A comparative analysis of algorithms for fast computation of Zernike moments

Chong, Chee-Way; Raveendran, P.; Mukundan, Ramakrishnan

doi:10.1016/s0031-3203(02)00091-2

Cited by 246 publications

(133 citation statements)

References 11 publications

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“…Images are firstly pre-processed and transformed by the discrete double density wavelet transform (3DWT). The following features are extracted: Texture descriptors [13], -Statistical moments [14], Tamura parameters [15,16], Radon's characteristics [17,18] and Zernike's moments [19] …”

Section: Features Extractionmentioning

confidence: 99%

Validation Study of Dimensionality Reduction Impact on Breast Cancer Classification

Hamdi¹,

Auhmani²,

Hassani³

2015

IJCSIT

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A fundamental problem in machine learning is identifying the most representative subset of features from which we can construct a predictive model for a classification task. This paper aims to present a validation study of dimensionality reduction effect on the classification accuracy of mammographic images. The studied dimensionality reduction methods were: locality-preserving projection (LPP), locally linear embedding (LLE), Isometric Mapping (ISOMAP) and spectral regression (SR). We have achieved high rates of classifications. In some combinations the classification rate was 100%. But in most of the cases the classification rate is about 95%. It was also found that the classification rate increases with the size of the reduced space and the optimal value of space dimension is 60. We proceeded to validate the obtained results by measuring some validation indices such as: Xie-Beni index, Dun index and Alternative Dunindex. The measurement of these indices confirms that the optimal value of reduced space dimension is d=60.

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Section: Features Extractionmentioning

confidence: 99%

Validation Study of Dimensionality Reduction Impact on Breast Cancer Classification

Hamdi¹,

Auhmani²,

Hassani³

2015

IJCSIT

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“…For example the most commonly used ones are the Zernike [4], Tchebichef [17] or power moments. Additionally, in [18] a method of custom built moments construction has been proposed.…”

Section: Momentsmentioning

confidence: 99%

Moments-Based Fast Wedgelet Transform

Lisowska

2010

J Math Imaging Vis

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In the paper the moments-based fast wedgelet transform has been presented. In order to perform the classical wedgelet transform one searches the whole wedgelets' dictionary to find the best matching. Whereas in the proposed method the parameters of wedgelet are computed directly from an image basing on moments computation. Such parameters describe wedgelet reflecting the edge present in the image. However, such wedgelet is not necessarily the best one in the meaning of Mean Square Error. So, to overcome that drawback, the method which improves the matching result has also been proposed. It works in the way that the better matching one needs to obtain the longer time it takes. The proposed transform works in linear time with respect to the number of pixels of the full quadtree decomposition of an image. More precisely, for an image of size N × N pixels the time complexity of the proposed wedgelet transform is O(N 2 log 2 N).

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“…Common methods are based on moments [28,2,13] including geometric, Legendre, Zernike, and Pseudo-Zernike moments. Comparative studies [2,28] have demonstrated the interest on improving invariance properties and reducing computational time of the Zernike moments [8]. On the other side, to overcome the drawbacks of contour-based Fourier descriptors, Zhang and Lu [30] have proposed a region-based Generic Fourier Descriptor (GFD).…”

Section: Introductionmentioning

confidence: 99%

DTW for Matching Radon Features: A Pattern Recognition and Retrieval Method

Santosh

Wendling

2011

Advanced Concepts for Intelligent Vision Systems

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The original publication is available at www.springerlink.comInternational audienceIn this paper, we present a method for pattern such as graphical symbol and shape recognition and retrieval. It is basically based on dynamic programming for matching the Radon features. The key characteristic of the method is to use DTW algorithm to match corresponding pairs of histograms at every projecting angle. This allows to exploit the Radon property to include both boundary as internal structure of shapes, while avoiding compressing pattern representation into a single vector and thus miss information, thanks to the DTW. Experimental results show that the method is robust to distortion and degradation including affine transformations

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A comparative analysis of algorithms for fast computation of Zernike moments

Cited by 246 publications

References 11 publications

Validation Study of Dimensionality Reduction Impact on Breast Cancer Classification

Validation Study of Dimensionality Reduction Impact on Breast Cancer Classification

Moments-Based Fast Wedgelet Transform

DTW for Matching Radon Features: A Pattern Recognition and Retrieval Method

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