Aladine Chetouani

Aladine Chetouani
Université d'Orléans | UO · Laboratoire PRISME

associate professor - University of Orleans

About

127
Publications
11,556
Reads
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590
Citations
Introduction
Aladine Chetouani received his master’s degree in computer science from the University Pierre and Marie Curie, France, in 2005, and his PhD degree in image processing from the University of Paris 13, France, in 2010. From 2010 to 2011, he was a postdoctoral researcher with the L2TI Laboratory. He is currently an associate professor with the PRISME Laboratory, Univertsity of Orleans, France. My research interests include image quality, perceptual analysis, visual attention, deep learning and
Additional affiliations
September 2012 - present
Université d'Orléans
Position
  • Polytech'Orléans
Description
  • Maître de conférences (Associate Professor)
September 2012 - present
Polytech'Orléans
Position
  • Professor (Associate)
February 2007 - November 2010
Université Paris 13 Nord
Position
  • PhD Student

Publications

Publications (127)
Article
The ever-growing depth and width of Convolutional Neural Networks (CNNs) drastically increases the number of their parameters and requires more powerful devices to train and deploy. In this paper, we propose a new architecture that outperforms the classical linear convolution function by expanding the latter to a higher degree kernel function witho...
Preprint
Full-text available
In our paper, we propose a novel strategy to learn distortion invariant latent representation from painting pictures for visual attention modelling downstream task. In further detail, we design an unsupervised framework that jointly maximises the mutual information over different painting styles. To show the effectiveness of our approach, we firstl...
Article
We propose a deep reinforcement learning‐based solution for the 3D reconstruction of objects of complex topologies from a single RGB image. We use a template‐based approach. However, unlike previous template‐based methods, which are limited to the reconstruction of 3D objects of fixed topology, our approach learns simultaneously the geometry and to...
Article
The use of 3D technologies is growing rapidly, and stereoscopic imaging is usually used to display the 3D contents. However, compression, transmission and other necessary treatments may reduce the quality of these images. Stereo Image Quality Assessment (SIQA) has attracted more attention to ensure good viewing experience for the users and thus sev...
Article
Full-text available
Human visual Attention modelling is a persistent interdisciplinary research challenge, gaining new interestin recent years mainly due to the latest developments in deep learning. That is particularly evident insaliency benchmarks. Novel deep learning-based visual saliency models show promising results in capturinghigh-level (top-down) human visual...
Preprint
Full-text available
This paper introduces a new framework to predict visual attention of omnidirectional images. The key setup of our architecture is the simultaneous prediction of the saliency map and a corresponding scanpath for a given stimulus. The framework implements a fully encoder-decoder convolutional neural network augmented by an attention module to generat...
Preprint
Full-text available
Visual scanpath is the sequence of fixation points that the human gaze travels while observing an image, and its prediction helps in modeling the visual attention of an image. To this end several models were proposed in the literature using complex deep learning architectures and frameworks. Here, we explore the efficiency of using common deep lear...
Chapter
Convolutional Neural Networks (CNNs) are based on linear kernel at different levels of the network. Linear kernels are not efficient, particularly, when the original data is not linearly separable. In this paper, we focus on this issue by investigating the impact of using higher order kernels. For this purpose, we replace convolution layers with Ke...
Preprint
We propose a novel deep reinforcement learning-based approach for 3D object reconstruction from monocular images. Prior works that use mesh representations are template based. Thus, they are limited to the reconstruction of objects that have the same topology as the template. Methods that use volumetric grids as intermediate representations are com...
Conference Paper
Full-text available
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...
Conference Paper
In this paper, we propose a new architecture to enhance dense layers with a Taylor Series Kernelized Layer (TSKL). The proposed layer expands the underlying linear kernel of dense layers to a higher-order Taylor series kernel. This kernel is able to learn more complex patterns than the linear one and thus be more discriminative. In other words, TKS...
Article
Full-text available
This paper proposes a new method for blind mesh visual quality assessment (MVQA) based on a graph convolutional network. For that, we address the node classification problem to predict the perceived visual quality. First, two matrices represent the 3D mesh: a graph adjacency matrix and a feature matrix. Both matrices are used as input to a shallow...
Preprint
Full-text available
Human vision is naturally more attracted by some regions within their field of view than others. This intrinsic selectivity mechanism, so-called visual attention, is influenced by both high- and low-level factors; such as the global environment (illumination, background texture, etc.), stimulus characteristics (color, intensity, orientation, etc.),...
