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首页> 外文会议>2018 International Conference on Intelligent Systems and Computer Vision >Efficient image classification by using improved dual Hahn Moment Invariants
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Efficient image classification by using improved dual Hahn Moment Invariants

机译：通过使用改进的双重Hahn矩不变量进行有效的图像分类

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摘要

The discrete orthogonal moments and moment invariants are powerful descriptors for image analysis and computer vision. However, until now obtaining moment invariants had always needed much computation time and numerical accuracy, which has not been resolved well. Therefore, the main purpose of this paper is to introduce an efficient set of discrete orthogonal moment invariants, named Improved dual Hahn Moment Invariants (IDHMI). The proposed IDHMI are based on a recursive methods for the computation of dual-Hahn polynomials coefficients. These recursive methods permits the fast and accurate computation of the dual Hahn Moment Invariants. In fact, this new set can be used to extract invariant shape features regardless the change of shape's orientation, size and position. Consequently, a series of numerical experiment are performed in order to evaluate the performance of the proposed moment invariants, with regard to the numerical stability, computational time and recognition accuracy. The theoretical and experimental results clearly show the applicability and the efficiency of the proposed method.
机译：离散的正交矩和矩不变性是图像分析和计算机视觉的强大描述符。但是，直到现在，获取矩不变性一直需要大量的计算时间和数值精度，但尚未很好地解决。因此，本文的主要目的是介绍一种有效的离散正交矩不变式集，称为改进对偶哈恩矩不变式（IDHMI）。所提出的IDHMI基于递归方法，用于计算双Hahn多项式系数。这些递归方法可以快速，准确地计算双Hahn Moment不变量。实际上，无论形状的方向，大小和位置如何变化，该新集合都可用于提取不变的形状特征。因此，进行了一系列数值实验，以评估所提出的不变矩在数值稳定性，计算时间和识别精度方面的性能。理论和实验结果清楚地表明了该方法的适用性和有效性。

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