Numerical experiments on the accuracy of rotation moments invariants

Rodtook, Annupan; Makhanov, Stanislav S.

doi:10.1016/j.imavis.2005.02.001

Cited by 10 publications

(7 citation statements)

References 16 publications

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“…We evaluate the performance of the proposed algorithm by two datasets. The first dataset consists of 14,000 gray level images based on ten Thai musical instruments [6], [13] (see Fig.4). Each instrument produces 950 training images and 450 testing images.…”

Section: Resultsmentioning

confidence: 99%

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Apriori Data Mining on Rotationally Invariant Multiresolutional Moments for Pattern Recognition

Rodtook¹,

Makhanov²

2006

Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)

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We propose a new feature selection procedure based on a combination of a pruning algorithm, Apriori mining techniques and fuzzy C-mean clustering. The feature selection algorithm is designed to mine on a multiresolution filter bank composed of rotationally invariant moments. The numerical experiments, with more than 10,000 images, demonstrate an accuracy increase of about 5% for a low noise, 15% for an average noise and 20% for a high-level noise.

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Section: Resultsmentioning

confidence: 99%

“…A popular class of the rotationally invariant features is based on the moment techniques [1]- [6] which are believed to be reliable for complex shapes because they involve not solely the contour pixels as it is the case for the shape descriptors but all the pixels constituting the object.…”

Section: Introductionmentioning

confidence: 99%

Apriori Data Mining on Rotationally Invariant Multiresolutional Moments for Pattern Recognition

Rodtook¹,

Makhanov²

2006

Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)

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“…Apart from the aforementioned computation errors, caused by the inherent weaknesses to apply the mathematical formulas to the set of image's pixels, the computation of image moments reveals some additional numerical instabilities [95,108,102,109,142,143,144,126].…”

Section: Numerical Stabilitymentioning

confidence: 99%

Over 50 Years of Image Moments and Moment Invariants

Papakostas¹

2014

Moments and Moment Invariants - Theory and Applications

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This chapter aims to analyze the research eld of moments and moment invariants in a holistic way. Initially, a literature analysis of the last 50 years is presented and discussed in order to highlight the potential of this topic and the increasing interest in many disciplines. A more in depth study of the issues addressed through the years by the researchers is next presented both in theory and applications of moments. The most representative works in each research direction are discussed in a chronological order to point out the progress in each specic eld of action. This analysis concludes with the challenges and perspectives that should motivate researchers towards the promotion of the moments and their invariants to new scientic horizons. For the rst time, this chapter gives a global overview of what happened in the last 50 years in moments and moment invariants research eld, but most of all it brings to light the open issues that should be addressed and highlights the rising topics that will occupy the scientists in the coming years. This chapter serves as a guide to those who nd the eld of moments and moment invariants a brilliant eld of action since it encloses all the milestones of this eld.

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“…The performance of pattern recognition systems depends on the specific feature extraction technique used to represent a pattern. A popular class of invariant features is based on the moment techniques including geometric moment, complex moments and orthogonal moments [1][2][3][4][5][6][7][8][9]. Among them, moments with orthogonal basis functions can represent the image by a set of mutually independent descriptors and thus have a minimal amount of information redundancy.…”

Section: Introductionmentioning

confidence: 99%

Construction of a complete set of orthogonal Fourier–Mellin moment invariants for pattern recognition applications

Zhang

Haigron

et al. 2010

Image and Vision Computing

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a b s t r a c tThe completeness property of a set of invariant descriptors is of fundamental importance from the theoretical as well as the practical points of view. In this paper, we propose a general approach to construct a complete set of orthogonal Fourier-Mellin moment (OFMM) invariants. By establishing a relationship between the OFMMs of the original image and those of the image having the same shape but distinct orientation and scale, a complete set of scale and rotation invariants is derived. The efficiency and the robustness to noise of the method for recognition tasks are shown by comparing it with some existing methods on several data sets.

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Numerical experiments on the accuracy of rotation moments invariants

Cited by 10 publications

References 16 publications

Apriori Data Mining on Rotationally Invariant Multiresolutional Moments for Pattern Recognition

Apriori Data Mining on Rotationally Invariant Multiresolutional Moments for Pattern Recognition

Over 50 Years of Image Moments and Moment Invariants

Construction of a complete set of orthogonal Fourier–Mellin moment invariants for pattern recognition applications

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