Abstract
Multiresolution models such as the wavelet-domain hidden Markov tree (HMT) model provide a powerful approach for image modeling and processing because it captures the key features of the wavelet coefficients of real-world data. It is observed that the Laplace distribution is peakier in the center and has heavier tails compared with the Gaussian distribution. Thus we propose a new HMT model based on the two-state, zero-mean Laplace mixture model (LMM), the LMM-HMT, which provides significantly potential for characterizing real-world textures. By using the HMT segmentation framework, we develop LMM-HMT based segmentation methods for image textures and dynamic textures. The experimental results demonstrate the effectiveness of the introduced model and segmentation methods.
Highlights
Texture is an important component of natural images, which provides abundant cues for visual information recognition and understanding
In order to evaluate the texture segmentation method based on the wavelet domain Laplace mixture model (LMM)-hidden Markov tree (HMT)
In orderwe to evaluate segmentation method based on the wavelet domain LMM-HMT
Summary
Texture is an important component of natural images, which provides abundant cues for visual information recognition and understanding. Dynamic texture analysis has attracted much attention. Dynamic textures are video sequences of complex dynamical objects such as smoke, fire, sea waves, foliage waving in wind, moving escalators, and swinging flags, which exhibit certain stationary properties in time [2]. They provide important visual cues for various video processing problems. Texture analysis is still an important and interesting research field [3,4,5,6,7,8]
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