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Titlebook: Applying Soft Computing in Defining Spatial Relations; Pascal Matsakis,Les M. Sztandera Book 2002 Springer-Verlag Berlin Heidelberg 2002 C

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發(fā)表于 2025-3-21 17:08:45 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Applying Soft Computing in Defining Spatial Relations
影響因子2023Pascal Matsakis,Les M. Sztandera
視頻videohttp://file.papertrans.cn/161/160268/160268.mp4
發(fā)行地址Includes supplementary material:
學(xué)科分類Studies in Fuzziness and Soft Computing
圖書封面Titlebook: Applying Soft Computing in Defining Spatial Relations;  Pascal Matsakis,Les M. Sztandera Book 2002 Springer-Verlag Berlin Heidelberg 2002 C
影響因子Geometric properties and relations play central roles in the description and processing of spatial data. The properties and relations studied by mathematicians usually have precise definitions, but verbal descriptions often involve imprecisely defined concepts such as elongatedness or proximity. The methods used in soft computing provide a framework for formulating and manipulating such concepts. This volume contains eight papers on the soft definition and manipulation of spatial relations and gives a comprehensive summary on the subject.
Pindex Book 2002
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https://doi.org/10.1007/978-3-322-88448-0s to project the fuzzy subsets onto two orthogonal coordinate axes and to utilize fuzzy set theoretic dominance relations to capture the approximate relationships. Simulation results are provided to corroborate the theory.
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Understanding the Spatial Organization of Image Regions by Means of Force Histograms: A Guided Tourd by fuzzy relations. They can also be used for scene description, where relative positions are represented by linguistic expressions. This chapter reviews and classifies work on the histogram of forces. It touches topics as varied as human-robot communication and spatial indexing mechanisms for medical image databases.
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Path Composition of Positional Relations Integrating Qualitative and Fuzzy Knowledge,ional relations, that is, the composition of two relations, if iterated over a path of several intermediate positions, introduces too much indeterminacy in the result. If the algorithms for composition take into account fuzzy knowledge, the latter effect is considerably reduced, obtaining an indication of the degree of likelihood of the result.
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