An Improved Harris-SIFT Algorithm Based on Rotation-invariant LBP Operator

Feature-points matching is an important concept in binocular stereo vision. The procession of multi-scale feature-points matching in classical Harris-SIFT algorithm is time-consuming and has high complexity when describing the feature-points. This paper proposed a new improved Harris-SIFT algorithm based on rotation-invariant LBP (Local binary patterns) operator. Firstly, the Harris operator is used to extract feature points from DOG (Difference of Gaussian) scale space. Then, the dominant direction of feature point is calculated and 81-dimensional rotation-invariant LBP descriptors are extracted when the rotation matching window is coordinated to this direction. At last, Best-Bin-First (BBF) algorithm is used to search the matching points between the two sets of feature points. Experimental results show that the proposed algorithm is lower time-consuming than classical Harris-SIFT algorithm and remains the similar matching correct rate.

[1]  Marko Heikkilä,et al.  Description of interest regions with local binary patterns , 2009, Pattern Recognit..

[2]  Cordelia Schmid,et al.  Evaluation of Interest Point Detectors , 2000, International Journal of Computer Vision.

[3]  Matti Pietikäinen,et al.  Performance evaluation of texture measures with classification based on Kullback discrimination of distributions , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[4]  Robert Laganière,et al.  Detecting and matching feature points , 2005, J. Vis. Commun. Image Represent..

[5]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[6]  Cordelia Schmid,et al.  A Performance Evaluation of Local Descriptors , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Zhao Dong-biao.  Harris-SIFT Algorithm and Its Application in Binocular Stereo Vision , 2010 .

[8]  Lijun Cai,et al.  A generalized local binary pattern descriptor and its applications to texture retrieval , 2012, 2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI).

[9]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  J. Koenderink.  The structure of images , 2004, Biological Cybernetics.

[11]  Di Huang,et al.  Local Binary Patterns and Its Application to Facial Image Analysis: A Survey , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[12]  Li Wang,et al.  Feature Representation for Facial Expression Recognition Based on FACS and LBP , 2014, Int. J. Autom. Comput..

[13]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[14]  Yan Ke,et al.  PCA-SIFT: a more distinctive representation for local image descriptors , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[15]  R. Venkatesh Babu,et al.  Speeding up SIFT using GPU , 2013, NCVPRIPG.

[16]  Chao Cai,et al.  A Multi-sensor Image Registration Method Based on Harris Corner Matching , 2006, VSMM.

[17]  Dancheng Li,et al.  3D Point Sets Matching Method Based on Moravec Vertical Interest Operator , 2012 .

[18]  Weng Muyun,et al.  Image Feature Detection and Matching Based on SUSAN Method , 2006, First International Conference on Innovative Computing, Information and Control - Volume I (ICICIC'06).

[19]  Nathan S. Netanyahu,et al.  An Efficient SIFT-Based Mode-Seeking Algorithm for Sub-Pixel Registration of Remotely Sensed Images , 2015, IEEE Geoscience and Remote Sensing Letters.

[20]  Liu Yu-tin.  Application of improved Harris-SIFT algorithm in binocular stereo vision , 2014 .

[21]  S. Malathi,et al.  Improved Partial Fingerprint Matching Based on Score Level Fusion Using Pore and SIFT Features , 2011, 2011 International Conference on Process Automation, Control and Computing.

[22]  Liu Weiguang.  Advanced algorithm based on SIFT and its application in binocular stereo vision , 2013 .