Integration Of Contextual Information Research Articles

SENSOR FUSION INTEGRATING CONTEXTUAL INFORMATION

The application of multi-sensor fusion, which aims at recogizing a state among a set of hypotheses for object classification, is of major interest as regards the performance improvement brought by the sensor complementarily. Nevertheless this needs to take into account the more accurate as possible information and take advantage of the statistical learning of the previous measurements acquired by sensors. The classical probabilistic fusion methods lack of performance when the previous learning is not representative of the real measurements provided by sensors. The theory of evidence is then introduced to face this disadvantage by integrating a further information which is the context of the sensor acquisitions. In this paper, we propose a formalism of modeling of the sensor reliability to the context that leads to two methods of integration when all the hypotheses, associated to the objects of the scene acquired by sensors, are previously learnt: the first one amounts to integrate this further information in the fusion rule as degrees of trust and the second models the sensor reliability directly as mass functions. These two methods are based on the theory of fuzzy events. Afterwards, we are interested in the evolvement of these two methods in the case where the previous learning is unavailable for a hypothesis associated to an object of the scene and compare these two methods in order to deduce a global method of contextual information integration in the fusion process.

Presentation and description of two classification methods using data fusion based on sensor management

The application of multi-sensor fusion, which aims at recognizing a state among a set of hypotheses for object classification, is of major interest with regard to the performance improvement brought by the sensor complementarity. Nevertheless, this needs to take into account the most accurate information and take advantage of the statistical learning of the previous measurements acquired by sensors. When previous learning is not representative of real measurements provided by the sensors, the classical probabilistic fusion methods lack performance. The Dempster–Shafer theory is then introduced to face this disadvantage by integrating further information which is the context of the sensor acquisitions. In this paper, we propose a model formalism for the sensor reliability in a context that leads to two methods of integration when all the hypotheses, associated to the objects of the scene acquired by sensors, are previously learned: the first one models the integration of this further information in the fusion rule as degrees of trust and the second models the sensor reliability directly as probability mass. These two methods are based on the theory of fuzzy events. Simulations of typical cases are developed in order to define the respective validity domains of these two methods. Afterwards, we are interested in the development of these two methods in the case where the previous learning is unavailable for an object and a global method of contextual information integration can be deduced.

Achieving incremental semantic interpretation through contextual representation

While much work has been done investigating the role of context in the incremental processing of syntactic indeterminacies, relatively little is known about online semantic interpretation. The experiments in this article made use of the eye-tracking paradigm with spoken language and visual contexts in order to examine how, and when listeners make use of contextually-defined contrast in interpreting simple prenominal adjectives. Experiment 1 focused on intersective adjectives. Experiment 1A provided further evidence that intersective adjectives are processed incrementally. Experiment 1B compared response times to follow instructions such as ` Pick up the blue comb' under conditions where there were two blue objects (e.g. a blue pen and a blue comb), but only one of these objects had a contrasting member in the display. Responses were faster to objects with a contrasting member, establishing that the listeners initially assume a contrastive interpretation for intersective adjectives. Experiments 2 and 3 focused on vague scalar adjectives examining the time course with which listeners establish contrast for scalar adjectives such as tall using information provided by the head noun (e.g. glass) and information provided by the visual context. Use of head-based information was examined by manipulating the typicality of the target object (e.g. whether it was a good or poor example of a tall glass. Use of context-dependent contrast was examined by either having only a single glass in the display (the no contrast condition) or a contrasting object (e.g. a smaller glass). The pattern of results indicated that listeners interpreted the scalar adjective incrementally taking into account context-specific contrast prior to encountering the head. Moreover, the presence of a contrasting object, sharply reduced, and in some conditions completely eliminated, typicality effects. The results suggest a language processing system in which semantic interpretation, as well as syntactic processing, is conducted incrementally, with early integration of contextual information.

A simulation of final stop consonants in speech perception using the bicameral neural network model

This paper demonstrates the integration of contextual information in a neural network for speech perception. Neural networks have been unable to integrate such information successfully because they cannot implement conditional rule structures. The Bicameral neural network employs an asynchronous controller which allows conditional rules to choose neurons for update rather than updating them randomly. The Bicameral model is applied to the perception of word-final plosives, an ongoing problem for machine recognition of speech.