Texture Segmentation Using Voronoi Polygons
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Texture Segmentation Using Markov Random Field Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
Wavelets, fractals, and radial basis functions
IEEE Transactions on Signal Processing
High resolution spectral analysis of images using the pseudo-Wignerdistribution
IEEE Transactions on Signal Processing
Multidimensional quasi-eigenfunction approximations and multicomponent AM-FM models
IEEE Transactions on Image Processing
Optimal Gabor filters for texture segmentation
IEEE Transactions on Image Processing
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
Mapping perceptual texture similarity for image retrieval
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
Image feature extraction using the fusion features of BEMD and WCB-NNSC
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
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This study introduces a new approach based on Bidimensional Empirical Mode Decomposition (BEMD) to extract texture features at multiple scales or spatial frequencies. Moreover, it can resolve the intrawave frequency modulation provided the frequency modulation. This decomposition, obtained by the bidimensional sifting process, plays an important role in the characterization of regions in textured images. The sifting process is realized using morphological operators to analyze the spatial frequencies and thanks to radial basis functions (RBF) for surface interpolation. We modified the original sifting algorithm to permit a pseudo bandpass decomposition of images by inserting scale criterion. Its effectiveness is demonstrated on synthetic and natural textures. In particular, we show that many different elements in textures can be extracted through the bidimensional empirical mode decomposition, which is fully unsupervised.