Estimation of Classifier Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
An iterative Gibbsian technique for reconstruction of m-ary images
Pattern Recognition
Unsupervised segmentation of noisy and textured images using Markov random fields
CVGIP: Graphical Models and Image Processing
Adaptive mixture estimation and unsupervised local Bayesian image segmentation
Graphical Models and Image Processing
Evaluation and comparison of different segmentation algorithms
Pattern Recognition Letters
An Unsupervised and Non-Parametric Bayesian Image Segmentation
ICIAP '95 Proceedings of the 8th International Conference on Image Analysis and Processing
SSAP '96 Proceedings of the 8th IEEE Signal Processing Workshop on Statistical Signal and Array Processing (SSAP '96)
Estimation of fuzzy Gaussian mixture and unsupervised statistical image segmentation
IEEE Transactions on Image Processing
Estimation of generalized mixtures and its application in image segmentation
IEEE Transactions on Image Processing
Unsupervised Bayesian image segmentation using orthogonal series
Journal of Visual Communication and Image Representation
Non-parametric and region-based image fusion with Bootstrap sampling
Information Fusion
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We propose here an unsupervised Bayesian classifier based on a non-parametric expectation-maximization algorithm. The non-parametric aspect comes from the use of the orthogonal probability density function (pdf) estimation, which is reduced to the estimation of the first Fourier coefficients of the pdf with respect to a given orthogonal basis. So, the mixture identification step based on the maximization of the likelihood can be realized without hypothesis on the conditional pdf's distribution. This means that for the unsupervised image segmentation example we do not need any assumption for the gray level image pixels distribution. The generalization to the multivariate case can be obtained by considering the multidimensional orthogonal function basis. In this paper, we give some simulation results for the determination of the smoothing parameter and to compute the error of classification.