The segmentation of an image can be presented as an inverse ill-posed problem. The segmentation problem is presented as, knowing an observed image g, how to obtain an original image f in which a classification in statistically homogeneous regions must be established. The inversion technique we use is done in a Bayesian probabilistic framework. Prior hypothesis, made on different parameters of t...

Speech segmentation is one of the problems in speech processing area. The main techniques that attempt to solve it are manual segmentation and hidden Markov models alignment. In this work a new technique based on an evolutionary algorithm that permits to segment the speech without previous training process is presented.

tion problem is to decide, from the observed image, in This paper proposes a new unsupervised fuzzy Bayesian which class each pixel lies. In the first case we speak of image segmentation method using a recent model using hidden fuzzy segmentation, and in the second case of hard segmenfuzzy Markov fields. The originality of this model is to use tation. As we can see, fuzzy and hard segmentations...

Different segmentation of lung nodules using the same algorithm can easily lead to excessive errors. Therefore, it is necessary design an effective improve image accuracy. Based on hidden Markov model, this study processed ultrasound images pulmonary their diagnostic results. At time, was combined with process images. In addition, combines convex hull for processing, uses improved vector method...