarios [J]. IEEE Transaction on Robotics,2004,20(1):45-59.
[64]Fox D, Burgard W, Thrun S. The dynamic window approach to collision avoidance [J]. IEEE Robotics & Automation Magazine,1997,4(1):23-33.
[65]Arras K, Persson J, Tomatis N. Real-time obstacle avoidance for polygonal robots with a reduced dynamic window [C]. Proceedings of the IEEE International Conference on Robotics and Automation, Washington DC, USA,2002:3050-3055.
[66]Lai X C, Ge S S, Mamun A A. Hierarchical incremental path planning and situation-dependent optimized dynamic motion planning considering accelerations [J]. IEEE Transaction on Systems, Man and Cybernetics-Part B:Cybernetics,2007, 37(6):1541-1554.
[67]Wang M, Liu J. Fuzzy logic based robot path planning in unknown environment [C]. Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Guangzhou, China,2005:813-818.
[68]Ghatee M, Mohades A. Motion planning in order to optimize the length and clearance applying a Hopfield neural network [J]. Expert Systems with Applications,2009, 36 (3):4688-4695.
[69]Erinc G, Carpin S. A genetic algorithm for nonholonomic motion planning [C]. Proceedings of 2007 IEEE International Conference on Robotics and Automation, Roma, Italy,2007:1843-1849.
[70]Marcos M G, Machado J T, Perdicoulis T P. Trajectory planning of redundant manipulators using genetic algorithms [J]. Communications in Nonlinear Science and Numerical Simulation,2009,14(7):2858-2869.
[71]Moravec H. Mind Children:The Future of Robot and Human Intelligence [M]. Cambridge, Massachusetts:Harvard University Press,1988.
[72]庄严,陈东,王伟,等.移动机器人基于视觉室外自然场景理解的研究与进展[J].自动化学报,2010,36(1)：1-11.
[73]Weijer J, Schmid C. Coloring local feature extraction [C]. Proceedings of the Sixth European Conference on Computer Vison, Dublin, Ireland,2006:334-348.
[74]Ferrari V, Jurie F, Schmid C. From images to shape models for object detection [J]. International Journal of Computer Vision,2010,87(3):284-303.
[75]Contour context selection for object detection:A set-to-set contour matching approach [C]. Proceedings of the 10th European Conference on Computer Vision, Marseille, France,2008:774-787.
[76]刘丽,匡纲要.图像纹理特征提取方法综述[J].中国图象图形学报,2009,14(4)：622-635.
[77]Shotton J, Winn J, Rother C, et al. Textonboost for image understanding:Multi-class object recognition and segmentation by jointly modeling texture, layout, and context [J]. International Journal of Computer Vision,2009,81(1):2-23.
[78]Ilonen J, Kamarainen J K, Paalanen P. Image feature localization by multiple hypothesis testing of Gabor features [J]. IEEE Transactions on Image Processing, 2008,17(3):311-325.
[79]Oliva A, Torralba A. Modeling the shape of the scene:a holistic representation of the spatial envelope [J]. International Journal of Computer Vision,2001, 42(3):145-175.
[80]Zhang S, Fan J, Lu H, et al. Salient object detection on large-scale video data [C]. Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, Minnesota,2007:1-6.
[81]Vogel J, Schiele B. Semantic modeling of natural scenes for content-based image retrieval [J]. International Journal of Computer Vision,2007,72(2):133-157.
[82]Nowak E, Jurie F, Triggs B. Sampling strategies for bag-of-features image classification [C]. Proceedings of the 9th European Conference on Computer Vision, Graz, Austria,2006:490-503.
[83]Bai M, Zhuang Y, Wang W. Statistical layout of Improved Image descriptor for pedestrian detection [J]. ICIC Express Letters,2010,4(5B):1931-1936.
[84]Tuytelaars T, Schmid C. Vector quantizing feature space with a regular lattice[C]. Proceedings of the 11th International Conference on Computer Vision, Rio de Janeiro, Brazil,2007:1-8.
[85]Yang L, Jin R, Sukthankar R, et al. Unifying discriminative visual codebook generation with classifier training for object category recognition [C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, Alaska,2008:1-8.
[86]Yang J, Jiang Y G, Hauptmann A G, et al. Evaluating bag-of-visual-words representations in scene classification [C]. Proceedings of the International Workshop on Multimedia Information Retrieval, New York, USA,2007:197-206.
[87]Tirilly P, Claveau V, Gros P. Language modeling for bag-of-visual words image categorization [C]. Proceedings of the International Conference on Content-based Image and Video Retrieval, Niagara Falls, Canada,2008:249-258.
[88]Perronnin F, Dance C. Fisher kernels on visual vocabularies for image categorization[C]. Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, Minnesota,2007:1-8.
[89]Elazary L, Itti L. A Bayesian model for efficient visual search and recognition [J]. Vision Research,2010,50(14):1338-1352.
[90]Deselaers T, Heigold G, Ney H. Object classification by fusing SVMs and Gaussian mixtures [J]. Pattern Recognition,2010,43(7):2476-2484.
[91]Li F F, Perona P. A bayesian hierarchical model for learning natural scene categories [C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, USA,2005:524-531.
[92]Fergus R, Li F F, Perona P, et al. Learning object categories from Google's image search [C]. Proceedings of International Conference on Computer Vision, Beijing, China,2005:1816-1823.
[93]Huang S P, Jin L W. A PLSA-based semantic bag generator with application to natural scene classification under multi-instance mMulti-label learning framework [C]. Proceedings of the Fifth International Conference on Image and Graphics, Xi'an, Shanxi,2009:331-335.
[94]Kumar R, Math S, Tripathi R C, et al. Patent classification of the new invention using PLSA [C]. Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia, New York, USA,2010:222-225.
[95]Bosch A, Zisserman A, Munoz X. Scene classification via PLSA [C]. Proceedings of European Conference on Computer Vision, Graz, Austria,2006:517-530.
[96]Bosch A, Zisserman A, Munoz X. Scene classification using a hybrid generative/ discriminative approach [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2008,30(4):712-727.
[97]Felzenszwalb P F, Girshick R B, McAllester D, et al. Object detection with discriminatively trained part-based models [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2010,32(9):1627-1645.
[98]Amit Y, Trouve A. POP:patchwork of parts models for object recognition [J]. International Journal Computer Vision,2007,75(2):267-282.
[99]Crandall D, Felzenszwalb P F, Huttenlocher D. Spatial priors for part-based recognition using statistical models [C]. Proceedings of the IEEE Conference of Computer Vision and Pattern Recognition, San Diego, CA,2005:10-17.
[100]Lin Z, Hua G, Davis L S. Multiple instance fFeature for robust part-based object detection [C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Miami, Florida,2009:405-412.
[101]Weber M. Unsupervised learning of models for object recognition [D]. PhD Dissertation, Pasadena:California Institute of Technology,2000.
[102]Fergus R, Perona P, Zisserman A. Weakly supervised scale-invariant learning of models for visual recognition [J]. International Journal of Computer Vision,2007, 71 (3):273-303.
[103]Felzenszwalb P F, Huttenlocher D P. Pictorial structures for object recognition [J]. International Journal of Computer Vision,2005,61(1):55-79.
[104]Vedaldi A, Gulshan V, Varma M, et al. Multiple kernels for object detection [C]. Proceedings of the 12th International Conference on Computer Vision, Kyoto, Japan, 2009:1-8.
[105]Grauman K, Darrell T. Pyramid match kernels:Discriminative classification with sets of image features[R]. Technical Report MIT-CSAIL-TR-2006-020, MIT CSAIL, Cambridge, MA,2006.
[106]Lazebnik S, Schmid C, Ponce J. Beyond bags of features:Spatial pyramid matching for recognizing natural scene categories[C]. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, Washington DC, USA, 2006:2169-2178.
