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Morphological autocorrelation transform: A new representation and classification scheme for two-dimensional images

机译:形态自相关变换:二维图像的新表示和分类方案

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A methodology based on mathematical morphology is proposed for efficient recognition of two-dimensional (2D) objects or shapes. It is based on the introduction a shape descriptor called the morphological autocorrelation transform (MAT). The MAT of an image is composed of a family of geometrical correlation functions (GCFs) which define its morphological covariance in a specific direction. The MAT is translation-, scale-, and rotation-invariant. It is shown that in most situations, a small subset of the MAT suffices for image representation. The characteristics and performance of a shape recognition system based on the MAT are investigated and analyzed. Computational complexity of the proposed morphological-based recognition system is examined. It is shown that shape properties, such as area, perimeter, and orientation, are readily derived from the MAT representation, and that the proposed system is well suited for shape representation and classification.
机译:提出了一种基于数学形态学的方法,可有效识别二维(2D)对象或形状。它基于介绍的形状描述符,称为形态自相关变换(MAT)。图像的MAT由一系列几何相关函数(GCF)组成,这些函数定义了特定方向上的形态协方差。 MAT是平移,缩放和旋转不变的。结果表明,在大多数情况下,一小部分MAT就足以表示图像。研究并分析了基于MAT的形状识别系统的特性和性能。研究了所提出的基于形态学的识别系统的计算复杂性。结果表明,形状特性(例如面积,周长和方向)很容易从MAT表示中得出,并且所提出的系统非常适合形状表示和分类。

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