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基于Dempster-Shafer证据理论的匹配区多特征适配性分析方法
引用本文:陈雪凌,赵春晖,李耀军,程咏梅.基于Dempster-Shafer证据理论的匹配区多特征适配性分析方法[J].计算机应用,2013,33(6):1665-1669.
作者姓名:陈雪凌  赵春晖  李耀军  程咏梅
作者单位:西北工业大学 自动化学院, 西安 710072
基金项目:国家自然科学基金资助项目(61074155);西北工业大学2011年本科毕业设计重点扶持项目
摘    要:匹配区的适配性分析在景象匹配视觉导航中至关重要。目前已有很多匹配区适配性分析特征指标,但都只能片面说明匹配区的适配性。试图将多种特征指标进行综合,解决不同特征指标对适配性分析的冲突性,提出一种特征指标适配置信度和不确定置信度的度量方法,利用Dempster-Shafer (D-S)证据理论对置信度进行融合,得出匹配区适配综合置信度,实验验证了该算法的有效性。

关 键 词:适配性  特征指标  Dempster-Shafer  (D-S)证据理论  景象匹配  视觉导航  
收稿时间:2012-12-11
修稿时间:2013-02-08

Multi-feature suitability analysis of matching area based on D-S theory
CHEN Xueling ZHAO Chunhui LI Yaojun CHENG Yongmei.Multi-feature suitability analysis of matching area based on D-S theory[J].journal of Computer Applications,2013,33(6):1665-1669.
Authors:CHEN Xueling ZHAO Chunhui LI Yaojun CHENG Yongmei
Affiliation:School of Automation, Northwestern Polytechnical University, Xi’an Shaanxi 710072, China
Abstract:The suitability analysis of matching area plays a significant role in the field of vision-based navigation. There are many feature indexes that can only unilaterally describe the suitability of matching area. An algorithm was proposed to integrate several feature indexes to solve conflicts among different feature indexes and provide a kind of method that can measure the suitable confidence and unsuitable confidence of a feature.And then the confidences were fused by using the Dempster-Shafer (DS) rules. At last the algorithm was verified by simulation experiment.
Keywords:suitability                                                                                                                          feature index                                                                                                                          Dempster-Shafer (D-S) theory                                                                                                                          scene matching                                                                                                                          vision-based navigation
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