[107]Zhang H, Berg A C, Maire M, et al. SVM-KNN:Discriminative nearest neighbor classification for visual category recognition [C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Washington DC, USA, 2006:2126-2136.
[108]Vedaldi A, Soatto S. Relaxed Matching Kernels For Robust Image Comparison [C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, Alaska,2008:1-8.
[109]Gehler P, Nowozin S. On feature combination for multiclass object classification [C]. Proceedings of the 12th International Conference on Computer Vision, Kyoto, Japan,2009:221-228.
[110]Abdullah A, Veltkamp R C, Wiering M A. Spatial pyramids and two-layer stacking SVM classifiers for image categorization:A comparative study [C]. Proceedings of International Joint Conference on Neural Networks. Atlanta, Georgia, USA, 2009:5-12.
[111]Baro X, Escalera S, Vitria J, et al. Traffic sign recognition using evolutionary Adaboost detection and forest-ECOC classification [J]. IEEE Transactions on Intelligent Transportation Systems,2009,10(1):113-126.
[112]Li R, Lu J J, Zhang Y F, et al. Dynamic Adaboost learning with feature selection based on parallel genetic algorithm for image annotation [J]. Knowledge-Based Systems,2010,23(3):195-201.
[113]Viola P, Miehael J. Rapid object detection using a boosted caseade of simple features [C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Kauai, HI, USA,2001,511-518.
[114]Viola P, Jones M. Fast and robust classification using asymmetric AdaBoost and a detector caseade[C]. Proceedings of Advances in Neural Information Processing Systems, Columbia, Canada,2002:1311-1318.
[115]Wu B, Zhouand A H, Huang C. Fast rotation invariant multiview face detection based on real AdaBoost[C]. Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition, Seoul, Korea,2004:79-84.
[116]Li S Z, Zhang Z Q. Floatboost learning and statistical face deteetion [J]. IEEE Transactions on Pattern Analysis and Maehine Intelligence,2004,26(9):1112-1123.
[117]Kumar S, Hebert M. A hierarchical field framework for unified context-based classification [C]. Proceedings of the Tenth IEEE International Conference on Computer Vision, Beijing, China,2005:1284-1291.
[118]Li S Z. Markov random field modeling in image analysis [M]. London:Springer Science & Business Media,2009.
[119]Liu C, Yuen J, Torralba A. Nonparametric scene parsing:label transfer via dense scene alignment [C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Miami, Florida,2009:1972-1979.
[120]Copsey K, Webb A. Bayesian networks for incorporation of contextual information in target recognition systems [C]. Joint IAPR International Workshops Structural, Syntactic, and Statistical Pattern Recognition, Ontario, Canada,2002:395-422.
[121]Wang W H, Tung C L. Dynamic hand gesture recognition using hierarchical dynamic Bayesian networks through low-level image processing [C]. Proceedings of IEEE Conference on Machine Learning and Cybernetics, Kunming, China,2008:3247-3253.
[122]Leibe B, Ettlin A, Schiele B. Learning semantic object parts for object categorizatio [J]. Image and Vision Computing,2008,26(1):15-26.
[123]Lafferty J D, McCallum A, Pereira F C. Conditional random fields:Probabilistic models for segmenting and labeling sequence data [C]. Proceedings of the Eighteenth International Conference on Machine Learning, San Francisco, CA,2001:282-289.
[124]Kumar S, Hebert M. Discriminative random fields:A discriminative framework for contextual interaction in classification [C]. Proceedings of the 9th IEEE International Conference on Computer Vision, Nice, France,2003:1150-1157.
[125]Kumar S, Hebert M. Discriminative random fields [J]. International Journal of Computer Vision,2006,68(2):179-201.
[126]Desai C, Ramanan D, Fowlkes C. Discriminative models for multi-class object layout [C]. Proceedings of the 12th International Conference on Computer Vision, Kyoto, Japan,2009:229-236.
[127]Sturgess P, Alahari K, Russell C, et al. What, where & how many combining object detectors and CRFs [C]. Crete, Greece,2010:424-437.
[128]Ponce J, Berg T L, Everingham M. Dataset issues in object recognition [C]. Proceedings of Toward Category-Level Object Recognition, Sicily,2006:29-48.
[129]Russell B C, Torralba A, Murphy K P. LabelMe:a database and web-based tool for image annotation [J]. International Journal of Computer Vision,2008,77(1-3):157-173.
[130]赵杰,于舒春,蔡鹤皋.金字塔双层动态规划立体匹配算法[J].控制与决策.2007,22(1)：69-77.
[131]Bai M, Zhuang Y, Wang W. Hierarchical adaptive stereo matching algorithm for obstacle detection with dynamic programming [J]. Journal of Control Theory and Applications, 2008,6(1):123-129.
[132]Yoon K J, Kweon I S. Locally adaptive support-weight approach for visual correspondence search [C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA,2005,924-931.
[133]Wang L, Liao M, Gong M, et al. High-quality real-time stereo using adaptive cost aggregation and dynamic programming [C]. Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission, Washington, DC, 2006:798-805.
[134]Zitnick C L, Kanade T. A cooperative algorithm for stereo matching and occlusion detection [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2000, 22(7):675-684.
[135]Veksler 0. Stereo correspondence by dynamic programming on a tree [C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, 2005:1075-1082.
[136]Gong M, Yang Y H. Near real-time reliable stereo matching using programmable graphics hardware [C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA,2005:924-931.
[137]Salmen J, Schlipsing M, Edelbrunner J, et al. Real-time stereo vision:making more out of dynamic programming [C]. Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns, Munster, Germany,2009:1096-1103.
[138]Deng Y, Lin X. A fast line segment based dense stereo algorithm using tree dynamic programming [C]. Proceedings of the Ninth European Conference on Computer Vision, Graz, Austria,2006:201-212.
[139]Broggi A, Caraffi C, Porta P P, et al. The single frame stereo vision system for reliable obstacle detection used during the 2005 DAPRA Grand Challenge on TerraMax [C]. Proceedings of IEEE International Conference on Intelligent Transportation Systems, Toronto, Canada,2006:745-752.
[140]Nedevschi S, Danescu R, Marita T. A sensor for urban driving assistance systems based on dense stereovision [C]. Proceedings of IEEE International Conference on Intelligent Vehicles Symposium, Istambul, Romania,2007:276-283.
[141]Bertozzi M, Broggi. GOLD:a parallel real-time stereo vision system for generic obstacle and lane detection [J]. IEEE Transactions on Image Processing,1998, 7(1):62-81.
[142]Lee K Y, Lee J W, Houshangi N. A stereo matching algorithm based on top-view transformation and dynamic programming for road-vehicle detection [J]. International Journal of Control, Automation and Systems,2009,7(2):221-231.
[143]Talukder A, Manduchi R, Rankin A, et al. Fast and reliable obstacle detection and segmentation for crosscountry navigation [C]. Proceedings of IEEE International Conference on Intelligent Vehicles Symposium, Versailles, France,2002:610-618.
[144]Murray D, Little J. Using real-time stereo vision for mobile robot navigation [J]. Autonomous Robots,2000,8(2):161-171.
[145]Labayrade R, Aubert D, Tarel J P. Real time obstacle detection on non flat road geometry through V-disparity representation [C]. Proceedings of IEEE International Conference on Intelligent Vehicles Symposium, Versailles, France,2002:646-651.
[146]Sappa A.On-board camera extrinsic parameter estimation [J]. Electronics Letters, 2006,42(13):745-747.
[147]Yong J Y, Acton S T. Speckle reducing anisotropic diffusion [J]. IEEE Transactions on Image Processing,2002,11 (11):1260-1270.
[148]Labayrade R, Royere C, Gruyer D. Cooperative fusion for multi-obstacles detection with use of stereovision and laser scanner [J]. Autonomous Robots,2005, 19(2):117-140.
[149]Lim Y C, Lee C H, Kwon S, et al. Distance estimation algorithm for both long and short ranges based on stereo vision system [C]. Proceedings of the IEEE Intelligent Vehicles Symposium, Eindhoven, Netherlands,2008:841-846.
