Name
Title | ||
|---|---|---|
A novel fuzzy evidential reasoning paradigm for data fusion with applications in image processing |
Abstract | ||
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This paper presents a novel data fusion paradigm based on fuzzy evidential reasoning. A new fuzzy evidence structure model is first introduced to formulate probabilistic evidence and fuzzy evidence in a unified framework. A generalized Dempster’s rule is then utilized to combine fuzzy evidence structures associated with multiple information sources. Finally, an effective decision rule is developed to take into account uncertainty, quantified by Shannon entropy and fuzzy entropy, of probabilistic evidence and fuzzy evidence, to deal with conflict and to achieve robust decisions. To demonstrate the effectiveness of the proposed paradigm, we apply it to classifying synthetic images and segmenting multi-modality human brain MR images. It is concluded that the proposed paradigm outperforms both the traditional Dempster–Shafer evidence theory based approach and the fuzzy reasoning based approach |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/s00500-005-0039-1 | Soft Comput. |
Keywords | Field | DocType |
fuzzy reasoning,fuzzy evidence structure,new fuzzy evidence structure,data fusion · dempster-shafer evidence theory · fuzzy evidential reasoning · image classification and segmentation,novel fuzzy evidential reasoning,fuzzy entropy,image processing,novel data fusion paradigm,fuzzy evidential reasoning,shafer evidence theory,proposed paradigm,probabilistic evidence,fuzzy evidence,decision rule,data fusion,evidential reasoning,image classification,shannon entropy,dempster shafer | Decision rule,Neuro-fuzzy,Fuzzy classification,Fuzzy set operations,Computer science,Fuzzy logic,Artificial intelligence,Probabilistic logic,Evidential reasoning approach,Fuzzy associative matrix,Machine learning | Journal |
Volume | Issue | ISSN |
10 | 12 | 1433-7479 |
Citations | PageRank | References |
9 | 0.71 | 19 |
Authors | ||
2 | ||