Computer Vision and Image Understanding
Multiband segmentation based on a hierarchical Markov model
Pattern Recognition
Multiscale skewed heavy tailed model for texture analysis
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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The work is addressed to provide realistic modeling of generic noise probability density functions (pdfs), in order to optimize signal detection in non-Gaussian environments. The target is to obtain a model depending on few parameters (quick and easy to estimate), and so general to be able to describe many kinds of noise (e.g., symmetric or asymmetric, with variable sharpness). To this end a new HOS-based model is introduced which derives from the generalized Gaussian function and depends on three parameters: kurtosis (fourth order), for representing variable sharpness, and left and right variances (whose combination provides the same information of skewness - third order) for describing deviation from symmetry. The model is applied in the design of a LOD test for detecting signals corrupted by real underwater acoustic noise in a low-frequency range.