[150]Bai M, Zhuang Y, Wang W. Stereovision based obstacle detection approach for mobile robot navigation [C]. Proceedings of IEEE International Conference on Intelligent Control and Information Processing, Dalian, China,2010:328-333.
[151]Hadsell R, Sermanet P, Ben J. Learning long-range vision for autonomous off-road driving [J]. Journal of Field Robotics,2009,26(2):120-144.
[152]McBridel J R, Ivanl J C, Rhode D S, et al. A perspective on emerging automotive safety applications, derived from lessons learned through participation in the DARPA Grand Challenges [J]. Journal of Field Robotics,2008,25(10):808-840.
[153]Ng J, Braunl T. Performance comparison of bug navigation algorithms [J]. Journal of Intelligent and Robotic Systems,2007,50(1):73-84.
[154]Plaku E, Kavraki L E, Vardi M Y. Motion planning with dynamics by a synergistic combination of layers of planning [J]. IEEE Transactions on Robotics,2010, 26(3):469-482.
[155]Mali A D. On the behavior-based architectures of autonomous agents [J]. IEEE Transaction on Systems, Man and Cybernetics-Part C,2002,32(3):231-242.
[156]Li W, Christensen H I, Oreback A, et al. An architecture for indoor navigation [C]. Proceedings of the IEEE Conference on Robotics and Automation, New Orleans, USA, 2004:1783-1788.
[157]Montesano L, Minguez J, Montano L. Lessons learned in integration for sensor-based robot navigation systems [J]. International Journal of Advanced Robotic System, 2006,3(1):85-91.
[158]Minguez J. and Montano L. Sensor-based robot motion generation in unknown, dynamic and troublesome scenarios [J]. Robotics and Autonomous Systems,2005, 52(4):290-311.
[159]Ge S S, Lai X C, Mamun A A. Sensor-based path planning for nonholonomic mobile robots subject to dynamic constraints [J]. Robotics and Autonomous Systems,2007, 55(7):513-526.
[160]Wang Y, Mulvaney D, Sillitoe I. Robot navigation by waypoints [J]. Journal of Intelligent and Robotic Systems,2008,52(2):175-207.
[161]LaValle S M. Planning algorithms [M]. Cambridge:Cambridge University Press,2006.
[162]Minguez J, Montano L. Extending reactive collision avoidance methods to consider any vehicle shape and the kinematics and dynamic constraints [J]. IEEE Transaction on Robotics,2009,25(2):367-381.
[163]Fox D, Burgard W, Thrun S, eta al. A hybrid collision avoidance method for mobile robots [C]. Proceedings of the IEEE International Conference on Robotics and Automation, Leuven, Belgium,1998:1238-1243.
[164]Tsianos K I, Sucan I A, Kavraki L E. Sampling-based robot motion planning:towards realistic applications [J]. Computer Science Review,2007,1(1):2-11.
[165]Howard T M, Green C J, Ferguson D. State space sampling of feasible motions for high-performance mobile robot navigation in complex environments [J]. Journal of Field Robotics,2008,25(6-7):325-345.
[166]Knepper R A, Mason M T. Empirical sampling of path sets for local area motion planning [C] Proceedings of the International Symposium of Experimental Robotics, Springer Tracts in Advanced Robotics Vol.54, Springer-Verlag, Berlin, Germany, 2008:451-462.
[167]Kelly A, Stentz A, Amidi O. Toward reliable off road autonomous vehicles operating in challenging environments [J]. International Journal of Robotics Research,2006, 25(5-6):449-483.
[168]Green C, Kelly A. Toward optimal sampling in the space of paths [C]. Proceedings of the 13th International Symposium of Robotics Research, Hiroshima, Japan, 2007:171-180.
[169]Jolly K G, Kumar R S, Vijayakumar R. A Bezier curve based path planning in a multi-agent robot soccer system without violating the acceleration limits [J]. Robotics and Autonomous Systems,2009,57(1):23-33.
[170]Huntsberger T, Aghazarian H, Howard H. Stereo vision-based navigation for autonomous surface vessels [J]. Issue Journal of Field Robotics Journal of Field Robotics,2011,28(1):3-18.
[171]Minguez J, Montano L. Abstracting vehicle shape and kinematic constraints from obstacle avoidance methods [J]. Autonomous Robots,2006,20(1):43-59.
[172]高隽,谢昭.图像理解理论与方法[M].北京：科学出版社,2009.
[173]Lampert C H, Blaschko M B, Hofmann T. Beyond sliding windows:Object localization by efficient subwindow search [C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, Alaska,2008:1-8.
[174]Dalal N, Triggs B. Histograms of oriented gradients for human detection [C]. IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA,2005:886-893.
[175]Lowe D. Distinctive image features from scale-invariant keypoints [J]. International Journal of Computer Vision,2004,60(2):91-110.
[176]Bay H, Ess A, Tuytelaars T, et al. SURF:Speeded Up Robust Features [J]. Computer Vision and Image Understanding,2008,110(3):346-359.
[177]Everingham M, Gool V, Williams L, et al. The PASCAL Visual Object Classes (VOC) Challenge [J]. International Journal of Computer Vision,2010,88(2):303-338.
[178]Mikolajczyk K,Schmid C. An affine invariant interest point detector [C]. Proceedings of the 7th European Conference on Computer Vision, Copenhagen, Denmark, 2002:128-142.
[179]Martin D R, Fowlkes C C, Malik J. Learning to detect natural image boundaries using local brightness, color, and texture cues [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,26(5):530-549.
[180]Leung T, Malik J. Representing and recognizing the visual appearance of materials using three-dimensional textons [J]. International Journal of Computer Vision,2001, 43(1):29-44.
[181]Mikolajczyk K, Schmid C. Scale and affine invariant interest point detectors [J]. International Journal of Computer Vision,2004,60(1):63-86.
[182]Forstner W, Gulch E. A fast operator for detection and precise location of distinct points, corners and centres of circular features [C]. Proceedings of Intercommission Conference on Fast Processing of Photogrammetric Data, Interlaken, Switzerland,1987:281-305.
[183]Ke Y, Sukthankar R. PCA-SIFT:A more distinctive representation for local image descriptors [C]. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, Washington, DC, USA,2004:506-513.
[184]Mori and G, Malik J. Recognizing objects in adversarial clutter:Breaking a visual captcha [C]. Proceedings of the Conference on Computer Vision and Pattern Recognition, Madison, Wisconsin,2003:134-141.
[185]Tuytelaars T, Gool L V. Matching widely separated views based on affine invariant regions [J]. International Journal of Computer Vision,2004,59(1):61-85.
[186]Mikolajczyk K, Tuytelaars T, Schmid C, et al. A comparison of aff ine region detectors [J]. International Journal of Computer Vision,2005,65(1-2):43-72.
[187]Vidal-Naquet M, Ullman S. Object recognition with informative features and linear classification [C]. Proceedings of the 9th International Conference on Computer Vision, Nice, France,2003:281-288.
[188]Viola P, Jones M J, Snow D. Detecting pedestrians using patterns of motion and appearance [C]. Proceedings of the 9th International Conference on Computer Vision, Nice, France,2003:734-741.
[189]Carneiro G, Jepson A D. Multi-scale phase-based local features [C]. Proceedings of the IEEE International Conference on Computer Vision Pattern Recognition, Madison, WI, USA,2003:736-743.
[190]Lazebnik S, Schmid C, Ponce J. A sparse texture representation using local affine regions [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2005, 27(8):1265-1278.
[191]Mikolajczyk K, Schmid C. A performance evaluation of local descriptors [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(10):1615-1630.
[192]Felzenszwalb P, McAllester D, Ramanan D. A discriminatively trained, multiscale, deformable part model [C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, Alaska,2008:1-8.
