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Statistical Pattern Recognition Using the Normalized Complex Moment Components Vector

机译:使用归一化复矩成分向量的统计模式识别

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摘要

This paper presents a new feature vector for statistical pattern recognition based on the theory of moments, namely the Normalized Complex Moment Components (NCMC). The NCMC will be evaluated in the recognition of objects which share identical silhouettes using grayscale images and its performance will be compared with that of a commonly used moment based feature vector, the Hu moment invariants. The tolerance of the NCMC to random noise and the effect of using different orders of moments in its calculation will also be investigated.
机译:本文基于矩理论提出了一种新的统计模式识别特征向量,即归一化复数矩分量(NCMC)。将使用灰度图像在共享相同轮廓的对象识别中评估NCMC,并将其性能与常用的基于矩的特征向量Hu矩不变性进行比较。还将研究NCMC对随机噪声的容忍度以及在计算中使用不同阶次矩的影响。

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