Title :
Unsupervised segmentation of Poisson data
Author :
Nowak, Robert D. ; Figueiredo, Mario A.T.
Author_Institution :
Dept. of Electr. & Comput. Eng., Rice Univ., Houston, TX, USA
Abstract :
Describes an approach to the analysis of Poisson point processes, in time (1D) or space (2D), which is based on the minimum description length (MDL) framework. Specifically, we describe a fully unsupervised recursive segmentation algorithm for 1D and 2D observations. Experiments illustrate the good performance of the proposed methods
Keywords :
encoding; maximum likelihood estimation; stochastic processes; 1D observations; 2D observations; Poisson data; Poisson point processes; minimum description length; unsupervised segmentation; Bayesian methods; Biomedical imaging; Electron microscopy; Electronic mail; Image segmentation; Maximum likelihood decoding; Maximum likelihood estimation; Physics; Statistics; Telecommunications;
Conference_Titel :
Pattern Recognition, 2000. Proceedings. 15th International Conference on
Conference_Location :
Barcelona
Print_ISBN :
0-7695-0750-6
DOI :
10.1109/ICPR.2000.903508
