Image classification using separable invariant moments of Charlier-Meixner and support vector machine

Hmimid, Abdeslam; Sayyouri, Mhamed; Qjidaa, Hassan

doi:10.1007/s11042-018-5623-3

Cited by 28 publications

(10 citation statements)

References 55 publications

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“…In Equation ( 8), n is the image bit width. The MSE is the mean square error, which is defined in Equation (9). X is the ground truth, and F(Y) is the reconstructed image.…”

Section: Results and Analysismentioning

confidence: 99%

“…This article does not discuss interpolation-based methods and reconstructed-based methods since these two types of methods are usually treated as traditional methods. For example, moments-based methods are very popular in image reconstruction [8][9][10][11]. Example-based methods establish the relationship between LR and HR images to reconstruct the high-frequency part of the LR images.…”

Section: Introductionmentioning

confidence: 99%

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Optical Remote Sensing Image Denoising and Super-Resolution Reconstructing Using Optimized Generative Network in Wavelet Transform Domain

Feng

Zhang

et al. 2021

Remote Sensing

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High spatial quality (HQ) optical remote sensing images are very useful for target detection, target recognition and image classification. Due to the influence of imaging equipment accuracy and atmospheric environment, HQ images are difficult to acquire, while low spatial quality (LQ) remote sensing images are very easy to acquire. Hence, denoising and super-resolution (SR) reconstruction technology are the most important solutions to improve the quality of remote sensing images very effectively, which can lower the cost as much as possible. Most existing methods usually only employ denoising or SR technology to obtain HQ images. However, due to the complex structure and the large noise of remote sensing images, the quality of the remote sensing image obtained only by denoising method or SR method cannot meet the actual needs. To address these problems, a method of reconstructing HQ remote sensing images based on Generative Adversarial Network (GAN) named “Restoration Generative Adversarial Network with ResNet and DenseNet” (RRDGAN) is proposed, which can acquire better quality images by incorporating denoising and SR into a unified framework. The generative network is implemented by fusing Residual Neural Network (ResNet) and Dense Convolutional Network (DenseNet) in order to consider denoising and SR problems at the same time. Then, total variation (TV) regularization is used to furthermore enhance the edge details, and the idea of Relativistic GAN is explored to make the whole network converge better. Our RRDGAN is implemented in wavelet transform (WT) domain, since different frequency parts could be handled separately in the wavelet domain. The experimental results on three different remote sensing datasets shows the feasibility of our proposed method in acquiring remote sensing images.

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“…In Equation ( 8), n is the image bit width. The MSE is the mean square error, which is defined in Equation (9). X is the ground truth, and F(Y) is the reconstructed image.…”

Section: Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optical Remote Sensing Image Denoising and Super-Resolution Reconstructing Using Optimized Generative Network in Wavelet Transform Domain

Feng

Zhang

et al. 2021

Remote Sensing

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show abstract

“…It is important to point out that some recent methods for computing the invariant moments [ 3 , 17 , 20 , 37 , 39 ] are numerically unstable for large-size images (>1024 × 1024).For this reason, no comparison with these works is carried out. In addition, accelerating and stabilizing the computation of the previously mentioned methods might be a potentially interesting direction for further research.…”

Section: Resultsmentioning

confidence: 99%

“…This method is based on the use of recursive formulas of certain factors involved in RMIs computation. However, this method and others presented in [ 3 , 17 , 20 , 39 ] are not suitable for computing the invariant moments of large-size images (≥1024 × 1024), since these methods involve the use of certain terms depending on gamma and factorial functions when computing the image moment invariants. Motivated by overcoming these problems, a fast and numerically stable computation of large-size image moment invariants is proposed in this paper.…”

Section: Introductionmentioning

confidence: 99%

Fast and stable computation of higher-order Hahn polynomials and Hahn moment invariants for signal and image analysis

Daoui

Karmouni

Sayyouri

et al. 2021

Multimed Tools Appl

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This article presents, on the one hand, new algorithms for the fast and stable computation of discrete orthogonal Hahn polynomials of high order (HPs) based on the elimination of all gamma and factorial functions that cause the numerical fluctuations of HPs, and based on the use of appropriate stability conditions. On the other hand, a new method for the fast and numerically stable computation of Hahn moment invariants (HMIs) is also proposed. This method is mainly based on the use of new recursive relations of HPs and of matrix multiplications when calculating HMIs. To validate the efficiency of the algorithms proposed for the calculation of HPs, several signals and large images (≥4000 × 4000) are reconstructed by Hahn moments (HMs) up to the last order with a reconstruction error tending towards zero ( MSE ≃ 10 −10 ). The efficiency of the proposed method for calculating HMIs is demonstrated on large medical images (2048 × 2048) with a very low relative error ( RE ≃ 10 −10 ). Finally, comparisons with some recent work in the literature are provided.

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“…In theory, the moment approach is divided into three main categories: The non-orthogonal moments, such as geometric and complex moments [5,6], the continuous orthogonal moments [7][8][9][10][11][12][13], and the discrete orthogonal moments (DOMs). We are going to focus mainly on DOMs such as the moments of Tchebichef [14,15], Krawtchouk [14][15][16][17], Hahn [18], Charlier [19][20][21], and last but not least Meixner [21,22], as these have concrete advantages over 3D image analysis. However, it has been noted that the computation of moments is a complex and costly task in terms of time.…”

mentioning

confidence: 99%

Partial 3D Image Reconstruction by Cuboids Using Stable Computation of Hahn Polynomials

Tahiri

Karmouni

Tahiri

et al. 2021

Lecture Notes in Electrical Engineering

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In this paper, we will present a new method of partial reconstruction of the 3D image. The latter is based on the following two concepts: one is the stable discrete orthogonal Hahn polynomials (DOHPs), which significantly reduces numerical defects. The other is the 3D image cuboid representation (ICR) to accelerate the computation time of discrete orthogonal Hahn moments (DOHMs) and improve the quality of 3D image reconstruction. The results of the simulation confirm the ability of the 3D image reconstruction using the proposed process. IntroductionResearch on 3D image reconstruction has always been a difficult challenge. The latter can be used for evaluating an object's 3D profile, as well as for defining a profile 3D coordinate at any given point. The reconstruction of 3D objects constitutes a

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Image classification using separable invariant moments of Charlier-Meixner and support vector machine

Cited by 28 publications

References 55 publications

Optical Remote Sensing Image Denoising and Super-Resolution Reconstructing Using Optimized Generative Network in Wavelet Transform Domain

Optical Remote Sensing Image Denoising and Super-Resolution Reconstructing Using Optimized Generative Network in Wavelet Transform Domain

Fast and stable computation of higher-order Hahn polynomials and Hahn moment invariants for signal and image analysis

Partial 3D Image Reconstruction by Cuboids Using Stable Computation of Hahn Polynomials

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