Preprint
Full-text available
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...
Article
Full-text available
An abundance of objective image quality metrics have been introduced in the literature. One important essential aspect that perceived image quality is dependent on is the viewing distance from the observer to the image. We introduce in this study a novel image quality metric able to estimate the quality of a given image without reference for differ...
Chapter
Facial Expression Recognition (FER) systems aims to classify human emotions through facial expression as one of seven basic emotions: happiness, sadness, fear, disgust, anger, surprise and neutral. FER is a very challenging problem due to the subtle differences that exist between its categories. Even though convolutional neural networks (CNN) achie...
Preprint
Full-text available
Point clouds are essential for storage and transmission of 3D content. As they can entail significant volumes of data, point cloud compression is crucial for practical usage. Recently, point cloud geometry compression approaches based on deep neural networks have been explored. In this paper, we evaluate the ability to predict perceptual quality of...
Conference Paper
Full-text available
Due to the use of 3D contents in various applications, Stereo Image Quality Assessment (SIQA) has attracted more attention to ensure good viewing experience for the users. Several methods have been thus proposed in the literature with a clear improvement for deep learning-based methods. This paper introduces a new deep learning-based no-reference S...
Conference Paper
Full-text available
With the expanding use of stereoscopic imaging for 3D applications, no-reference perceptual quality evaluation has become important to provide good viewing experience. The effect of the quality distortion is related to the scene’s spatial details. Taking this into account, this paper introduces a blind stereoscopic image quality measurement using s...
Article
Point clouds are essential for storage and transmission of 3D content. As they can entail significant volumes of data, point cloud compression is crucial for practical usage. Recently, point cloud geometry compression approaches based on deep neural networks have been explored. In this paper, we evaluate the ability to predict perceptual quality of...
Article
Full-text available
A number of full reference and reduced reference methods have been proposed in order to estimate the perceived visual quality of 3D meshes. However, in most practical situations, there is a limited access to the information related to the reference and the distortion type. For these reasons, the development of a no-reference mesh visual quality (MV...
Conference Paper
Full-text available
Despite the diversity of methods developed in recent years, the implementation of an efficient system for automatic recognition of facial emotions remains a technological challenge that has not been fully resolved. Many problems have not yet been resolved. The occlusion problem remains a challenge today for the research community, certain features...
Conference Paper
Full-text available
Fully connected layer is an essential component of Convolutional Neural Networks (CNNs), which demonstrates its efficiency in computer vision tasks. The CNN process usually starts with convolution and pooling layers that first break down the input images into features, and then analyze them independently. The result of this process feeds into a ful...
Preprint
Full-text available
Fully connected layer is an essential component of Convolutional Neural Networks (CNNs), which demonstrates its efficiency in computer vision tasks. The CNN process usually starts with convolution and pooling layers that first break down the input images into features, and then analyze them independently. The result of this process feeds into a ful...
Article
Pooling layers are spatial down-sampling layers used in convolutional neural networks (CNN) to gradually downscale the feature map, increase the receptive field size and reduce the number of the parameters in the model. The use of pooling layers leads to less computing complexity and memory consumption reduction but also introduces invariance to ce...
Article
Full-text available
Contrast in visible images is one of the most relevant characteristics of visual signals. Since the pioneering works performed in vision psychology and optics, different definitions have been proposed in the literature. However, for the time being there exist no definition of contrast on which the vision research and visual processing scientific co...
Article
Image quality assessment is an important field in computer vision, since it has a great impact on related tasks. To meet these needs, a plethora of metrics has been developed. In this paper, we propose an efficient method that estimates the quality of 2D images without access to the pristine image. This metric is modeled based on the relevant patch...
Conference Paper
Images are often distorted by some necessary treatments (cap- ture, compression, transmission, etc..) that can affect the per- ceptual quality. To evaluate the impact of those treatments, a plethora of metrics were developed in the literature. In this paper, we propose an efficient blind method to estimate the quality of 2D-images based on the sele...
Article
Full-text available
