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文摘
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针对增强现实中的三维注册问题,提出一种基于机器学习的图像自然特征点识别方法.基于高斯混合模型进行样本选择,利用模式识别中的分类方法替代特征向量的最近邻匹配,将计算负担从实时阶段转移到训练阶段,利用各匹配点对之间的相似度计算核密度估计的权值,实现相关平面目标的跟踪.实验结果表明,该方法实时性好、相机位姿估计精确,对光照、遮挡、透视等变化具有较强的鲁棒性 |
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其他语种文摘
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A natural feature recognition method based on machine learning is proposed for 3D registration in augmented reality application. This method increases the accuracy of key-points recognition and moves the computational burden from runtime matching to offline training by substituting specific classification for nearest-neighbor searching. Robust camera tracking and pose estimation can be obtained by the similarity of these matched key-points and the homography matrix. Experimental results demonstrate that this method is suitable for real-time application and is stable against illumination change, occlusion and perspective effect |
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来源
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计算机工程
,2010,36(20):182-184,190 【核心库】
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关键词
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机器学习
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自然特征
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增强现实
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三维注册
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地址
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上海大学计算机工程与科学学院, 上海, 200072
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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1000-3428 |
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学科
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自动化技术、计算机技术 |
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基金
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国家科技支撑计划项目
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上海市重点学科建设项目
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文献收藏号
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CSCD:4035524
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被引
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