基于机器学习的自然特征匹配方法  

Natural Feature Matching Method Based on Machine Learning

作　　者：黄诗华[1] 陈一民[1] 陆意骏[1] 陈明[1] 姚争为[1] 

机构地区：[1]上海大学计算机工程与科学学院,上海200072

出　　处：《计算机工程》2010年第20期182-184,共3页Computer Engineering

基　　金：国家科技支撑计划基金资助项目(2006BAK13B10);上海市重点学科建设基金资助项目(J50103)

摘　　要：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。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.

关 键 词：机器学习 自然特征 增强现实 三维注册 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]