Discovery of Patterns in Associated Sets: Foundations and Applications

拟议的研究引入了一个计算框架来测量视觉信息颗粒之间的相似性。利用相关的集合和特征向量的接近距离泛函，可以设计支持近似推理的基于感知的视觉系统（特征向量提供集合成员的描述）。这种方法导致信息颗粒可视化，因为这样的集合有利于描述-以及在人工视觉系统的设计中基于位置的事件检测以及广泛的技术转让。建议的研究的科学方法是发展的基础和应用程序的关联集识别的不相交的视觉对象集的成员之间的描述的亲和力。所提出的接近度量化方法是通过一个相关联的集合框架来实现的，该框架使用各种距离函数来识别相对于集合之间的距离具有相似性（或不相似性）的非空集合。换句话说，我们正在寻找问题的答案：一对集合足够近（足够远），可以被认为相似（或不相似）吗？

The proposed research introduces a computational framework for measuring resemblance between visual information granules. The utilization of associated sets and nearness distance functionals of feature vectors make it possible to design perception-based vision systems that support approximate reasoning (feature vectors provide descriptions of set members). This approach leads to information granule visualisation because such sets facilitate description- as well as location-based event detection in the design of artificial vision systems as well as broad-based technology transfer. The scientific approach of the proposed research is to develop the foundations and applications of associated sets in discerning affinities between descriptions of members of disjoint sets of visual objects. The proposed nearness quantification approach is accomplished via an associated set framework in discerning non-empty sets that are descriptively similar (or dissimilar) relative to the distances between the sets using various distance functions. In other words, we are seeking answers to the question: Is a pair of sets sufficiently near (sufficiently far apart) to be considered similar (or dissimilar)?