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In this paper, a novel possibilistic approach for representing pixelic spatial knowledge is proposed to be used in classification; more specifically in segmentation of low quality images. This approach uses the spatial contextual information at the pixel level in order to produce a local possibility distribution. The similarity between this local possibility distribution representing the contextual...
An iris recognition requires parametric modeling texture. The extracted model should characterize the individual corresponding to considered iris. Such a model is often referred to as biometric signature. Several approaches to uniquely specify an iris by extracting parameters characteristic of its texture exist in the literature. An original approach based on an analysis by the Meyer wavelet of the...
In this paper a novel possibilistic knowledge modeling at the pixel level, is proposed. This model consists on the use of the spatial contextual information at the level of each pixel, in order to evaluate a local based possibility distribution, resuming the pixel information. The proposed possibilistic modeling approach performance is evaluated through a pixel classification of both synthetic image...
In data processing, merging of data requires definition of conflict degree between source information in order to select the appropriate merging operator. The diversity of merging operators and the variability of information precision and source reliability generate great difficulty in the selection of the appropriate merging operator. Possibility theory and Dempster Shafer (DS) theory are two main...
The process of staircases detection and recognition is complex for blinds. Therefore, an intelligent and real time system is required to help them. In this paper, we investigate using only one ultrasonic sensor and few samples with small size to represent floor and staircases. The performance of such system depend on object representation, data modeling and finally classification algorithm. A simple...
In this work, we propose an object recognition strategy in a domestic environment. Our contribution is to use low-level features extracted from images with high-level concepts generated from an ontology of domestic objects to get richer decision. It consists in developing a semantic classification by providing for a white cane user the class of the obstacle and the scene in which it is located. The...
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