An effective scheme for image texture classification based on binary local structure pattern

[1]  Yang Zhao,et al.  Completed robust local binary pattern for texture classification , 2013, Neurocomputing.

[2]  M. Amirani,et al.  Matrix based cyclic spectral estimator for fast and robust texture classification , 2013, The Visual Computer.

[3]  R. Balasubramanian,et al.  Local maximum edge binary patterns: A new descriptor for image retrieval and object tracking , 2012, Signal Process..

[4]  Subrahmanyam Murala,et al.  Local Tetra Patterns: A New Feature Descriptor for Content-Based Image Retrieval , 2012, IEEE Transactions on Image Processing.

[5]  Fakhry M. Khellah,et al.  Texture Classification Using Dominant Neighborhood Structure , 2011, IEEE Transactions on Image Processing.

[6]  Yong Xu,et al.  A new texture descriptor using multifractal analysis in multi-orientation wavelet pyramid , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[7]  Zhenhua Guo,et al.  A Completed Modeling of Local Binary Pattern Operator for Texture Classification , 2010, IEEE Transactions on Image Processing.

[8]  Peter Lambert,et al.  Noise- and compression-robust biological features for texture classification , 2010, The Visual Computer.

[9]  Xiaoyang Tan,et al.  Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions , 2007, IEEE Transactions on Image Processing.

[10]  Andrew Zisserman,et al.  A Statistical Approach to Material Classification Using Image Patch Exemplars , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Yong Xu,et al.  Viewpoint Invariant Texture Description Using Fractal Analysis , 2009, International Journal of Computer Vision.

[12]  Shu Liao,et al.  Dominant Local Binary Patterns for Texture Classification , 2009, IEEE Transactions on Image Processing.

[13]  C. Schmid,et al.  Description of Interest Regions with Center-Symmetric Local Binary Patterns , 2006, ICVGIP.

[14]  Yong Xu,et al.  A Projective Invariant for Textures , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[15]  Cordelia Schmid,et al.  Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[16]  Cordelia Schmid,et al.  A sparse texture representation using local affine regions , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Andrew Zisserman,et al.  A Statistical Approach to Texture Classification from Single Images , 2004, International Journal of Computer Vision.

[18]  Andrew Zisserman,et al.  Texture classification: are filter banks necessary? , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[19]  Matti Pietikäinen,et al.  Outex - new framework for empirical evaluation of texture analysis algorithms , 2002, Object recognition supported by user interaction for service robots.

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

[21]  Tieniu Tan,et al.  Brief review of invariant texture analysis methods , 2002, Pattern Recognit..

[22]  Cedric Nishan Canagarajah,et al.  Robust rotation invariant texture classification , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[23]  Shree K. Nayar,et al.  Reflectance and texture of real-world surfaces , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[24]  R. K. Goyal,et al.  Scale and rotation invariant texture analysis based on structural property , 1995, Proceedings of IECON '95 - 21st Annual Conference on IEEE Industrial Electronics.

[25]  A. Kundu,et al.  Rotation and gray-scale transform invariant texture recognition using hidden Markov model , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[26]  F. S. Cohen,et al.  Classification of Rotated and Scaled Textured Images Using Gaussian Markov Random Field Models , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  G. EICHMANN,et al.  Topologically invariant texture descriptors , 1988, Comput. Vis. Graph. Image Process..

[28]  Rangasami L. Kashyap,et al.  A Model-Based Method for Rotation Invariant Texture Classification , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  J. Duvernoy Optical-digital processing of directional terrain textures invariant under translation, rotation, and change of scale. , 1984, Applied optics.

[30]  Larry S. Davis,et al.  Polarograms: A new tool for image texture analysis , 1979, Pattern Recognit..