Ayoub Karine

• Doctor of Engineering
• Associate Professor at Université Paris Cité

Due to recent technological developments, the acquisition and availability of deep-sea imagery has increased exponentially in the last years, leading to an increasing backlog in image annotation and processing, attributable to limited specialized human resources. In this work, we investigate the performance of well-established convolutional neural...

This study is a practical exploration of the application of machine learning for the mechanical analysis of filament-wound thin-composite hydrogen storage tanks under internal pressure. Our innovative approach seamlessly integrates classical laminate theory, comprehensive parametric analysis, and machine learning to advance the state-of-the-art in...

Digital representation of 3D content in the form of 3D point clouds (PC) has gained increasing interest and has emerged in various computer vision applications. However, various degradation may appear on the PC during acquisition, transmission, or treatment steps in the 3D processing pipeline. Therefore, several Full-Reference, Reduced-Reference, a...

This paper addresses the problem of blind stereoscopic image quality assessment (NR-SIQA) using a new multi-task deep learning based-method. In the field of stereoscopic vision, the information is fairly distributed between the left and right views as well as the binocular phenomenon. In this work, we propose to integrate these characteristics to e...

This paper deals with the binary classification ofseals videos. To address this problem, we propose a novelsystem based on two phases : offline and online. The offlinephase aims to generate a trained model. For this end, we usea transfer learning of convolutional neural network approach.Especially, we employed the VGG-16 deep network trained on the...

We propose in this paper a new method for targets recognition from radar images. To characterize the radar images, we adopt a statistical multivariate modeling using copula in the complex wavelet domain. For the recognition step, we investigate the weighted sparse representation-based classification (WSRC) method. To build the dictionary, the estim...

Automatic target recognition has become a flourishing research topic in remote sensing. This problematic is of paramount importance in several military and civilian applications (security, surveillance, automobile, environment, medicine, ...). In this work, we focus on the development of new methodology dedicated to the target recognition from synt...

This paper proposes a new target recognition method for inverse synthetic aperture radar (ISAR) images. This method is based on joint statistical modeling of the complex wavelet coefficients for ISAR image characterization and the sparse representation based classification (SRC) for the recognition. To extract features from an ISAR image, we first...

In this paper, we propose a novel approach to recognize radar targets on inverse synthetic aperture radar (ISAR) and synthetic aperture radar (SAR) images. This approach is based on the multiple salient keypoint descriptors (MSKD) and multitask sparse representation based classification (MSRC). Thus, to characterize the targets in the radar images,...

In this paper, we propose a novel approach to recognize radar targets on inverse synthetic aperture radar (ISAR) and synthetic aperture radar (SAR) images. This approach is based on the multiple salient keypoint descriptors (MSKD) and multitask sparse representation based classification (MSRC). Thus, to characterize the targets in the radar images,...

In this paper, we present a novel approach for radar automatic target recognition on inverse synthetic aperture radar (ISAR). This approach is composed by two complementary steps: feature extraction and recognition. For the feature extraction step, we adopt a statistical modeling of the ISAR image in the complex wavelet domain. For doing so, we app...

This paper presents a new stereo image (SI) retrieval method based on a statistical model of complex wavelet coefficients subbands. In this context, a Gaussian copula-based multivariate model is used to capture the dependence between complex wavelet coefficients of both left and right images, and a non-Gaussian univariate model is used to character...

This paper aims to present a novel method for automatic target recognition based on synthetic aperture radar (SAR) images. In order to describe a region of interest (target area), we use a saliency attention model. Then, the produced saliency map is used as a mask on SAR image in order to separate the ground target from the background. After that,...

Dans le présent papier, nous proposons l’étude et l’application d’une nouvelle approche pour l’aide à la reconnaissance automatique de cibles (ATR, pour Automatic Target Recognition) à partir des images à synthèse d’ouverture inverse (ISAR, pour Inverse Synthetic Aperture Radar). Cette approche est composée de deux phases principales. Dans la premi...

The work presented in this paper is part of the filed of automatic recognition of radar targets. Thus, for assistance in target recognition, we propose a new approach to extract efficient feature from synthetic aperture radar (SAR) images. The proposed approach deals with a combination of two feature descriptors obtained from two methods. In the fi...

Ce papier s'intègre bien dans la problématiques générale de la reconnaissance automatique de cibles radar (ATR, pour Automatic Target Recognition) et ceci en s'appuyant sur des images ISAR (Inverse Synthetic Aperture Radar). Dans le travail présenté dans ce papier, nous proposons un nouveau descripteur caractérisant une image Radar (le cas considér...

This paper addresses the problem of automatic target recognition (ATR) using inverse synthetic aperture radar (ISAR) images. In this context, we propose a novel approach for feature extraction to describe precisely an aircraft target from ISAR images. In our approach, a visual attention model is adopted to separate the salient regions from the back...

This paper presents a novel approach for automatic target recognition (ATR) using inverse synthetic aperture radar (ISAR) images. This proposed approach is mainly composed of two steps. In the first step, we adopt a statistical method to compute a novel target template from feature descriptors. The proposed template is achieved by combining the Gam...

Ce travail s'inscrit dans le cadre des travaux de recherche qui visent le traitement et l'exploitation des images radar dites ISAR (Inverse Sythetic Aperture Radar). Cette thématique de recherche est d'une grande importance dans diverses applications en environnement incertain aérien et maritime. L'objectif phare concerne la proposition d'une chaˆı...

This paper deals with the classification and seg-mentation of seafloor images recorded by sidescan sonar. To address this problem, related to texture analysis, a supervised approach is considered. The features of the textured images are extract by characterizing the wavelet coefficients through parametric probabilistic models. In this contribution,...

This work is part of the sonar imaging classification and segmentation research. This axis of research is important to several domains such as the management of marine resources or the war on mines. Sonar images are composed of many textured homogenous zones, which represent different types of seabeds. It is thus necessary to use texture analysis...