[193]Shotton J, Johnson M, Cipolla R. Semantic texton forests for image categorization and segmentation [C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, Alaska,2008:1-8.
[194]Kumar S, Hebert M. Discriminative fields for modeling spatial dependencies in natural images [C]. Proceedings of the IEEE Conference on Advances in Neural Information Processing Systems, Columbia, Canada,2003:1351-1358.
[195]Verbeek J, Triggs B. Scene segmentation with CRFs learned from partially labeled images [C]. Proceedings of the IEEE Conference on Advances in Neural Information Processing Systems, Vancouver, Canada,2008:1553-1560.
[196]Kumar S, Hebert M. Multiclass discriminative fields for parts-based object detection [C]. Proceedings of Snowbird Learning Workshop, Utah,2004:1-8.
[197]Pedersoli M, Gonzalez J, Villanueva J J. High-speed human detection using a multiresolution cascade of histograms of oriented gradients [C]. Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis, Povoa do Varzim, Portugal,2009:48-55.
[198]Pedersoli M, Gonzalez J, Bagdanov A D, et al. Recursive coarse-to-fine localization for fast object detection [C]. Proceedings of the 11th European Conference in Computer Vision, Crete, Greece,2010:280-293.
[199]Harzallah H, Jurie F, Schmid C. Combining efficient object localization and image classification [C]. Proceedings of the 12th International Conference on Computer Vision, Kyoto, Japan,2009:237-244.
[200]Gonfaus J M, Boix X, van de Weijer J. Harmony potentials for joint classification and segmentation [C]. Proceedings of 23rd IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, USA,2010:3280-3287.
[201]Kumar M P, Zisserman A, Torr P. Efficient discriminative learning of parts-based models [C]. Proceedings of IEEE 12th International Conference on Computer Vision, Kyoto, Japan,2009:552-559.
[202]Kumar M P, Koller D. Efficiently selecting regions for scene understanding [C]. Proceedings of 23rd IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, USA,2010:3217-3224.
[203]Carreira J, Sminchisescu C. Constrained parametric min-cuts for automatic object segmentation [C]. Proceedings of 23rd IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, USA,2010:3241-3248.
[204]Li F X, Carreira J, Sminchisescu C. Object recognition as ranking holistic figure-ground hypotheses [C]. Proceedings of 23rd IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, USA,2010:1712-1719.
[2]Pauli J. Learning-based robot vision:principles and applications [M].1st ed. Springer:Lecture Notes in Computer Science,2001.
[3]Davies E R. Machine vision theory, algorithms, practicalities [M]. San Fransisco: Morgan Kaufmann Publishers,2005.
[4]蔡自兴.机器人学(第二版)[M].北京：清华大学出版社,2009.
[5]Guilherme N D, Avinash C K. Vision for mobile robot navigation:A survey [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(2):237-267.
[6]Marr D. Vision:A computational investigation into the human representation and processing of visual information [M]. Massachusetts:The MIT Press,2010.
[7]Barnard S T, Fischler M A. Computational stereo [J]. ACM Computing Surveys,1982,14(4)：553-572.
[8]Dhond U R, Aggarwal J K. Structure from stereo-a review [J]. IEEE Transactions on Systems, Man, and Cybernetics,1989,19(6):1489-1510.
[9]Faugeras 0. What can be seen in three dimensions with an uncalibrated stereo rig [C]. Proceedings of the Second European Conference Computer Vision, Santa Margherita Ligure, Italy,1992:563-578.
[10]Koschan A. What is new in computational stereo since 1989:A survey of current stereo papers [R]. Technical Report 93-22, Germany:Technical University of Berlin,1993.
[11]Scharstein D, Szeliski R. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms [J]. International Jounal of Computer Vision,2002,47(1)：7-42.
[12]Brown M Z, Burschka D, Hager G D. Advances in computational stereo [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(8):993-1008.
[13]白明,庄严,王伟.双目立体匹配算法的研究与进展[J].控制与决策,2008,23(7)：721-729.
[14]Szeliski R, Zabih R, Scharstein D, et al. A comparative study of energy minimization methods for markov random fields with smoothness based priors [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2008,30(6):1068-1080.
[15]Birchfield S, Tomasi C. Depth discontinuities by pixel-to-pixel stereo [C]. Proceedings of IEEE International Conference of Computer Vision, Bombay, India, 1998:1073-1080.
[16]Cox I J, Hingorani S L, Rao S B, et al. A maximum likelihood stereo algorithm [J]. Computer Vision and Image Understanding,1996,63(3):542-567.
[17]BobickAF, IntilleSS. Large occlusion sereo [J]. International Journal of Computer Vision,1999,33(3):181-200.
[18]Gong M, Yang Y H. Fast unambiguous stereo matching using reliability-based dynamic programming [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005,27(6):998-1003.
[19]Lei C, Selzer J, Yang Y H. Region-tree based stereo using dynamic programming optimization [C]. Proceedings of IEEE Conference of Computer Vision and Pattern Recognition, New York, USA,2006:378-385.
[20]Cox I J, Roy S. A maximum-flow formulation of the N-camera stereo correspondence problem [C]. Proceedings of the Sixth International Conference on Computer Vision, Bombay, India,1998:492-499.
[21]Kolmogorov V, Zabin R. What energy functions can be minimized via graph cuts [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2004, 26 (2):147-159.
[22]Boykov Y, Veksler O, Zabih R. Fast approximate energy minimization via graph cuts [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2001, 23(11):1222-1239.
[23]王年,范益政,鲍文霞,等.基于图割的图像匹配算法[J].电子学报,2006,34(2)：232-236.
[24]Boykov Y, Kolmogorov V. An experimental comparison of min-cut/max-f low algorithms for energy minimization in vision [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,26(9):1124-1137.
[25]Wang Z F, Zheng Z G. A region based stereo matching algorithm using cooperative optimization [C]. Proceedings of 2008 IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA,2008:1-8.
[26]Min D, Sohn K. Cost aggregation and occlusion handling with WLS in stereo matching [J]. IEEE Transactions on Image Processing,2008,17(8):1431-1442.
[27]Sun J, Zheng N N, Shum H Y. Stereo matching using belief propagation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(7):787-800.
[28]Felzenszwalb P F, Huttenlocher D P. Efficient belief propagation for early vision [C]. Proceedings of IEEE Conference of Computer Vision and Pattern Recognition, Washington DC, USA,2004:261-268.
[29]Klaus A, Sormann M, Karner K. Segment-based stereo matching using belief propagation and a self-adapting dissimilarity measure [C]. Proceedings of the 18th International Conference on Pattern Recognition, Hong Kong, China,2006:15-18.
[30]Ruichek Y. Multilevel- and neural-network-based stereo-matching method for real-time obstacle detection using linear cameras [J]. IEEE Transactions on Intelligent Transportation Systems,2005,6(1):54-62.
[31]Hua X J, Yokomichi M, Kono M. Stereo correspondence using color based on competitive-cooperative neural networks [C]. Proceedings of the Sixth International Conference on Parallel and Distributed Computing, Applications and Technologies, Denver, Colorado,2005:856-860.
[32]Wang B, Chung R, Shen C. Genetic algorithm-based stereo vision with no block-partitioning of input images [A]. Proceedings of the IEEE International Symposium on Computational Intelligence in Robotics and Automation [C]. Kobe, Japan, 2003:830-836.
[33]Gong M, Yang Y H. Genetic-based stereo algorithm and disparity map evaluation [J]. International Journal of Computer Vision,2002,47(1-3):63-77.
[34]Ruichek Y, Issa H, Postaire J G, et al. Towards real-time obstacle detection using a hierarchical decomposition methodology for stereo matching with a genetic algorithm [A]. Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence [C]. Boca Raton,2004:138-147.
[35]Choset H, Lynch K M, Hutchinson S, et al. Principles of robot motion:Theory, algorithms, and implementations [M]. Massachusetts:MIT Press, Cambridge,2005.
