高斯二阶差分特征算子在图像拼接中的应用
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重庆市高等学校青年骨干教师资助项目(自然科学类);重庆市工业职业技术学院科研项目自然科学基金(GZY201313)


Image Mosaic Method Based on Gaussian Second-Order Difference Feature Operator
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    摘要:

    为了将同一场景中具有重叠区域序列的图像快速准确合成一幅具有宽视角、高分辨率的图像,提出了基于高斯二阶差分(D2oG)特征检测算子的SIFT算法.采用高斯二阶差分(D2oG)金字塔的过零点检测提取图像尺度不变特征点,并选用RANSAC算法对特征点匹配对进行提纯,在此基础上计算不变换矩阵H,最后,用渐进渐出平滑算法完成图像的无缝拼接.实验中分别采用所提方法和SIFT算法对具有典型变换的4种图像进行拼接与测试,结果表明:所提方法提取的匹配点数、拼接所消耗时间明显低于采用SIFT算法,同时匹配效率也高于后者.此方法降低了运算复杂度的同时,图像拼接实时性也得到提高.

    Abstract:

    In order to compose the wide visual angle and high resolution image from the sequence of images which have overlapping region in the same scene quickly and correctly, an improved SIFT algorithm which based on D2oG interest point detector was proposed. It extracted the image feature points and generated corresponding feature descriptors by improved SIFT algorithm. Then, feature point matching pairs were purified using the random consistency(RANSAC) algorithm and the transformation matrix H was caculated. Last, the seamless mosaic of images was completed by using the image fusion algorithm of slipping into and out. The images which had the four typical transformations were separately processed with the traditional SIFT and the proposed method in the experiment. The result indicated that the number of feature pairs is fewer, the mosaic time is shorter and the matching efficiency is higher than that of SIFT algorithm. This method reduces the complexity of operation and improves real-time of image mosaic simultaneously.

    参考文献
    1 林锦梅,周付根,金挺.采用SIFT特征的空基动态视屏稳定技术.红外与激光工程,2011,40(12):2552-2557.
    2 詹斌,李华格,等.遥感图像拼接系统.计算机系统应用, 2014,23(5):31-36.
    3 张玉洁.图像局部不变特征提取与匹配及应用研究[学位论文].南京:南京理工大学,2010.
    4 王威,唐一平,任娟丽,等.一种改进的Harris角点提取算法.光学精密工程,2008,16(10):1995-2001.
    5 Jiang DG, Yi JK.Comparison and study of classic feature point detection algorithm.Computer Science & Service System.Nanjing, China.2012.2307-2309.
    6 Ryu JB, Park HH.Log-log scaled Harris corner detector.Electronics Letters, 2010, 46(24):1602-1604.
    7 Lowe DG.Distinctive image features from scale-invariant keypoints.International Journal of Computer Vision, 2004, 60(20):91-110.
    8 王正林.基于对比度的小波图像融合算法研究.激光与红外,2014,44(9):1043-1044.
    9 曹峦,王元钦,谭久彬.改进的SIFT特征提取和匹配算法.光学精密工程,2011,19(6):1391-1397.
    10 刘向增,田铮,史振广,等.基于FKICA-SIFT特征的合成孔径图像度尺度配准.光学精密工程,2011,19(9):2186-2195.
    11 Zhu ZW, Shen ZF, Luo JC.Parallel remote sensing image registration based on improved SIFT point feature.Journal of Remote Sensing, 2011, 15(5):1024-1031.
    12 Wan LZ, Chong WN.Flip-Invariant SIFT for copy and object detection.IEEE Trans.on Image Processing, 2013, 22(3):980-991.
    13 Zhu XF, Ma CW, Liu B.Target classification using SIFT sequence scale invariants.Systems Engineering and Electronics, 2012, 23(5):633-639.
    14 Yang BLST.Multifocus image fusion and restration with sparse representation.IEEE Trans.Instrumentation And Measurement, 2010, 59(4):884-892.
    15 刘立,彭复员,赵坤,等.采用简化SIFT算法实现快速图像匹配.红外与激光工程,2008,37(1):181-184.
    16 张维中,杨厚俊,张丽艳,等.基于多福图像的同名曲线亚像素匹配算法.北京邮电大学学报,2008,31(4):66-69.
    17 丁小丽.图像拼接技术研究[学位论文].南京:东南大学, 2009.
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徐敏,莫东鸣,张祯.高斯二阶差分特征算子在图像拼接中的应用.计算机系统应用,2016,25(4):167-173

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  • 收稿日期:2015-08-04
  • 最后修改日期:2015-10-08
  • 在线发布日期: 2016-04-19
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