Fast Scene Matching Method Based on Scale Invariant Feature Transform
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摘要:
传统基于特征的景象匹配方法存在冗余点多、匹配精度低等问题，难以同时满足实时性及鲁棒性要求，对此，论文提出一种基于尺度不变特征变换(SIFT)的快速景象匹配方法。在特征提取阶段，采用高速分段特征检测器(FAST)在多尺度检测角点作为初始特征，经过高斯差分(DOG)算子在尺度空间中进行特征的2次筛选，简化原有遍历式的特征搜索过程；在特征匹配阶段，采用仿射模型模拟变换关系建立几何约束条件，克服SIFT算法由于忽略几何信息而产生的误匹配。实验表明：该方法在匹配精度和实时性方面均优于SIFT算法，且对光照、模糊、尺度等变换具有良好的鲁棒性，能够更好地实现景象匹配。
Abstract:The traditional feature-based image matching method has many problems such as many redundant points and low matching accuracy, which can hardly meet the real-time and robustness requirements. In this regard, a fast scene matching method based on Scale Invariant Feature Transform (SIFT) is proposed. In the feature detection phase, FAST (Features from Accelerated Segment Test) is used to detect characteristics in multi-scale, after then, combining with Difference Of Gauss (DOG) operators to filter characteristics again. From this, the feature search process is simplified. In feature matching phase, the affine transformation model is used to simulate the transformation relation and establish the geometric constraint, to overcome the mismatching because of ignoring the geometric information. The experimental results show that the proposed method is superior to the SIFT in efficiency and precision, also has good robustness to light, blur and scale transformation, achieves scene matching better.
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表 1 相关性实验数据
图像 DOG特征点数 FAST特征点数 DOG∩FAST 重复率(%) Img1 2261 3531 627 27.7 Img2 1840 2276 418 22.7 Img3 3195 2168 711 32.8 Img4 2020 1523 473 23.4 Img5 8143 8615 3013 37.0 Img6 7788 9176 3060 39.3 Img7 2812 1491 573 38.4 Img8 3214 2003 645 32.2 
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表 2 本文算法与SIFT算法消耗时间对比(ms)
数据集 SIFT SURF I-SIFT 特征检测时间 特征匹配时间 总时间 特征检测时间 特征匹配时间 总时间 特征检测时间 特征匹配时间 总时间 graffiti 30574 4082 34656 7727 1021 8748 13866 2984 16850 bikes 16652 2779 19431 4098 884 4982 5216 1052 6268 boat 41933 6093 48026 15010 3235 18245 34397 7923 42320 leuven 14502 2248 16750 3458 487 3945 8518 1369 9887 average 25915 3801 29716 7573 1407 8980 15499 3332 18831
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