Translation and scale invariants of Tchebichef moments

Zhu, Hongqing; Hu, Shu; Xia, Ting; Luo, Limin; Coatrieux, Jean Louis

doi:10.1016/j.patcog.2006.12.003

Cited by 88 publications

(43 citation statements)

References 22 publications

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“…In this context, radial Tchebichef moments were proposed to obtain rotation invariant descriptors from images [42]. Additionally, translation and scale invariants from DTM have been defined [43].…”

Section: Discussionmentioning

confidence: 99%

Texture classification using discrete Tchebichef moments

Marcos¹,

Cristóbal²

2013

J. Opt. Soc. Am. A

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In this paper, a method to characterize texture images based on discrete Tchebichef moments is presented. A global signature vector is derived from the moment matrix by taking into account both the magnitudes of the moments and their order. The performance of our method in several texture classification problems was compared with that achieved through other standard approaches. These include Haralick's gray-level co-occurrence matrices, Gabor filters, and local binary patterns. An extensive texture classification study was carried out by selecting images with different contents from the Brodatz, Outex, and VisTex databases. The results show that the proposed method is able to capture the essential information about texture, showing comparable or even higher performance than conventional procedures. Thus, it can be considered as an effective and competitive technique for texture characterization.

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Section: Discussionmentioning

confidence: 99%

Texture classification using discrete Tchebichef moments

Marcos¹,

Cristóbal²

2013

J. Opt. Soc. Am. A

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“…A similar approach was then used to construct both translation and scale invariants of Legendre moments [4]. The problem of scale and translation invariants of Tchebichef moments has been investigated by Zhu et al [43]. Discrete orthogonal moments such as Tchebichef moments yield better performance than the continuous orthogonal moments, but the rotation invariants are dicult to derive.…”

Section: Derivation Of Moment Invariants To Geometric Transformationsmentioning

confidence: 99%

“…To overcome this shortcoming, Mukundan [19] introduced the radial Tchebichef moments, which are dened in polar coordinate system, to achieve the rotation invariance. It was shown that the methods reported in [3,4,43,19] perform better than the classical approaches such as image normalization and indirect methods. However, it seems dicult to obtain the completeness property by the above mentioned methods since no explicit formulation is derived for moment invariants.…”

Section: Derivation Of Moment Invariants To Geometric Transformationsmentioning

confidence: 99%

Derivation of Moment Invariants

Hu¹,

Luo²,

Coatrieux³

2014

Gate to Computer Science and Research

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In most computer vision applications, the extraction of key image features, whatever the transformations applied and the image degradations observed, is of major importance. A large diversity of approaches has been reported in the literature. This chapter concentrates on one particular processing frame: the moment-based methods. It provides a survey of methods proposed so far for the derivation of moment invariants to geometric transforms and blurring eects. Theoretical formulations and some selected examples dealing with very dierent problems are given.

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“…Mainly, two methodologies used to ensure invariance under common geometric transformations such as rotation, scaling and translation, either by image coordinates normalization and description through the geometric moment invariants (Mukundan & Ramakrishnan, 1998;Zhu et al, 2007) or by developing new computation formulas which incorporate these useful properties inherently (Zhu et al, 2007).…”

Section: Invariant Descriptionmentioning

confidence: 99%