大仿射场景的混合特征提取与匹配  

Mixed Feature Extraction and Matching for Large Affine Scene

作　　者：佟国峰[1] 李勇[1] 刘楠[1] 纪光旭 

机构地区：[1]东北大学信息科学与工程学院,辽宁沈阳110819

出　　处：《光学学报》2017年第11期207-214,共8页Acta Optica Sinica

基　　金：国家863计划(2012AA041402);中国科技部国家重点科技研究发展计划(2015BAF13B00-5)

摘　　要：为了提高大规模场景三维重建中的精度,在保证算法效率的前提下,提取两种局部稳定不变特征,并采用多特征融合方法进行匹配。针对基于航拍影像和城市街景图像联合建模存在的问题,提出了一种两种局部稳定特征匹配的方法。其步骤为:先提取ASIFT(Affine Scale Invariant Feature Transform)特征点和MSER(Maximally Stable Extremal Regions)特征区域,并对MSER算法进行改进,得到这两种稳定的图像特征;再用SIFT(Scale Invariant Feature Transform)特征描述符对其进行描述;最后利用基于单应矩阵的特征匹配算法进行匹配。利用图形处理单元(GPU)对特征匹配环节进行并行优化处理。大量实验及对比结果表明,本文算法可以得到两种单一算法两倍以上的正确匹配对。In order to improve accuracy of large-scale scene model in three-dimensional （3D） reconstruction, we extract two kinds of partial stable invariant features, under the premise of ensuring the efficiency of the algorithm, and use a multi-feature fusion method to match image features. Considering both problems of the joint modeling based on aerial and urban street images, we propose a matching method based on the two kinds of partial stable features. The method comprises the following steps. Firstly, we extract ASIFT （Affine Scale Invariant Feature Transform） feature points and MSER feature areas, and improve the MSER （Maximally Stable Extremal Regions） algorithm to get the two stable features described by SIFT （Scale Invariant Feature Transform） feature descriptor; secondly, we use the homography matrix to match features by the feature matching algorithm; finally, we parallelly optimize feature matching by using graphics processing unit（GPU）. A large number of experiments and comparison results show that more than twice correct matching pairs can be obtained by the proposed algorithm than other two algorithms.

关 键 词：机器视觉 三维重建 特征融合 特征匹配 仿射场景 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]