[36]Swaminathan G. Robot motion planning [R]. Canada:School of Engineering Science, Simon Fraser University,2006.
[37]Brook 0, Hasegawa T, Lavalle S, et al. Algorithms for planning and control of robot motions [J]. IEEE Robotics & Automation Magazine,2009,16(1):14-15.
[38]Glavaski D, Volf M, Bonkovic M. Mobile robot path planning using exact cell decomposition and potential field methods [J]. WSEAS Transactions on Circuits and Systems,2009,8(9):789-800.
[39]郑效光,庄严,王伟.基于导航评价函数的非完整轮式移动机器人路径规划[C].第二十六届中国控制会议,中南大学,湖南,张家界,2007：103-107.
[40]Wang C K, Botea A. Scalable multi-agent pathfinding on grid maps with tractability and completeness guarantees [C]. Proceeding of the 19th European Conference on Artificial Intelligence, Lisboa, Portugal,2010:977-978.
[41]Lindemann S R, LaValle S M. A multiresolution approach for motion planning under differential constraints [C]. Proceeding of the IEEE International Conference on Robotics and Automation, Orlando, Florida,2006:139-144.
[42]Yang A, Niu Q, Zhao W Q, et al. An efficient algorithm for grid-based robotic path planning based on priority sorting of direction vectors [C]. Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, Wuxi, China 2010:456-466.
[43]Kala P, Shukla A, Tiwari R. Fusion of probabilistic A* algorithm and fuzzy inference system for robotic path planning [J]. Artificial Intelligence Review,33(4), 2010:307-327.
[44]Kang H I, Lee B, Kim K. Path planning algorithm using the particle swarm optimization and the improved Dijkstra algorithm [C]. Proceedings of the IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, Wuhan, China, 2008:1002-1004.
[45]Henrique L, Rios 0, Chaimowicz L. A survey and classification of A* based best-first heuristic search algorithms [C]. Proceedings of the 20th Brazilian Symposium on Artificial Intelligence, Sao Bernardo do Campo, Brazil,2010:253-262.
[46]闫飞,庄严,白明,等.基于拓扑高程模型的室外三维环境建模与路径规划[J].自动化学报,2010,36(11)：1493-1501.
[47]邓方安,雍龙泉,周涛,等.基于“矩阵乘法”的网络最短路径算法[J].电子学报,2009,37(7)：1594-1598.
[48]Meng R, Su W J, Lian X F. Mobile robot path planning based on dynamic fuzzy artificial potential field method [J]. Computer Engineering and Design,2010,31 (7):1558-1561.
[49]樊晓平,李双艳,陈特放.基于新人工势场函数的机器人动态避障规划[J].控制理论与应用.2005,22(5)：703-707.
[50]Qi N N, Ma B J, Liu X E. A modified artificial potential field algorithm for mobile robot path planning [C]. Proceedings of the Seventh World Congress on Intelligent Control and Automation, Chongqing, China,2008:2603-2607.
[51]Burns B, and Brock 0. Sampling-based motion planning with sensing uncertainty [C]. Proceedings of the IEEE International Conference on Robotics and Automation, Roma, Italy,2007:3313-3318.
[52]Kavraki L, Svestka P, Latombe J C. Probabilistic roadmaps for path planning in high-dimensional configuration space [J]. IEEE Transaction on Robotics and Automation,1996,12(4):566-580.
[53]LaValle S M, Branicky M, Lindemann S. On the relationship between classical grid search and probabilistic roadmaps [J]. International Journal of Robotics Research, 2004,23(7-8):673-692.
[54]Hsu D J, Latombe C, Kurniawati H. On the probabilistic foundations of probabilistic roadmap planning [J]. International Journal of Robotics Research,2006, 25(7):627-643.
[55]LaValle S M, Kuffner J J. Rapidly-exploring random trees:progress and prospects [C]. Algorithmic and Computational Robotics:New Directions:the 4th Workshop on the Algorithmic Foundations of Robotics, A K Peters, Ltd., Wellesley, USA, 2001:293-308.
[56]Yershova A, Jaillet L, Simeon T, et al. Dynamic-domain RRTs:efficient exploration by controlling the sampling domain [C]. Proceedings of the IEEE International Conference on Robotics and Automation, Barcelona, Spain,2005:3856-3861.
[57]Melchior N A, Simmons R. Particle RRT for path planning with uncertainty [C]. Proceedings of the IEEE International Conference on Robotics and Automation, Roma, Italy,2007:1617-1624.
[58]Jaillet L, Cortes J, Simeon T. Transition-based RRT for path planning in continuous cost spaces [C]. Proceedings of the IEEE/RSJ International Conference on Intelligent Robot Systems, Nice, France,2008:2145-2150.
[59]Bruce J, Veloso M M. Real-time randomized path planning for robot navigation [C]. Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Switzerland,2002:2383-2388.
[60]Branicky M, LaValle S M, Olson K. Quasi-randomized path planning [C]. Proceedings of the IEEE Conference on Robotics and Automation, Seoul, Korea,2001:1481-1487.
[61]Pivtoraiko M, Knepper R A, Kelly A. Optimal, smooth, nonholonomic mobile robot motion planning in state lattices, Technology Report CMU-RI-TR-07-15, Robotics Institute, Carnegie Mellon University, Pittsburgh,2007.
[62]Ulrich I, Borenstein J. VFH+:reliable obstacle avoidance for fast mobile robots [C]. Proceedings of IEEE International Conference on Robotics and Automation, Leuven, Belgium,1998:1572-1577.
[63]Minguez J, Montano L. Nearness diagram (ND) navigation:collision avoidance in troublesome scenarios [J]. IEEE Transaction on Robotics,2004,20(1):45-59.
[64]Fox D, Burgard W, Thrun S. The dynamic window approach to collision avoidance [J]. IEEE Robotics & Automation Magazine,1997,4(1):23-33.
[65]Arras K, Persson J, Tomatis N. Real-time obstacle avoidance for polygonal robots with a reduced dynamic window [C]. Proceedings of the IEEE International Conference on Robotics and Automation, Washington DC, USA,2002:3050-3055.
[66]Lai X C, Ge S S, Mamun A A. Hierarchical incremental path planning and situation-dependent optimized dynamic motion planning considering accelerations [J]. IEEE Transaction on Systems, Man and Cybernetics-Part B:Cybernetics,2007, 37(6):1541-1554.
[67]Wang M, Liu J. Fuzzy logic based robot path planning in unknown environment [C]. Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Guangzhou, China,2005:813-818.
[68]Ghatee M, Mohades A. Motion planning in order to optimize the length and clearance applying a Hopfield neural network [J]. Expert Systems with Applications,2009, 36 (3):4688-4695.
[69]Erinc G, Carpin S. A genetic algorithm for nonholonomic motion planning [C]. Proceedings of 2007 IEEE International Conference on Robotics and Automation, Roma, Italy,2007:1843-1849.
[70]Marcos M G, Machado J T, Perdicoulis T P. Trajectory planning of redundant manipulators using genetic algorithms [J]. Communications in Nonlinear Science and Numerical Simulation,2009,14(7):2858-2869.
[71]Moravec H. Mind Children:The Future of Robot and Human Intelligence [M]. Cambridge, Massachusetts:Harvard University Press,1988.
[72]庄严,陈东,王伟,等.移动机器人基于视觉室外自然场景理解的研究与进展[J].自动化学报,2010,36(1)：1-11.
[73]Weijer J, Schmid C. Coloring local feature extraction [C]. Proceedings of the Sixth European Conference on Computer Vison, Dublin, Ireland,2006:334-348.
[74]Ferrari V, Jurie F, Schmid C. From images to shape models for object detection [J]. International Journal of Computer Vision,2010,87(3):284-303.
[75]Contour context selection for object detection:A set-to-set contour matching approach [C]. Proceedings of the 10th European Conference on Computer Vision, Marseille, France,2008:774-787.
