Opto-Electronic Engineering, Volume. 46, Issue 11, 180604(2019)
Texture target classification with CLBP and local geometric features
[1] [1] El Merabet Y, Ruichek Y. Local concave-and-convex mi-cro-structure patterns for texture classification[J]. Pattern Rec-ognition, 2018, 76: 303–322.
[3] [3] Haralick R M, Shanmugam K, Dinstein I. Textural features for image classification[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1973, SMC-3(6): 610–621.
[4] [4] Manjunath B S, Ma W Y. Texture features for browsing and retrieval of image data[J]. IEEE Transactions on Pattern Analy-sis and Machine Intelligence, 1996, 18(8): 837–842.
[5] [5] Porter R, Canagarajah N. Robust rotation invariant texture classification[C]//Proceedings of 1997 IEEE International Con-ference on Acoustics, Speech, and Signal Processing, 1997: 3157–3160.
[6] [6] Ojala T, Pietik.inen M, M.enp.. T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7): 971–987.
[8] [8] Tan X Y, Triggs B. Enhanced local texture feature sets for face recognition under difficult lighting conditions[J]. IEEE Transac-tions on Image Processing, 2010, 19(6): 1635–1650.
[9] [9] Guo Z H, Zhang L, Zhang D. A completed modeling of local binary pattern operator for texture classification[J]. IEEE Transactions on Image Processing, 2010, 19(6): 1657–1663.
[10] [10] Zhao Y, Huang D S, Jia W. Completed local binary count for rotation invariant texture classification[J]. IEEE Transactions on Image Processing, 2012, 21(10): 4492–4497.
[11] [11] Dalal N, Triggs B. Histograms of oriented gradients for human detection[C]//Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005: 886–893.
[12] [12] Do Carmo M P. Differential Geometry of Curves and Surfaces [M]. Englewood Cliffs: Prentice-Hall, 1976.
[13] [13] Mellor M, Hong B W, Brady M. Locally rotation, contrast, and scale invariant descriptors for texture analysis[J]. IEEE Trans-actions on Pattern Analysis and Machine Intelligence, 2008, 30(1): 52–61.
[14] [14] Zhang J, Zhao H, Liang J M. Continuous rotation invariant local descriptors for texton dictionary-based texture classification[J]. Computer Vision and Image Understanding, 2013, 117(1): 56–75.
[15] [15] Hanbay K, Alpaslan N, Talu M F, et al. Principal curvatures based rotation invariant algorithms for efficient texture classifi-cation[J]. Neurocomputing, 2016, 199: 77–89.
[16] [16] Hanbay K, Alpaslan N, Talu M F, et al. Continuous rotation invariant features for gradient-based texture classification[J]. Computer Vision and Image Understanding, 2015, 132: 87–101.
[17] [17] Liao S, Law M W K, Chung A C S. Dominant local binary pat-terns for texture classification[J]. IEEE Transactions on Image Processing, 2009, 18(5): 1107–1118.
Get Citation
Copy Citation Text
Kou Qiqi, Cheng Deqiang, Yu Wenjie, Li Huayu. Texture target classification with CLBP and local geometric features[J]. Opto-Electronic Engineering, 2019, 46(11): 180604
Category: Article
Received: Nov. 21, 2018
Accepted: --
Published Online: Dec. 8, 2019
The Author Email: Qiqi Kou (kouqiqi@cumt.edu.cn)