A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
When upper probabilities are possibility measures
Fuzzy Sets and Systems - Special issue dedicated to Professor Claude Ponsard
Artificial Intelligence
Fuzzy sets as a basis for a theory of possibility
Fuzzy Sets and Systems
Supremum preserving upper probabilities
Information Sciences: an International Journal
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Uncertainty-Based Information: Elements of Generalized Information Theory
Uncertainty-Based Information: Elements of Generalized Information Theory
Digital Signal Filtering, Analysis and Restoration (Telecommunications Series)
Digital Signal Filtering, Analysis and Restoration (Telecommunications Series)
Comparing probability measures using possibility theory: A notion of relative peakedness
International Journal of Approximate Reasoning
Granulation of a fuzzy set: Nonspecificity
Information Sciences: an International Journal
Possibility theory and statistical reasoning
Computational Statistics & Data Analysis
Uncertainty of discrete stochastic systems: general theory and statistical inference
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Fuzzy Sets and Systems
Imprecise expectations for imprecise linear filtering
International Journal of Approximate Reasoning
Using cloudy kernels for imprecise linear filtering
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Possibilistic signal processing: How to handle noise?
International Journal of Approximate Reasoning
Use of the domination property for interval valued digital signal processing
SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
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In this paper, we propose granularity as a new index to characterize the non-specificity of a summative kernel. This index is intended to reflect the behavior of a kernel in the usual signal processing applications. We show, in different experiments, that two kernels having the same granularity have very similar behavior. This granularity-based adaptation is compared to other adaptation methods. These experiments highlight the ability of the granularity index to measure the spreading and collecting properties of a summative kernel.