Moment matrices for recognition of spatial pattern in noisy images

Hero, Alfred O.; O'Neill, J.C.; Williams, William J.

doi:10.1109/icip.1997.638783

Cited by 7 publications

(9 citation statements)

References 7 publications

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“…Hu"s seven moment invariants have been widely used in pattern recognition, and their performance has been evaluated under various deformation situation including blurring [10], spatial degradations [3], random noise [1], skew and perspective transformations [7]. As Hu"s seven moment invariants take every image pixel into account the computation cost will be much higher than boundary-based invariants.…”

Section: Related Development In This Areamentioning

confidence: 99%

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Reconstruction of Oriya Alphabets Using Zernike Moments

Tripathy¹

2010

IJCA

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There has been a significant amount of research in pattern recognition in different aspects of printed character based user interfaces including interactive design tools, ink beautification and printed character recognition. Optical Character recognition (OCR) systems have been effectively developed for the recognition of printed characters of non-Indian languages. Efforts are on the way for the development of efficient OCR system for Indian language especially for ORIYA. I present in this paper the reconstruction of the basic characters (vowels and consonants) in Oriya text, which can handle different font sizes and font types. Hu"s seven moments and Zernike moments have been progressively used to extract the features of ORIYA characters. When I reconstruct by taking the extracted features, due to certain difficulties in the Hu"s moments I can use the Zernike moments. The methodology can be extended for recognition of the ORIYA conjuncts too.

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Section: Related Development In This Areamentioning

confidence: 99%

“…Original Image Unit circle with fixed Computational range radius of 32 pixels (3) Fruits Zernike(1934) introduced a set of complex polynomials {Vnm( x, y )} which form a complete orthogonal set over the unit disk of x2+y2≤1 in polar coordinates [1]. The form of the polynomials is:…”

Section: Zernike Momentsmentioning

confidence: 99%

Reconstruction of Oriya Alphabets Using Zernike Moments

Tripathy¹

2010

IJCA

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“…the citations in [4]. Recognition should rely on a method which is able to capture the essential features of a word while being invariant to deformations such as font, position and scale.…”

Section: Introductionmentioning

confidence: 99%

“…When word scale and position are known noise subspace processing of the matrix of spatial moments is a very effective way to obtain more reliable moment descriptions [4] which are robust to noise degradation. This is because the effect of binary bit-flip noise is approximately additive in the moment domain.…”

Section: Introductionmentioning

confidence: 99%

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Noisy word recognition using denoising and moment matrix discriminants

Nikolova

Hero²

Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics. SPW-HOS '99

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We consider the problem of recognition of a printed word belonging to a limited dictionary. The main difficulty comes from the fact that this word can be printed using different fonts, sizes, and positions on the page. Invariant moment methods for word recognition developed by Hu [5] and Li [6] are unreliable when the quality of the word image is degraded by noise. In this paper we investigate the effectivenes of simple median filter denoising for preprocessing noise degraded images prior to moment based classification using the moment matrix discriminants introduced by Hero etal [4].

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Digital modulation classification using power moment matrices

Hero

Hadinejad-Mahram²

Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181

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With the rising number of modulation types used in multi-user and multi-service digital communication systems, the need to find efficient methods for their discrimination in the presence of noise has become increasingly important. Here, we present a new approach based on a recently developed pattern recognition method previously applied to word spotting problems in binary images [ 11. In this approach, a large number of spatial moments are arranged in a symmetric positive definite matrix for which eigendecomposition and noise subspace processing methods can be applied. The resultant denoised moment matrix has entries which are used in place of the raw moments for improved pattern classification. In this paper, we generalize the moment matrix technique to grey scale images and apply the technique to discrimination between M-ary PSK and QAM constellations in signal space. Invariance to unknown phase angle and signal amplitude is achieved by representing the in-phase and quadrature components of the signal in the complex plane, and computing joint moments of normalized magnitude and phase components.

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Moment matrices for recognition of spatial pattern in noisy images

Cited by 7 publications

References 7 publications

Reconstruction of Oriya Alphabets Using Zernike Moments

Reconstruction of Oriya Alphabets Using Zernike Moments

Noisy word recognition using denoising and moment matrix discriminants

Digital modulation classification using power moment matrices

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