Pattern Recognition
An analysis of histogram-based thresholding algorithms
CVGIP: Graphical Models and Image Processing
Goal-Directed Evaluation of Binarization Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Bootstrap Coverage Plots for Image Segmentation
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
IEEE Transactions on Image Processing
Segmentation of textured images using a multiresolution Gaussian autoregressive model
IEEE Transactions on Image Processing
Proceedings of the 5th IBM Collaborative Academia Research Exchange Workshop
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This paper presents a novel method of unsupervised segmentation for synthetic aperture radar (SAR) images. Firstly, we define a generalized multiresolution likelihood ratio (GMLR), which classifies different kinds of signals more accurately than classical likelihood ratio by fusing more and different signal features. For our SAR image segmentation application, multiresolution stochastic structure inherent in SAR imagery is well captured by a set of multiscale autoregressive (MAR) models. Secondly, good parameter estimates of GMLR can be obtained by estimating several MMARP models using EM algorithm. Thirdly, considering the independence assumption of maximum likelihood estimation of parameter by EM algorithm and reduction of the segmentation time, we present the bootstrap sampling techniques applied above algorithm. Experimental results demonstrate that our algorithm performs fairly well.