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Choquet integral-based morphological operators with applications to object detection and information fusion.

机译：基于Choquet积分的形态学算子及其在对象检测和信息融合中的应用。

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摘要

In this research, we defined Choquet integral based-morphological operators. These operators generalize both gray-scale morphological operations and Choquet integrals. Based on these operators, we developed a novel methodology to select important information sources. We applied the methodology to information fusion and image feature extraction. These operators were also used to improve the speed of MSNN.; We showed how the Choquet integral can be interpreted in terms of mathematical morphology in the case that the fuzzy measure is defined on subsets of the two-dimensional plane. We showed that by interpreting the domain of the fuzzy measure as the shape of interest, the Choquet integral can be naturally viewed as computing the degree to which an input shape fits into the shape of interest, and therefore naturally defines fuzzy morphological operations.; We, then, formally defined a generalization of gray-scale morphological operations that are based on Choquet integral filters. This definition covers both binary and gray-scale morphology with flat and gray-scale structuring elements.; We developed a methodology to select (reject) important (unimportant) information sources. Some of the applications include feature selection and domain shape learning.; We generalized the MSNN using Choquet integral based morphological operators. We investigated the applicability of the CMSNN to a land mine detection problem. We compared the performance to the MSNN. The experiments showed that CMSNN has better performance than MSNN. We also showed that domain reduction (domain learning) may increase detection performance.
机译：在这项研究中，我们定义了基于Choquet积分的形态学算子。这些算子对灰度形态学运算和Choquet积分都进行了概括。基于这些运算符，我们开发了一种新颖的方法来选择重要的信息源。我们将该方法应用于信息融合和图像特征提取。这些运算符还用于提高MSNN的速度。我们展示了在模糊量度定义在二维平面子集上的情况下，如何用数学形态学来解释Choquet积分。我们表明，通过将模糊量度的域解释为感兴趣的形状，可以将Choquet积分自然地视为计算输入形状适合感兴趣的形状的程度，因此自然地定义了模糊形态学运算。然后，我们正式定义了基于Choquet积分滤波器的灰度形态运算的一般化。该定义涵盖具有平面和灰度结构元素的二进制和灰度形态。我们开发了一种方法来选择（拒绝）重要的（不重要的）信息源。一些应用包括特征选择和域形状学习。我们使用基于Choquet积分的形态学算子来概括MSNN。我们调查了CMSNN在地雷探测问题上的适用性。我们将性能与MSNN进行了比较。实验表明，CMSNN的性能优于MSNN。我们还表明，域缩减（域学习）可以提高检测性能。

著录项

  • 作者

    Hocaoglu, Ali Koksal.;

  • 作者单位

    University of Missouri - Columbia.;

  • 授予单位 University of Missouri - Columbia.;
  • 学科 Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2000
  • 页码 144 p.
  • 总页数 144
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 无线电电子学、电信技术;
  • 关键词

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