Analysis and segmentation of higher dimensional data sets with fuzzy operators for representation and visualization

Wolfgang F. Kraske

doi:10.1117/12.2283794

28 June 1994 Analysis and segmentation of higher dimensional data sets with fuzzy operators for representation and visualization

Wolfgang F. Kraske

Author Affiliations +

Proceedings Volume 10312, Neural and Fuzzy Systems: The Emerging Science of Intelligent Computing; 103120B (1994) https://doi.org/10.1117/12.2283794
Event: SPIE Institutes for Advanced Optical Technologies 12, 1994, Bellingham, WA, United States

Abstract

The segmentation and representation of complex features in higher dimensional data sets is of paramount importance for machine recognition and human perception of image informa- tion. A multiresolution and multiÃ¢â‚¬â€aspect data representation paradigm, a morphological skeleton, is used in this paper to provide a hierarchical framework for efficient representa- tion and visualization of data for machine recognition and human perception of data features. The utilization of fuzzy operators establishes a basis within this framework for organizing packets, or fuzzy sets, of approximate information by minimum coverings or maximal sub- tense. 3Ã¢â‚¬â€D and 2Ã¢â‚¬â€D image data are used to demonstrate applications of these techniques on higher dimensional data sets. Grayscale mathematical morphology provides an established basis and an algebra for fuzzy operators due to its representation of fuzzy maps over a set support. Specifically, the applica- tion of morphological operators in scale and orientation paradigms with tractable support shapes provides an ordered basis for topological analysis and user perception of data. To eliminate precision loss grayscale morphology utilizes only set operations requiring only computer addition and comparison.

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Wolfgang F. Kraske "Analysis and segmentation of higher dimensional data sets with fuzzy operators for representation and visualization", Proc. SPIE 10312, Neural and Fuzzy Systems: The Emerging Science of Intelligent Computing, 103120B (28 June 1994); https://doi.org/10.1117/12.2283794

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