[76]刘丽,匡纲要.图像纹理特征提取方法综述[J].中国图象图形学报,2009,14(4)：622-635.
[77]Shotton J, Winn J, Rother C, et al. Textonboost for image understanding:Multi-class object recognition and segmentation by jointly modeling texture, layout, and context [J]. International Journal of Computer Vision,2009,81(1):2-23.
[78]Ilonen J, Kamarainen J K, Paalanen P. Image feature localization by multiple hypothesis testing of Gabor features [J]. IEEE Transactions on Image Processing, 2008,17(3):311-325.
[79]Oliva A, Torralba A. Modeling the shape of the scene:a holistic representation of the spatial envelope [J]. International Journal of Computer Vision,2001, 42(3):145-175.
[80]Zhang S, Fan J, Lu H, et al. Salient object detection on large-scale video data [C]. Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, Minnesota,2007:1-6.
[81]Vogel J, Schiele B. Semantic modeling of natural scenes for content-based image retrieval [J]. International Journal of Computer Vision,2007,72(2):133-157.
[82]Nowak E, Jurie F, Triggs B. Sampling strategies for bag-of-features image classification [C]. Proceedings of the 9th European Conference on Computer Vision, Graz, Austria,2006:490-503.
[83]Bai M, Zhuang Y, Wang W. Statistical layout of Improved Image descriptor for pedestrian detection [J]. ICIC Express Letters,2010,4(5B):1931-1936.
[84]Tuytelaars T, Schmid C. Vector quantizing feature space with a regular lattice[C]. Proceedings of the 11th International Conference on Computer Vision, Rio de Janeiro, Brazil,2007:1-8.
[85]Yang L, Jin R, Sukthankar R, et al. Unifying discriminative visual codebook generation with classifier training for object category recognition [C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, Alaska,2008:1-8.
[86]Yang J, Jiang Y G, Hauptmann A G, et al. Evaluating bag-of-visual-words representations in scene classification [C]. Proceedings of the International Workshop on Multimedia Information Retrieval, New York, USA,2007:197-206.
[87]Tirilly P, Claveau V, Gros P. Language modeling for bag-of-visual words image categorization [C]. Proceedings of the International Conference on Content-based Image and Video Retrieval, Niagara Falls, Canada,2008:249-258.
[88]Perronnin F, Dance C. Fisher kernels on visual vocabularies for image categorization[C]. Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, Minnesota,2007:1-8.
[89]Elazary L, Itti L. A Bayesian model for efficient visual search and recognition [J]. Vision Research,2010,50(14):1338-1352.
[90]Deselaers T, Heigold G, Ney H. Object classification by fusing SVMs and Gaussian mixtures [J]. Pattern Recognition,2010,43(7):2476-2484.
[91]Li F F, Perona P. A bayesian hierarchical model for learning natural scene categories [C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, USA,2005:524-531.
[92]Fergus R, Li F F, Perona P, et al. Learning object categories from Google's image search [C]. Proceedings of International Conference on Computer Vision, Beijing, China,2005:1816-1823.
[93]Huang S P, Jin L W. A PLSA-based semantic bag generator with application to natural scene classification under multi-instance mMulti-label learning framework [C]. Proceedings of the Fifth International Conference on Image and Graphics, Xi'an, Shanxi,2009:331-335.
[94]Kumar R, Math S, Tripathi R C, et al. Patent classification of the new invention using PLSA [C]. Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia, New York, USA,2010:222-225.
[95]Bosch A, Zisserman A, Munoz X. Scene classification via PLSA [C]. Proceedings of European Conference on Computer Vision, Graz, Austria,2006:517-530.
[96]Bosch A, Zisserman A, Munoz X. Scene classification using a hybrid generative/ discriminative approach [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2008,30(4):712-727.
[97]Felzenszwalb P F, Girshick R B, McAllester D, et al. Object detection with discriminatively trained part-based models [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2010,32(9):1627-1645.
[98]Amit Y, Trouve A. POP:patchwork of parts models for object recognition [J]. International Journal Computer Vision,2007,75(2):267-282.
[99]Crandall D, Felzenszwalb P F, Huttenlocher D. Spatial priors for part-based recognition using statistical models [C]. Proceedings of the IEEE Conference of Computer Vision and Pattern Recognition, San Diego, CA,2005:10-17.
[100]Lin Z, Hua G, Davis L S. Multiple instance fFeature for robust part-based object detection [C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Miami, Florida,2009:405-412.
[101]Weber M. Unsupervised learning of models for object recognition [D]. PhD Dissertation, Pasadena:California Institute of Technology,2000.
[102]Fergus R, Perona P, Zisserman A. Weakly supervised scale-invariant learning of models for visual recognition [J]. International Journal of Computer Vision,2007, 71 (3):273-303.
[103]Felzenszwalb P F, Huttenlocher D P. Pictorial structures for object recognition [J]. International Journal of Computer Vision,2005,61(1):55-79.
[104]Vedaldi A, Gulshan V, Varma M, et al. Multiple kernels for object detection [C]. Proceedings of the 12th International Conference on Computer Vision, Kyoto, Japan, 2009:1-8.
[105]Grauman K, Darrell T. Pyramid match kernels:Discriminative classification with sets of image features[R]. Technical Report MIT-CSAIL-TR-2006-020, MIT CSAIL, Cambridge, MA,2006.
[106]Lazebnik S, Schmid C, Ponce J. Beyond bags of features:Spatial pyramid matching for recognizing natural scene categories[C]. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, Washington DC, USA, 2006:2169-2178.
[107]Zhang H, Berg A C, Maire M, et al. SVM-KNN:Discriminative nearest neighbor classification for visual category recognition [C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Washington DC, USA, 2006:2126-2136.
[108]Vedaldi A, Soatto S. Relaxed Matching Kernels For Robust Image Comparison [C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, Alaska,2008:1-8.
[109]Gehler P, Nowozin S. On feature combination for multiclass object classification [C]. Proceedings of the 12th International Conference on Computer Vision, Kyoto, Japan,2009:221-228.
[110]Abdullah A, Veltkamp R C, Wiering M A. Spatial pyramids and two-layer stacking SVM classifiers for image categorization:A comparative study [C]. Proceedings of International Joint Conference on Neural Networks. Atlanta, Georgia, USA, 2009:5-12.
[111]Baro X, Escalera S, Vitria J, et al. Traffic sign recognition using evolutionary Adaboost detection and forest-ECOC classification [J]. IEEE Transactions on Intelligent Transportation Systems,2009,10(1):113-126.
[112]Li R, Lu J J, Zhang Y F, et al. Dynamic Adaboost learning with feature selection based on parallel genetic algorithm for image annotation [J]. Knowledge-Based Systems,2010,23(3):195-201.
[113]Viola P, Miehael J. Rapid object detection using a boosted caseade of simple features [C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Kauai, HI, USA,2001,511-518.
[114]Viola P, Jones M. Fast and robust classification using asymmetric AdaBoost and a detector caseade[C]. Proceedings of Advances in Neural Information Processing Systems, Columbia, Canada,2002:1311-1318.
[115]Wu B, Zhouand A H, Huang C. Fast rotation invariant multiview face detection based on real AdaBoost[C]. Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition, Seoul, Korea,2004:79-84.
[116]Li S Z, Zhang Z Q. Floatboost learning and statistical face deteetion [J]. IEEE Transactions on Pattern Analysis and Maehine Intelligence,2004,26(9):1112-1123.
[117]Kumar S, Hebert M. A hierarchical field framework for unified context-based classification [C]. Proceedings of the Tenth IEEE International Conference on Computer Vision, Beijing, China,2005:1284-1291.
[118]Li S Z. Markov random field modeling in image analysis [M]. London:Springer Science & Business Media,2009.
[119]Liu C, Yuen J, Torralba A. Nonparametric scene parsing:label transfer via dense scene alignment [C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Miami, Florida,2009:1972-1979.
