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Translation and scale invariants of Tchebichef moments

机译:切比切夫矩的平移和尺度不变量

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Discrete orthogonal moments such as Tchebichef moments have been successfully used in the field of image analysis. However, the invariance property of these moments has not been studied mainly due to the complexity of the problem. Conventionally, the translation and scale invariant functions of Tchebichef moments can be obtained either by normalizing the image or by expressing them as a linear combination of the corresponding invariants of geometric moments. In this paper, we present a new approach that is directly based on Tchebichef polynomials to derive the translation and scale invariants of Tchebichef moments. Both derived invariants are unchanged under image translation and scale transformation. The performance of the proposed descriptors is evaluated using a set of binary characters. Examples of using the Tchebichef moments invariants as pattern features for pattern classification are also provided. (c) 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
机译:离散正交矩(例如Tchebichef矩)已成功地用于图像分析领域。但是,由于问题的复杂性,尚未研究这些矩的不变性。通常,可以通过对图像进行归一化或将其表示为几何矩的相应不变量的线性组合来获得Tchebichef矩的平移和尺度不变函数。在本文中,我们提出了一种直接基于Tchebichef多项式的新方法来推导Tchebichef矩的平移和尺度不变性。在图像转换和比例转换下,两个派生的不变式均不变。建议的描述符的性能使用一组二进制字符进行评估。还提供了使用Tchebichef矩不变量作为模式特征进行模式分类的示例。 (c)2007模式识别学会。由Elsevier Ltd.出版。保留所有权利。

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