Statistical image modeling in the contourlet domain with application to texture segmentation
Younan, Nicholas H.
Fowler, James E.
Moorhead II, Robert J.
King, Roger L.
Date of Degree
Original embargo terms
MSU Only Indefinitely
Dissertation - Open Access
Doctor of Philosophy
James Worth Bagley College of Engineering
Department of Electrical and Computer Engineering
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. To further exploit its potential, in this research, a statistical model, the contourlet contextual hidden Markov model (C-CHMM), has been developed to characterize contourlet images. A systematic mutual information based context construction procedure has been developed to form an appropriate context for the model. With this contourlet image model, a multiscale segmentation method has also been established for the application to texture images. The segmentation method combines a model comparison approach with a multiscale fusion and a multi-neighbor combination process. It also features a neighborhood selection scheme based on a smoothed context map, for both the model estimation and the neighbor combination. The effectiveness of the image model has been verified through a series of denoising and segmentation experiments. As demonstrated with the denoising performance, this new model for contourlet images is more promising than the state of the art, the contourlet hidden Markov tree (C-HMT) model. The other model being compared with in this work is the wavelet contextual hidden Markov model (W-CHMM). Through the denoising experiments, the presented C-CHMM shows better robustness against noise than the W-CHMM. Moreover, the new model demonstrates its superiority to the wavelet model in the segmentation performance. Through the segmentation experiments, the value of the systematic context construction procedure has been proven. The C-CHMM based segmentation method has also been validated. In comparison with the state of the art methods for the same type, the presented technique shows improved accuracy in segmenting texture patterns of diversified nature. This success in segmentation has further manifested the potential of the newly developed contourlet image model.
Long, Zhiling, "Statistical image modeling in the contourlet domain with application to texture segmentation" (2007). Theses and Dissertations MSU. 4054.