JIA Di, ZHAO Mingyuan, CAO Jun, et al., “A Method of Non-textured Regions Matching,” Chinese Journal of Electronics, vol. 28, no. 3, pp. 598-603, 2019, doi: 10.1049/cje.2019.02.008
Citation: JIA Di, ZHAO Mingyuan, CAO Jun, et al., “A Method of Non-textured Regions Matching,” Chinese Journal of Electronics, vol. 28, no. 3, pp. 598-603, 2019, doi: 10.1049/cje.2019.02.008

A Method of Non-textured Regions Matching

doi: 10.1049/cje.2019.02.008
Funds:  This work is supported by the National Natural Science Foundation of China (No.61601213), Postdoctoral Research Foundation of China (No.2017M611252), and Liaoning Provincial Education Department Project (China) (No.LR2016045).
  • Received Date: 2018-06-27
  • Publish Date: 2019-05-10
  • It is difficult for existing dense or quasidense matching algorithms to obtain accurate matching results due to the lack of effective feature information in non-textured regions. This may affect the quality of subsequent 3D reconstruction and super-resolution reconstruction. We propose a method of non-textured regions matching. To reduce the impact of noise and illumination, vector sampling normalized cross correlation is proposed to directly measure coherence between two colour images by the effective information of multi-channel features. Three mathematical properties are used in affine transformation: 1) Centroid location is not affected by affine transformation; 2) Affine transformation transforms a straight line into another straight line; 3) Affine transformation maintains the linear relation invariant. In non-textured regions, we can construct texture regions which have affine invariant properties to improve the accuracy of template matching. This could provide more information for the diffusion of dense or quasi-dense matching. In experiments, we demonstrate that the method has high accuracy in cases where there is no big distortion between the two viewing angles from two aspects of simulated images and photographic images.
  • loading
  • D.G. Lowe, "Distinctive image features from scale invariant keypoints", International Journal of Computer Vision, Vol.60, No.2, pp.91-110, 2004.
    H. Bay, A. Ess, T. Tuytelaars, et al., "Speeded-up robust features (SURF)", Computer Vision and Image Understanding, Vol.110, No.3, pp.346-359, 2008.
    Y.D. Lyu, X.J. Shen, H.P. Chen, "Copy-paste detection based on a SIFT marked graph feature vector", Chinese Journal of Electronics, Vol.26, No.2, pp.345-350, 2017.
    Y.B. Men, G.Y. Zhang, C.G. Men, et al., "A subpixel disparity refinement algorithm based on lagrange interpolation", Chinese Journal of Electronics, Vol.26, No.4, pp.784-789, 2017.
    F. Wang, H.J. You, X.Y. Fu, et al., "Cascade SIFT matching method for multi-Source SAR images", Acta Electronica Sinica, Vol.44, No.3, pp.548-554, 2016. (in Chinese)
    X.Q. Zhao, Z.D. Yue, "A fast matching algorithm based on local binary patterns and graph transformation", Acta Electronica Sinica, Vol.45, No.9, pp.2156-2161, 2017. (in Chinese)
    G.S. Yu and J.M. Morel, "ASIFT:An algorithm for fully affine invariant comparison", Image Processing on Line, Vol.1, pp.11-38, 2011.
    E. Rublee, V. Rabaud, K. Konolige, et al., "ORB:An efficient alternative to SIFT or SURF", IEEE International Conference on Computer Vision, pp.2564-2571, 2011.
    Z. Wang, B. Fan and F. Wu, "Local intensity order pattern for feature description", IEEE International Conference on Computer Vision, pp.603-610, 2016.
    Y. Verdie,K. Yi, P.Fua, et al., ‘TILDE:a temporally invariant learned Detector", IEEE Conference on Computer Vision and Pattern Recognition, pp.5279-5288, 2015.
    K.M. Yi, E. Trulls, V. Lepetit, et al., "Lift:Learned invariant feature transform", European Conference on Computer Vision, pp.467-483, 2016.
    J.X. Wang, W.X. Wang, X.M. Li, et al., "Line matching algorithm for aerial image combining image and object space similarity constraints", International Society for Photogrammetry and Remote Sensing, Vol.XLI-B3, pp.783-788, 2016.
    L. Juan, S. Roi, F. XoséR, et al., "Two-view line matching algorithm based on context and appearance in low textured images", Pattern Recognition,, Vol.48, No.7, pp.2164-2184, 2015.
    A.O. Ok, J.D. Wegner, C. Heipke, et al., "Matching of straight line segments from aerial stereo images of urban areas", ISPRS Journal of Photo Grammetry and Remote Sensing, Vol.74, pp.133-152, 2012.
    H. Kim and S. Lee, "Simultaneous line matching and epipolar geometry estimation based on the intersection context of coplanar line pairs", Pattern Recognition Letters, Vol.33, No.10, pp.1349-1363, 2012.
    B. Wu, Y.S. Zhang and Q. Zhu, "Integrated point and edge matching on poor textural images constrained by self adaptive triangulations", ISPRS Journal of Photo Grammetry and Remote Sensing, Vol.68, pp.40-55, 2012.
    S. Korman, D. Reichman, G. Tsur, et al., "Fast-match:Fast affine template matching", IEEE Conference on Computer Vision and Pattern Recognition, pp.2331-2338, 2013.
    D. Jia, J. Cao, W.D. Song, et al., "Colour FAST (CFAST) match:fast affine template matching for colour images", Electronics Letters, Vol.52, No.14, pp.1220-1221, 2016.
    Y.C. Yang, Z.J. Lu and S. Ganesh, ‘Coarse-to-fine region selection and matching", IEEE Conference on Computer Vision and Pattern Recognition, pp.5051-5059, 2015.
    S. Korman, E. Ofek and S. Avidan, "Peeking template matching for depth extension", IEEE International Conference on Computer Vision, pp.5051-5059, 2015.
    T. Dekel, S. Oron, M. Rubinstein, et al., "Best-buddies similarity for robust template matching", IEEE International Conference on Computer Vision, pp.2174-2182, 2015.
    S. En, C. Petitjean, S. Nicolas, et al., "Pattern localization in historical document images via template matching", International Conference on Pattern Recognition, pp.2054-2059, 2016.
    S. Belongie, J. Malik and J. Puzicha, "Shape matching and object recognition using shape contexts", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.24, No.4, pp.509-522, 2002.
    K. Mikolajczyk, T. Tuytelaars, C. Schmid, et al., "A comparison of affine region detectors", International Journal of Computer Vision, Vol.65, No.1-2, pp.43-72, 2005.
    H. Yang, M. Yu and S. Zhang, "Wide baseline stereo matching based on scale invariant feature transformation with hybrid geometric constraints", IET Computer Vision, Vol.8, No.6, pp.611-619, 2014.
    Y. Wan, Z. Miao, Q.M.J. Wu, et al., "A quasi-dense matching approach and its calibration application with internet photos", IEEE Transactions on Cybernetics, Vol.45, No.3, pp.370-383, 2015.
    H. Shao, T. Svoboda and G.L. Van, "Zubudzurich buildings database for image based recognition", Tech. Report,, 2003.
    K. Mikolajczyk and C. Schmid, "A performance evaluation of local descriptors", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.27, No.10, pp.1615-1630, 2005.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (394) PDF downloads(186) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return