基于模糊逻辑的数据集成:理论与应用

M. Abdulghafour, A. Fellah, M. Abidi
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引用次数: 13

摘要

多传感器系统提供了单个传感器无法提供的有目的的环境描述。由于传感器提供的观测是不确定和不完整的,我们采用模糊集理论作为综合不确定测量的一般框架。我们建立了一个基于模糊度量的融合公式。根据聚变算子的几个理想性质,对聚变公式进行了数学测试。我们建立了一种模糊化方案,通过该方案可以对不同类型的输入数据进行建模。提出了一种去模糊化方案,从组合模糊评价中恢复出清晰的数据。该方法在Odetics激光距离扫描仪获取的真实距离和强度图像上进行了实现和测试。目标是通过对两幅图像的分割过程来获得更好的场景描述。提出了一种评价分割结果的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fuzzy logic-based data integration: theory and applications
Multisensor systems provide a purposeful description of the environment that a single sensor cannot offer. Because observations provided by sensors are uncertain and incomplete, we adopt the use of fuzzy sets theory as a general framework to combine uncertain measurements. We develop a fusion formula based on the measure of fuzziness. The fusion formula is mathematically tested against several desirable properties of fusion operators. We establish a fuzzification scheme by which different types of input data would be modeled. A defuzzification scheme is carried out to recover crisp data from the combined fuzzy assessment. This approach is implemented and tested with real range and intensity images acquired by an Odetics Laser Range Scanner. The goal is to obtain better scene descriptions through a segmentation process of both images. A method for evaluating segmentation results is presented.<>
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