A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introductory Digital Image Processing: A Remote Sensing Perspective
Introductory Digital Image Processing: A Remote Sensing Perspective
Digital Image Processing
Hidden Markov tree modeling of complex wavelet transforms
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 01
Contourlet Image Modeling with Contextual Hidden Markov Models
SSIAI '06 Proceedings of the 2006 IEEE Southwest Symposium on Image Analysis and Interpretation
Wavelet-based statistical signal processing using hidden Markovmodels
IEEE Transactions on Signal Processing
IEEE Transactions on Multimedia
Shiftable multiscale transforms
IEEE Transactions on Information Theory - Part 2
Spatially adaptive wavelet thresholding with context modeling for image denoising
IEEE Transactions on Image Processing
Multiscale image segmentation using wavelet-domain hidden Markov models
IEEE Transactions on Image Processing
Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance
IEEE Transactions on Image Processing
Texture classification using spectral histograms
IEEE Transactions on Image Processing
The contourlet transform: an efficient directional multiresolution image representation
IEEE Transactions on Image Processing
Directional multiscale modeling of images using the contourlet transform
IEEE Transactions on Image Processing
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The contourlet transform is an emerging multiscale multidirection image processing technique. It effectively represents smooth curvature details typical of natural images, overcoming a major drawback of the 2-D wavelet transform. Previously, we developed a contourlet image model, that is, the contourlet contextual hidden Markov model (C-CHMM). In this paper, we further develop a multiscale texture segmentation technique based on the C-CHMM. The segmentation method combines a model comparison approach with a multiscale fusion and a neighbor combination process. It also features a neighborhood selection scheme based on smoothed context maps, for both model estimation and neighbor combination. Through a series of segmentation experiments, we examine the effectiveness of the C-CHMM in comparison with closely related models. We also investigate how different context designs affect the segmentation performance. Moreover, we show that the C-CHMM based technique provides improved accuracy in segmenting texture patterns of diversified nature, as compared with popular methods such as the HMTseg and the JMCMS. All these simulation experiments demonstrate the great potential of the C-CHMM for image analysis applications.