Image segmentation is a vital part of image processing. Segmentation has its application widespread in the field of medical images in order to diagnose curious diseases. The same medical images can be segmented manually. But the accuracy of image segmentation using the segmentation algorithms is more when compared with the manual segmentation. In the field of medical diagnosis an extensive dive...

Advances in distributed networking have resulted in an explosion in size of modern datasets while storage and processing power continue to lag behind. This requires the need for algorithms that are efficient in terms of number of measurements and running time. To combat challenges associated with large datasets in distributed networks we propose hierarchical intuitionistic fuzzy possibilistic c...

The paper deals with a possibilistic imprecise second-order probability model. It is argued that such models appear naturally in a number of situations. They lead to the introduction of a new type of previsions, called possibilistic previsions, which formally generalise coherent upper and lower previsions. The converse problem is also looked at: given a possibilistic prevision, under what condi...

Many fuzzy clustering based techniques when applied to image segmentation do not incorporate spatial relationships of the pixels, while fuzzy rule-based image segmentation techniques are generally application dependent. Also for most of these techniques, the structure of the membership functions is predefined and parameters have to either automatically or manually derived. This paper addresses ...

To obtain archaeological simulated maps, we need to compare dates of excavation data, represented by fuzzy numbers. Fuzzy Max Order (FMO) is a partial order relation on the set of the fuzzy numbers. But FMO is not able to compare two fuzzy numbers in some situations. In this paper, we propose a new method, Possibilistic Variation Order, extending FMO. We build new indices to order two fuzzy num...

Probability assessments of events are often linguistic in nature. We model them by means of possibilistic probabilities (a version of Zadeh’s fuzzy probabilities with a behavioural interpretation) with a suitable shape for practical implementation (on a computer). Employing the tools of interval analysis and the theory of imprecise probabilities we argue that the verification of coherence for t...

Unsupervised clustering of a set of datums into homogenous groups is a primitive operation required in many signal and image processing applications. In fact, different incarnations and hybrids of Fuzzy C–Means (FCM) and Possibilistic C–means (PCM) have been suggested which address additional requirements such as accepting weighted sets and being robust to the presence of outliers. Nevertheless...

The modeling of complex risk situations imposes the existence of multiple ways to represent the risk and compare the risk situations between them. In probabilistic models, risk is described by random variables and risk situations are compared by stochastic dominance. In possibilistic or credibilistic models, risk is represented by fuzzy variables. This paper concerns three indicators of dominan...

In this paper, both the uncertainty and the origin of pieces of information is handled in an extended possibilistic logic framework. Each formula is associated with a set (a fuzzy set more generally) which gathers labels of sources according to which the formula is (more or less) certainly true. In case of a single source of information, possibilistic logic is recovered. Soundness and completen...