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基于局部不变性特征的无人机影像特征点提取 被引量:1

UAV Image Feature Extraction Based on Local Invariant Features
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摘要 针对不同尺度影像特征点提取的问题,提出了基于局部不变性特征的算法。以经典的SIFT特征点检测算法为参照,详细分析了SURF特征点检测算法,并通过实验从特征点提取速度和适应性2个方面对Moravec、Harris、SUSAN、SIFT、SURF等算法进行了比较。结果表明,SURF算法提取影像特征点的速度较快、适应性较强。 According to the problems of different scales image feature extraction, this paper proposed an algorithm based on local invariant features. Referring to the classic SIFT algorithm, the paper analyzed the most popular SURF feature detection algorithm, and compared Moravec algorithm, Harris algorithm, SUSAN algorithm, SIFT algorithm with SURF algorithm in feature extraction speed and adaptability through experimental study. Experimental results show that the SURF algorithm is better than other algorithm in extraction speed and adaptability.
作者 甘洁
出处 《地理空间信息》 2015年第3期47-49,9,共3页 Geospatial Information
关键词 影像 特征点提取 算法 局部不变性特征 SIFT SURF image,feature extraction,algorithm,local invariant feature,SIFT,SURF
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参考文献9

  • 1谌一夫.高分辨率遥感影像几何纠正方法[J].地理空间信息,2012,10(5):5-7. 被引量:6
  • 2Harris C,Stephens M.A Combined Corner and Edge Detector[C].The 4th Alvey Vision Conference,1988.
  • 3Smith S M,Brady M.SUSAN-A New Approach to Low Jevel Image Processing[J].International Journal of Computer Vision,1997,23(1):21-26.
  • 4Lowe D G.Object Recognition from Local Scale-Invariant Features[C].International Conference on Computer Vision,1999.
  • 5李芳芳,肖本林,贾永红,毛星亮.SIFT算法优化及其用于遥感影像自动配准[J].武汉大学学报(信息科学版),2009,34(10):1245-1249. 被引量:61
  • 6李欢欢,黄山,张洪斌.基于Harris与SIFT算法的自动图像拼接[J].计算机工程与科学,2012,34(11):104-108. 被引量:20
  • 7王力勇.无人机低空遥感数字影像自动拼接与快速定位技术研究[D].郑州:信息工程大学,2011.
  • 8Bay H,Tuvetllars T,Van G L.SURF:Speeded up Robust Features[C].Conference on Computer Vision,2006.
  • 9Schmid C,Mohr R,Bauckhage C.Evaluation of Interest Point Detectors[J].International Journal of Computer Vision,2000,37(2):151-172.

二级参考文献22

  • 1张祖勋,张剑清.广义点摄影测量及其应用[J].武汉大学学报(信息科学版),2005,30(1):1-5. 被引量:57
  • 2张祖勋,张宏伟,张剑清.基于直线特征的遥感影像自动绝对定向[J].中国图象图形学报(A辑),2005,10(2):213-217. 被引量:19
  • 3张祖勋 张剑清.数字摄影测量[M].武汉:武汉测绘科技大学出版社,1997.180-190.
  • 4Lowe D G. Distinctive Image Features from Scaleinvariant Keypoints [J]. International Journal of Computer Vision, 2004, 60(2):91-110.
  • 5Brown M, Lowe D G. Recognizing Panoramas [C]. The 9th International Conference on Computer Vision (ICCV03), Nice, 2003.
  • 6Schafalitzky F, Zisserm an A. Multi-view Matching for Unordered Image Sets, or How Do I Organize My Holiday Snaps[C]. The 7th European Conference on Computer Vision (ECCV02), Berlin, 2002.
  • 7Lowe D G. Object Recognition from Local Scale-Invariant Features [C]. International Conference on Computer Vision, Corfu, Greece, 1999.
  • 8Lowe D G. Distinctive Image Features from Scaleinvariant Keypoints[J]. International Journal of Computer Vision, 2004, 60(2) : 91-110.
  • 9Mikolajczyk K, Schmid C. A Performance Evaluation of Local Descriptors [J]. IEEE Trans Pattern Analysis and Machine Intelligence, 2005, 27(10):1 615-1 630.
  • 10Fischler M, Bolles R. Random Sample Consensus: a Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography [J]. ACM, Graphics and Image Processing, 1981, 24 (6) :381-395.

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