Image analysis by Meixner moments and a digital filter

Jahid, Tarik; Hmimid, Abdeslam; Karmouni, Hicham; Sayyouri, Mhamed; Qjidaa, Hassan; Rezzouk, A.

doi:10.1007/s11042-017-5371-9

Cited by 33 publications

(8 citation statements)

References 24 publications

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“…Zhu et al [11,12] demonstrated that discrete orthogonal moments are more effective than continuous orthogonal moments at representing images. The types of discrete orthogonal moments (DOMs) according to their corresponding discrete orthogonal polynomials include Tchebichef [13,14], Krawtchouk [15][16][17][18], Charlier [19,20 ], Hahn [21,22] and Meixner [23,24] moments. At the present time, Discrete Orthogonal Moments (DOMs) are gaining popularity in analyzing one-dimensional signals due to their effectiveness in capturing digital information without redundancy.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Computation of Discrete Orthogonal Moments for Bio-Signals Reconstruction

Fathi

Ahmed

Makhlouf

2022

Preprint

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Bio-signals are extensively used in diagnosing many diseases in wearable devices. In signal processing, signal reconstruction is one of the essential applications. Discrete Orthogonal Moments (DOMs) are effective analysis tools for signals that can extract digital information without redundancy. The propagation of numerical errors is a significant challenge for the computation of DOMs at high orders. This problem damages the orthogonality property of these moments, which restricts the ability to recover the signal's distinct and unique components with no redundant information. This paper proposes a stable computation of DOMs based on QR decomposition methods: the Gram-Schmidt, Householder, and Given Rotations methods. It also presents a comparative study on the performance of the types of moments: Tchebichef, Krawtchouk, Charlier, Hahn, and Meixner moments. The proposed algorithm's evaluation is done using the MIT-BIH arrhythmia dataset in terms of mean square error (MSE ) and peak signal to noise ratio ( PSNR). The results demonstrate the superiority of the proposed method in computing DOMs, especially at high moment orders. Moreover, the results indicate that the Householder method outperforms Gram-Schmidt and Given Rotations methods in execution time and reconstruction quality. The comparative results show that Tchebichef, Krawtchouk, and Charlier moments have superior reconstruction quality than Hahn and Meixner moments, and Tchebichef generally has the highest performance in signal reconstruction.

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Section: Introductionmentioning

confidence: 99%

An Efficient Computation of Discrete Orthogonal Moments for Bio-Signals Reconstruction

Fathi

Ahmed

Makhlouf

2022

Preprint

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“…The advantage of the FIR filter compared to the IIR filter is that it is stable and has a linear phase, which means that in the equalization process there is a delay that aims to make the frequency acceptable at the same time. Several studies that discuss digital filters are [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. This work will be developed by designing a digital filter that is implemented on the Arduino microcontroller device.…”

Section: Introductionmentioning

confidence: 99%

Digital Moving Average Filter Application for Echo Signals and Temperature

2021

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In this work, we propose a digital filter design, namely moving average filter (MAF) to be implemented on echo signal and temperature data. The echo signal data is obtained from the ultrasonic transducer, while the temperature data is obtained from the PI controller. The implementation of the moving average filter on the echo signal data will compare the second-order MAF and the fourth-order MAF. The results of the test show that the use of a digital filter makes the data smoother when compared to unfiltered data. Furthermore, the comparison of orders on the MAF filter has an impact on the filter response getting closer to the ideal characteristics if using a higher order.

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“…In order to solve this problem, discrete orthogonal moments such as Krawtchouk, Tchebichef, Hahn, Charlier and Meixner moments have been successfully introduced within the field of image analysis [27,31,50,70]. The implementation of these moments does not require any numerical approximation since the basis set is orthogonal in the discrete domain of the image coordinate space [12,44].…”

Section: Introductionmentioning

confidence: 99%

Hiding data inside images using orthogonal moments

Soria-Lorente¹,

Berres²,

Avila-Domenech³

2019

Preprint

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In this contribution we propose a novel steganographic method based on several orthogonal polynomials and their combinations. The steganographic algorithm embeds a secrete message at the first eight coefficients of high frequency image. Moreover, this embedding method uses the Beta chaotic map to determine the order of the blocks where the secret bits will be inserted. In addition, from a 128-bit private key and the steps of a cryptography algorithm according to the Advanced Encryption Standard (AES) to generate the key expansion, the proposed method generates a key expansion of 2560 bits, with the purpose to permute the first eight coefficients of high frequency before the insertion. The insertion takes eventually place at the first eight high frequency coefficients in the transformed orthogonal moments domain. Before the insertion of the message the image undergoes a series of transformations. After the insertion the inverse transformations are applied to the original transformations in reverse order. The experimental work on the validation of the algorithm consists of the calculation of the Peak Signal-to-Noise Ratio (PSNR), the Universal Image Quality Index (UIQI), the Image Fidelity (IF), and the Relative Entropy (RE), comparing the same characteristics for the cover and stego image. The proposed algorithm improves the level of imperceptibility and security analyzed through the PSNR and RE values, respectively.

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Image analysis by Meixner moments and a digital filter

Cited by 33 publications

References 24 publications

An Efficient Computation of Discrete Orthogonal Moments for Bio-Signals Reconstruction

An Efficient Computation of Discrete Orthogonal Moments for Bio-Signals Reconstruction

Digital Moving Average Filter Application for Echo Signals and Temperature

Hiding data inside images using orthogonal moments

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