[120]Copsey K, Webb A. Bayesian networks for incorporation of contextual information in target recognition systems [C]. Joint IAPR International Workshops Structural, Syntactic, and Statistical Pattern Recognition, Ontario, Canada,2002:395-422.
[121]Wang W H, Tung C L. Dynamic hand gesture recognition using hierarchical dynamic Bayesian networks through low-level image processing [C]. Proceedings of IEEE Conference on Machine Learning and Cybernetics, Kunming, China,2008:3247-3253.
[122]Leibe B, Ettlin A, Schiele B. Learning semantic object parts for object categorizatio [J]. Image and Vision Computing,2008,26(1):15-26.
[123]Lafferty J D, McCallum A, Pereira F C. Conditional random fields:Probabilistic models for segmenting and labeling sequence data [C]. Proceedings of the Eighteenth International Conference on Machine Learning, San Francisco, CA,2001:282-289.
[124]Kumar S, Hebert M. Discriminative random fields:A discriminative framework for contextual interaction in classification [C]. Proceedings of the 9th IEEE International Conference on Computer Vision, Nice, France,2003:1150-1157.
[125]Kumar S, Hebert M. Discriminative random fields [J]. International Journal of Computer Vision,2006,68(2):179-201.
[126]Desai C, Ramanan D, Fowlkes C. Discriminative models for multi-class object layout [C]. Proceedings of the 12th International Conference on Computer Vision, Kyoto, Japan,2009:229-236.
[127]Sturgess P, Alahari K, Russell C, et al. What, where & how many combining object detectors and CRFs [C]. Crete, Greece,2010:424-437.
[128]Ponce J, Berg T L, Everingham M. Dataset issues in object recognition [C]. Proceedings of Toward Category-Level Object Recognition, Sicily,2006:29-48.
[129]Russell B C, Torralba A, Murphy K P. LabelMe:a database and web-based tool for image annotation [J]. International Journal of Computer Vision,2008,77(1-3):157-173.
[130]赵杰,于舒春,蔡鹤皋.金字塔双层动态规划立体匹配算法[J].控制与决策.2007,22(1)：69-77.
[131]Bai M, Zhuang Y, Wang W. Hierarchical adaptive stereo matching algorithm for obstacle detection with dynamic programming [J]. Journal of Control Theory and Applications, 2008,6(1):123-129.
[132]Yoon K J, Kweon I S. Locally adaptive support-weight approach for visual correspondence search [C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA,2005,924-931.
[133]Wang L, Liao M, Gong M, et al. High-quality real-time stereo using adaptive cost aggregation and dynamic programming [C]. Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission, Washington, DC, 2006:798-805.
[134]Zitnick C L, Kanade T. A cooperative algorithm for stereo matching and occlusion detection [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2000, 22(7):675-684.
[135]Veksler 0. Stereo correspondence by dynamic programming on a tree [C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, 2005:1075-1082.
[136]Gong M, Yang Y H. Near real-time reliable stereo matching using programmable graphics hardware [C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA,2005:924-931.
[137]Salmen J, Schlipsing M, Edelbrunner J, et al. Real-time stereo vision:making more out of dynamic programming [C]. Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns, Munster, Germany,2009:1096-1103.
[138]Deng Y, Lin X. A fast line segment based dense stereo algorithm using tree dynamic programming [C]. Proceedings of the Ninth European Conference on Computer Vision, Graz, Austria,2006:201-212.
[139]Broggi A, Caraffi C, Porta P P, et al. The single frame stereo vision system for reliable obstacle detection used during the 2005 DAPRA Grand Challenge on TerraMax [C]. Proceedings of IEEE International Conference on Intelligent Transportation Systems, Toronto, Canada,2006:745-752.
[140]Nedevschi S, Danescu R, Marita T. A sensor for urban driving assistance systems based on dense stereovision [C]. Proceedings of IEEE International Conference on Intelligent Vehicles Symposium, Istambul, Romania,2007:276-283.
[141]Bertozzi M, Broggi. GOLD:a parallel real-time stereo vision system for generic obstacle and lane detection [J]. IEEE Transactions on Image Processing,1998, 7(1):62-81.
[142]Lee K Y, Lee J W, Houshangi N. A stereo matching algorithm based on top-view transformation and dynamic programming for road-vehicle detection [J]. International Journal of Control, Automation and Systems,2009,7(2):221-231.
[143]Talukder A, Manduchi R, Rankin A, et al. Fast and reliable obstacle detection and segmentation for crosscountry navigation [C]. Proceedings of IEEE International Conference on Intelligent Vehicles Symposium, Versailles, France,2002:610-618.
[144]Murray D, Little J. Using real-time stereo vision for mobile robot navigation [J]. Autonomous Robots,2000,8(2):161-171.
[145]Labayrade R, Aubert D, Tarel J P. Real time obstacle detection on non flat road geometry through V-disparity representation [C]. Proceedings of IEEE International Conference on Intelligent Vehicles Symposium, Versailles, France,2002:646-651.
[146]Sappa A.On-board camera extrinsic parameter estimation [J]. Electronics Letters, 2006,42(13):745-747.
[147]Yong J Y, Acton S T. Speckle reducing anisotropic diffusion [J]. IEEE Transactions on Image Processing,2002,11 (11):1260-1270.
[148]Labayrade R, Royere C, Gruyer D. Cooperative fusion for multi-obstacles detection with use of stereovision and laser scanner [J]. Autonomous Robots,2005, 19(2):117-140.
[149]Lim Y C, Lee C H, Kwon S, et al. Distance estimation algorithm for both long and short ranges based on stereo vision system [C]. Proceedings of the IEEE Intelligent Vehicles Symposium, Eindhoven, Netherlands,2008:841-846.
[150]Bai M, Zhuang Y, Wang W. Stereovision based obstacle detection approach for mobile robot navigation [C]. Proceedings of IEEE International Conference on Intelligent Control and Information Processing, Dalian, China,2010:328-333.
[151]Hadsell R, Sermanet P, Ben J. Learning long-range vision for autonomous off-road driving [J]. Journal of Field Robotics,2009,26(2):120-144.
[152]McBridel J R, Ivanl J C, Rhode D S, et al. A perspective on emerging automotive safety applications, derived from lessons learned through participation in the DARPA Grand Challenges [J]. Journal of Field Robotics,2008,25(10):808-840.
[153]Ng J, Braunl T. Performance comparison of bug navigation algorithms [J]. Journal of Intelligent and Robotic Systems,2007,50(1):73-84.
[154]Plaku E, Kavraki L E, Vardi M Y. Motion planning with dynamics by a synergistic combination of layers of planning [J]. IEEE Transactions on Robotics,2010, 26(3):469-482.
[155]Mali A D. On the behavior-based architectures of autonomous agents [J]. IEEE Transaction on Systems, Man and Cybernetics-Part C,2002,32(3):231-242.
[156]Li W, Christensen H I, Oreback A, et al. An architecture for indoor navigation [C]. Proceedings of the IEEE Conference on Robotics and Automation, New Orleans, USA, 2004:1783-1788.
[157]Montesano L, Minguez J, Montano L. Lessons learned in integration for sensor-based robot navigation systems [J]. International Journal of Advanced Robotic System, 2006,3(1):85-91.
[158]Minguez J. and Montano L. Sensor-based robot motion generation in unknown, dynamic and troublesome scenarios [J]. Robotics and Autonomous Systems,2005, 52(4):290-311.
[159]Ge S S, Lai X C, Mamun A A. Sensor-based path planning for nonholonomic mobile robots subject to dynamic constraints [J]. Robotics and Autonomous Systems,2007, 55(7):513-526.
[160]Wang Y, Mulvaney D, Sillitoe I. Robot navigation by waypoints [J]. Journal of Intelligent and Robotic Systems,2008,52(2):175-207.
