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The Topological Detection Algorithm of Object Arrays in Noisy Context Based on Fuzzy Spatial Information Fusion and Prim Algorithm

机译:基于模糊空间信息融合和Prim算法的噪声环境下目标阵列拓扑检测算法

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Most computer vision methods deal with the single object recognition problem. If an object is so small that little features can be used to support recognition procedure, the relationship between multi-objects could be helpful. In many cases, small objects are likely to be arranged by some regular shapes. To recognize these arrays, the paper presents a spatial topology detection algorithm. We call it as S-Prim (Spatial Prim) algorithm which is based on classic Prim algorithm, and integrates the fuzzy spatial information. The algorithm evaluates the spatial distribution regularity among neighboring nodes by back searching the path in the found tree when it is growing, and controls its growing direction according to some fuzzy rules to find out the most likely regular spatial topology. The detected tree can be considered as a spanning tree constrained by topological structures.
机译:大多数计算机视觉方法处理单个对象识别问题。如果一个对象太小,几乎没有功能可用于支持识别过程,则多对象之间的关系可能会有所帮助。在许多情况下,小物体可能会以某些规则的形状排列。为了识别这些阵列,本文提出了一种空间拓扑检测算法。我们称其为S-Prim(Spatial Prim)算法,它基于经典的Prim算法,并集成了模糊的空间信息。该算法通过在树中生长时回溯搜索路径来评估相邻节点之间的空间分布规律性,并根据一些模糊规则控制其生长方向以找出最可能的规则空间拓扑。可以将检测到的树视为受拓扑结构约束的生成树。

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