The use of a robust toolbox for texture classification and segmentation

In this paper, the design and use of a toolbox that integrates several texture analysis algorithms is presented. The most important statistical, spectral and multiresolution methods are implemented. Examples of the toolbox interfaces are given. The interface windows for the algorithms and classifiers are explained. Experimental results are presented which show the application of the toolbox algorithms for image classification and segmentation. Textures that are transformed can also be classified, an example is presented using a wavelet algorithm. Segmentation of remote sensing images is discussed using the co-occurrence matrix method. Classification with extrema features is demonstrated for different sets of images. An application of the algorithm to segmenting industrial images using logical transform algorithm is discussed. The organization of the toolbox is in a hierarchical manner. It also implements auxiliary methods such as edge detection and noise filtering that aid in texture analysis.

[1]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[2]  Ingrid Daubechies,et al.  The wavelet transform, time-frequency localization and signal analysis , 1990, IEEE Trans. Inf. Theory.

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

[4]  Yue Wu,et al.  An efficient method for rotation and scaling invariant texture classification , 1995, 1995 International Conference on Acoustics, Speech, and Signal Processing.

[5]  C.-C. Jay Kuo,et al.  Texture analysis and classification with tree-structured wavelet transform , 1993, IEEE Trans. Image Process..

[6]  T. Lonnestad.  A new set of texture features based on the Haar transform , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol. III. Conference C: Image, Speech and Signal Analysis,.

[7]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Jane You,et al.  Classification and segmentation of rotated and scaled textured images using texture "tuned" masks , 1993, Pattern Recognit..

[9]  R. Porter,et al.  Robust rotation-invariant texture classification: wavelet, Gabor filter and GMRF based schemes , 1997 .

[10]  M. M. Leung,et al.  Scale and rotation invariant texture classification , 1992, [1992] Conference Record of the Twenty-Sixth Asilomar Conference on Signals, Systems & Computers.

[11]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[12]  Bedrich J. Hosticka,et al.  A comparison of texture feature extraction using adaptive gabor filtering, pyramidal and tree structured wavelet transforms , 1996, Pattern Recognit..

[13]  Azriel Rosenfeld,et al.  A Comparative Study of Texture Measures for Terrain Classification , 1975, IEEE Transactions on Systems, Man, and Cybernetics.

[14]  I. Daubechies.  Orthonormal bases of compactly supported wavelets , 1988 .

[15]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[16]  Ramón E. Vásquez,et al.  Logical operators as a new tool for texture classification , 1998, Defense, Security, and Sensing.

[17]  Xiaoou Tang,et al.  Texture information in run-length matrices , 1998, IEEE Trans. Image Process..

[18]  Ezzatollah Salari,et al.  Texture segmentation using hierarchical wavelet decomposition , 1995, Pattern Recognit..

[19]  Phil Brodatz,et al.  Textures: A Photographic Album for Artists and Designers , 1966 .

[20]  Jelena Kovacevic,et al.  Wavelets and Subband Coding , 2013, Prentice Hall Signal Processing Series.

[21]  Truong Q. Nguyen,et al.  Wavelets and filter banks , 1996 .

[22]  Stéphane Mallat,et al.  Singularity detection and processing with wavelets , 1992, IEEE Trans. Inf. Theory.

[23]  Vidya B. Manian,et al.  Scaled and rotated texture classification using a class of basis functions , 1998, Pattern Recognit..

[24]  T. N. Tan,et al.  Scale and rotation invariant texture classification , 1994 .