[161]LaValle S M. Planning algorithms [M]. Cambridge:Cambridge University Press,2006.
[162]Minguez J, Montano L. Extending reactive collision avoidance methods to consider any vehicle shape and the kinematics and dynamic constraints [J]. IEEE Transaction on Robotics,2009,25(2):367-381.
[163]Fox D, Burgard W, Thrun S, eta al. A hybrid collision avoidance method for mobile robots [C]. Proceedings of the IEEE International Conference on Robotics and Automation, Leuven, Belgium,1998:1238-1243.
[164]Tsianos K I, Sucan I A, Kavraki L E. Sampling-based robot motion planning:towards realistic applications [J]. Computer Science Review,2007,1(1):2-11.
[165]Howard T M, Green C J, Ferguson D. State space sampling of feasible motions for high-performance mobile robot navigation in complex environments [J]. Journal of Field Robotics,2008,25(6-7):325-345.
[166]Knepper R A, Mason M T. Empirical sampling of path sets for local area motion planning [C] Proceedings of the International Symposium of Experimental Robotics, Springer Tracts in Advanced Robotics Vol.54, Springer-Verlag, Berlin, Germany, 2008:451-462.
[167]Kelly A, Stentz A, Amidi O. Toward reliable off road autonomous vehicles operating in challenging environments [J]. International Journal of Robotics Research,2006, 25(5-6):449-483.
[168]Green C, Kelly A. Toward optimal sampling in the space of paths [C]. Proceedings of the 13th International Symposium of Robotics Research, Hiroshima, Japan, 2007:171-180.
[169]Jolly K G, Kumar R S, Vijayakumar R. A Bezier curve based path planning in a multi-agent robot soccer system without violating the acceleration limits [J]. Robotics and Autonomous Systems,2009,57(1):23-33.
[170]Huntsberger T, Aghazarian H, Howard H. Stereo vision-based navigation for autonomous surface vessels [J]. Issue Journal of Field Robotics Journal of Field Robotics,2011,28(1):3-18.
[171]Minguez J, Montano L. Abstracting vehicle shape and kinematic constraints from obstacle avoidance methods [J]. Autonomous Robots,2006,20(1):43-59.
[172]高隽,谢昭.图像理解理论与方法[M].北京：科学出版社,2009.
[173]Lampert C H, Blaschko M B, Hofmann T. Beyond sliding windows:Object localization by efficient subwindow search [C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, Alaska,2008:1-8.
[174]Dalal N, Triggs B. Histograms of oriented gradients for human detection [C]. IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA,2005:886-893.
[175]Lowe D. Distinctive image features from scale-invariant keypoints [J]. International Journal of Computer Vision,2004,60(2):91-110.
[176]Bay H, Ess A, Tuytelaars T, et al. SURF:Speeded Up Robust Features [J]. Computer Vision and Image Understanding,2008,110(3):346-359.
[177]Everingham M, Gool V, Williams L, et al. The PASCAL Visual Object Classes (VOC) Challenge [J]. International Journal of Computer Vision,2010,88(2):303-338.
[178]Mikolajczyk K,Schmid C. An affine invariant interest point detector [C]. Proceedings of the 7th European Conference on Computer Vision, Copenhagen, Denmark, 2002:128-142.
[179]Martin D R, Fowlkes C C, Malik J. Learning to detect natural image boundaries using local brightness, color, and texture cues [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,26(5):530-549.
[180]Leung T, Malik J. Representing and recognizing the visual appearance of materials using three-dimensional textons [J]. International Journal of Computer Vision,2001, 43(1):29-44.
[181]Mikolajczyk K, Schmid C. Scale and affine invariant interest point detectors [J]. International Journal of Computer Vision,2004,60(1):63-86.
[182]Forstner W, Gulch E. A fast operator for detection and precise location of distinct points, corners and centres of circular features [C]. Proceedings of Intercommission Conference on Fast Processing of Photogrammetric Data, Interlaken, Switzerland,1987:281-305.
[183]Ke Y, Sukthankar R. PCA-SIFT:A more distinctive representation for local image descriptors [C]. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, Washington, DC, USA,2004:506-513.
[184]Mori and G, Malik J. Recognizing objects in adversarial clutter:Breaking a visual captcha [C]. Proceedings of the Conference on Computer Vision and Pattern Recognition, Madison, Wisconsin,2003:134-141.
[185]Tuytelaars T, Gool L V. Matching widely separated views based on affine invariant regions [J]. International Journal of Computer Vision,2004,59(1):61-85.
[186]Mikolajczyk K, Tuytelaars T, Schmid C, et al. A comparison of aff ine region detectors [J]. International Journal of Computer Vision,2005,65(1-2):43-72.
[187]Vidal-Naquet M, Ullman S. Object recognition with informative features and linear classification [C]. Proceedings of the 9th International Conference on Computer Vision, Nice, France,2003:281-288.
[188]Viola P, Jones M J, Snow D. Detecting pedestrians using patterns of motion and appearance [C]. Proceedings of the 9th International Conference on Computer Vision, Nice, France,2003:734-741.
[189]Carneiro G, Jepson A D. Multi-scale phase-based local features [C]. Proceedings of the IEEE International Conference on Computer Vision Pattern Recognition, Madison, WI, USA,2003:736-743.
[190]Lazebnik S, Schmid C, Ponce J. A sparse texture representation using local affine regions [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2005, 27(8):1265-1278.
[191]Mikolajczyk K, Schmid C. A performance evaluation of local descriptors [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(10):1615-1630.
[192]Felzenszwalb P, McAllester D, Ramanan D. A discriminatively trained, multiscale, deformable part model [C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, Alaska,2008:1-8.
[193]Shotton J, Johnson M, Cipolla R. Semantic texton forests for image categorization and segmentation [C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, Alaska,2008:1-8.
[194]Kumar S, Hebert M. Discriminative fields for modeling spatial dependencies in natural images [C]. Proceedings of the IEEE Conference on Advances in Neural Information Processing Systems, Columbia, Canada,2003:1351-1358.
[195]Verbeek J, Triggs B. Scene segmentation with CRFs learned from partially labeled images [C]. Proceedings of the IEEE Conference on Advances in Neural Information Processing Systems, Vancouver, Canada,2008:1553-1560.
[196]Kumar S, Hebert M. Multiclass discriminative fields for parts-based object detection [C]. Proceedings of Snowbird Learning Workshop, Utah,2004:1-8.
[197]Pedersoli M, Gonzalez J, Villanueva J J. High-speed human detection using a multiresolution cascade of histograms of oriented gradients [C]. Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis, Povoa do Varzim, Portugal,2009:48-55.
[198]Pedersoli M, Gonzalez J, Bagdanov A D, et al. Recursive coarse-to-fine localization for fast object detection [C]. Proceedings of the 11th European Conference in Computer Vision, Crete, Greece,2010:280-293.
[199]Harzallah H, Jurie F, Schmid C. Combining efficient object localization and image classification [C]. Proceedings of the 12th International Conference on Computer Vision, Kyoto, Japan,2009:237-244.
[200]Gonfaus J M, Boix X, van de Weijer J. Harmony potentials for joint classification and segmentation [C]. Proceedings of 23rd IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, USA,2010:3280-3287.
[201]Kumar M P, Zisserman A, Torr P. Efficient discriminative learning of parts-based models [C]. Proceedings of IEEE 12th International Conference on Computer Vision, Kyoto, Japan,2009:552-559.
[202]Kumar M P, Koller D. Efficiently selecting regions for scene understanding [C]. Proceedings of 23rd IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, USA,2010:3217-3224.
[203]Carreira J, Sminchisescu C. Constrained parametric min-cuts for automatic object segmentation [C]. Proceedings of 23rd IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, USA,2010:3241-3248.
[204]Li F X, Carreira J, Sminchisescu C. Object recognition as ranking holistic figure-ground hypotheses [C]. Proceedings of 23rd IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, USA,2010:1712-1719.