Color image analysis of quaternion discrete radial Krawtchouk moments

Amakdouf, Hicham; Zouhri, Amal; Mallahi, Mostafa El; Qjidaa, Hassan

doi:10.1007/s11042-020-09120-0

Cited by 19 publications

(3 citation statements)

References 19 publications

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“…Apostolidis and Papakostas [44] showed that using Krawtchouk moments as an image local descriptor and a watermarking attack can affects the accuracy of deep learning models when it applied in medical images. Amakdouf et al [45] came up with quaternion radial Krawtchouk moments that could be useful in the field of color image analysis by showing a good representation capability and robustness to different noises. Hassan et al [46] demonstrated that invariant quaternion Krawtchouk moments are more effective than continuous orthogonal moments at representing images and showed more stability against the translation, rotation, and scaling transformation.…”

Section: Introductionmentioning

confidence: 99%

Robust face recognition using convolutional neural networks combined with Krawtchouk moments

Madmoune

Ouariachi

Zenkouar

et al. 2023

IJECE

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<span lang="EN-US">Face recognition is a challenging task due to the complexity of pose variations, occlusion and the variety of face expressions performed by distinct subjects. Thus, many features have been proposed, however each feature has its own drawbacks. Therefore, in this paper, we propose a robust model called Krawtchouk moments convolutional neural networks (KMCNN) for face recognition. Our model is divided into two main steps. Firstly, we use 2D discrete orthogonal Krawtchouk moments to represent features. Then, we fed it into convolutional neural networks (CNN) for classification. The main goal of the proposed approach is to improve the classification accuracy of noisy grayscale face images. In fact, Krawtchouk moments are less sensitive to noisy effects. Moreover, they can extract pertinent features from an image using only low orders. To investigate the robustness of the proposed approach, two types of noise (salt and pepper and speckle) are added to three datasets (YaleB extended, our database of faces (ORL), and a subset of <a name="_Hlk128818620"></a>labeled faces in the wild (LFW)). Experimental results show that KMCNN is flexible and performs significantly better than using just CNN or when we combine it with other discrete moments such as Tchebichef, Hahn, Racah moments in most densities of noises.</span>

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Section: Introductionmentioning

confidence: 99%

Robust face recognition using convolutional neural networks combined with Krawtchouk moments

Madmoune

Ouariachi

Zenkouar

et al. 2023

IJECE

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“…In this work, we will rely on our research in the field of artificial intelligence for the service of health [ 2 , 3 , 13 , 15 – 20 , 29 – 32 , 34 , 44 ]. We plan to offer a new approach for the early detection of COVID19 that will be make available to structures in rural and remote areas with a simple chest X-ray machine.…”

Section: Introductionmentioning

confidence: 99%

Recognizing COVID-19 from chest X-ray images for people in rural and remote areas based on deep transfer learning model

Qjidaa

Ben-fares

Amakdouf

et al. 2022

Multimed Tools Appl

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In this article, we propose Deep Transfer Learning (DTL) Model for recognizing covid-19 from chest x-ray images. The latter is less expensive, easily accessible to populations in rural and remote areas. In addition, the device for acquiring these images is easy to disinfect, clean and maintain. The main challenge is the lack of labeled training data needed to train convolutional neural networks. To overcome this issue, we propose to leverage Deep Transfer Learning architecture pre-trained on ImageNet dataset and trained Fine-Tuning on a dataset prepared by collecting normal, COVID-19, and other chest pneumonia X-ray images from different available databases. We take the weights of the layers of each network already pre-trained to our model and we only train the last layers of the network on our collected COVID-19 image dataset. In this way, we will ensure a fast and precise convergence of our model despite the small number of COVID-19 images collected. In addition, for improving the accuracy of our global model will only predict at the output the prediction having obtained a maximum score among the predictions of the seven pre-trained CNNs. The proposed model will address a three-class classification problem: COVID-19 class, pneumonia class, and normal class. To show the location of the important regions of the image which strongly participated in the prediction of the considered class, we will use the Gradient Weighted Class Activation Mapping (Grad-CAM) approach. A comparative study was carried out to show the robustness of the prediction of our model compared to the visual prediction of radiologists. The proposed model is more efficient with a test accuracy of 98%, an f1 score of 98.33%, an accuracy of 98.66% and a sensitivity of 98.33% at the time when the prediction by renowned radiologists could not exceed an accuracy of 63.34% with a sensitivity of 70% and an f1 score of 66.67%.

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“…Recently, orthogonal discrete moments have been widely employed in image detection, reconstruction, watermarking, recognition, classification, compression, and other Blurred image recognition. [22][23][24] A few years ago, divers descriptors such as moments have been generally used in image engineering, [25][26][27] image recognition and classification, [28][29][30] digital object watermarking, image compression, object detection, and other types of applications. [31][32][33] The H ∞ deconvolution filtering for 2D systems is rarely addressed in the literature, particularly in rebuild the 2D signal in transmission and seismic sensors.…”

Section: Introductionmentioning

confidence: 99%

H_∞ deconvolution filter for two‐dimensional numerical systems using orthogonal moments

Boukili

Mallahi

El-Amrani

et al. 2021

Optim Control Appl Methods

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In this article, we propose the issue of H ∞ deconvolution filtering for two-dimensional (2D) systems described by the Fornasini-Marchesini local state-space model. The main challenge is the design of a deconvolution filter to rebuild the 2D signal so that the filter error system is asymptotically stable and preserves a guaranteed H ∞ performance. To overcome this issue, we use some free matrix variables to eliminate coupling between Lyapunov matrix and system matrices to obtain sufficient conditions in linear matrix inequality form to ensure the desired stability and performance of the error systems in the first time. Moreover, we use the orthogonal moments to extract the feature vectors to generate the input system, with the minimum information with and without noise. Simulation examples are provided to show that the new design technology proposed in this article achieves better H ∞ performance than the existing design methods. Finally, this work can be very helpful tools for the practitioners in telecommunication, and data scientists to aid them in deconvolution, diagnostic, and transmission.

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Color image analysis of quaternion discrete radial Krawtchouk moments

Cited by 19 publications

References 19 publications

Robust face recognition using convolutional neural networks combined with Krawtchouk moments

Robust face recognition using convolutional neural networks combined with Krawtchouk moments

Recognizing COVID-19 from chest X-ray images for people in rural and remote areas based on deep transfer learning model

H_∞ deconvolution filter for two‐dimensional numerical systems using orthogonal moments

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Color image analysis of quaternion discrete radial Krawtchouk moments

Cited by 19 publications

References 19 publications

Robust face recognition using convolutional neural networks combined with Krawtchouk moments

Robust face recognition using convolutional neural networks combined with Krawtchouk moments

Recognizing COVID-19 from chest X-ray images for people in rural and remote areas based on deep transfer learning model

H∞ deconvolution filter for two‐dimensional numerical systems using orthogonal moments

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Product

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H_∞ deconvolution filter for two‐dimensional numerical systems using orthogonal moments