OsteoArthritis (OA) is the most common disorder of the musculoskeletal system and the major cause of reduced mobility among seniors. The visual evaluation of OA still suffers from subjectivity. Recently, Computer- Aided Diagnosis (CAD) systems based on learning methods showed potential for improving knee OA diagnostic accuracy. However, learning di...
Chapter
Despite the fact that face recognition systems have improved significantly, the main concern of these systems remains its security against presentation attacks, so-called spoofing attacks. Therefore, it is important to develop techniques to automatically detect those attacks referred to as presentation attack detection (PAD) mechanisms. It is also...
Article
Full-text available
Despite the rapid growth of face recognition-based biometrics for both authentication and identification, the security of face biometric systems against presentation attacks (also called spoofing attacks) remains a great concern. Indeed, Face recognition-based authentication techniques can be easily spoofed using various types of attacks such photo...
Article
The ARCADIA project aims at using pattern recognition and machine learning to promote a systematic analysis of the large corpus of archaeological pottery fragments excavated in Saran (France). Dating from the High Middle Ages, these sherds have been engraved with repeated patterns using a carved wooden wheel. The study of these engraved patterns al...
Article
Blind or No reference quality evaluation is a challenging issue since it is done without access to the original content. In this work, we propose a method based on deep learning for the mesh visual quality assessment without reference. For a given 3D model, we first compute its mesh saliency. Then, we extract views from the 3D mesh and the correspo...
Preprint
Full-text available
Due to the recent demand of 3-D models in several applications like medical imaging, video games, among others, the necessity of implementing 3-D mesh watermarking schemes aiming to protect copyright has increased considerably. The majority of robust 3-D watermarking techniques have essentially focused on the robustness against attacks while the im...
Preprint
Full-text available
Due to the recent demand of 3-D meshes in a wide range of applications such as video games, medical imaging, film special effect making, computer-aided design (CAD), among others, the necessity of implementing 3-D mesh watermarking schemes aiming to protect copyright has increased in the last decade. Nowadays, the majority of robust 3-D watermarkin...
Chapter
Due to the recent demand of 3-D models in several applications like medical imaging, video games, among others, the necessity of implementing 3-D mesh watermarking schemes aiming to protect copyright has increased considerably. The majority of robust 3-D watermarking techniques have essentially focused on the robustness against attacks while the im...
Conference Paper
Image quality is one of the emerging domain, since it impacts the efficiency of computer vision applications. Another key information that can be exploited to analyze an image is the image utility which estimates the useful of an image. Both in- formation have weak relationship and should be exploited as relevant indicators. In this paper, we focus...
Chapter
Due to the recent demand of 3-D meshes in a wide range of applications such as video games, medical imaging, film special effect making, computer-aided design (CAD), among others, the necessity of implementing 3-D mesh watermarking schemes aiming to protect copyright has increased in the last decade. Nowadays, the majority of robust 3-D watermarkin...
Article
Full-text available
Three-dimensional models have been extensively used in several applications including computer-aided design (CAD), video games, medical imaging due to the processing capability improvement of computers, and the development of network bandwidth. Therefore, the necessity of implementing 3D mesh watermarking schemes aiming to protect copyright has inc...
Article
Full-text available
In this study, the authors introduce new solutions and improvements to the multi-channel blind image deconvolution problem. More precisely, authors' contributions are threefold: (i) At first, a simplified version of the existing cross-relation method for blind system identification is proposed; but most importantly, the authors incorporate into the...
Conference Paper
ARCADIA project aims to enhance the archaeological heritage of ceramic shards extracted in Saran (France). Dating from the High Middle Ages, these shards have been engraved by repeated patterns using a carved wooden wheel. The study of these shards allows the archeologists to better understand the diffusion of ceramic productions. In this paper, we...
Article
Three-dimensional (3-D) meshing has become a commonly used tool in several computer vision applications. As the performance of these applications depends highly on the quality of the meshes, several methods have been proposed in the literature to quantify mesh quality. We propose a 3-D mesh quality measure based on the fusion of some selected featu...
Conference Paper
In this paper, we propose to show the importance to consider the image quality in Computer Vision (CV) applications. We also describe a proposed framework that not only take into account the quality but rather permits to select the more adapted measure for a given CV application. Here, the selection of the image quality metric is based on a degrada...
Article
Predicting the perceived quality of stereoscopic 3D images is a challenging task, especially when the stereo-pair is asymmetrically distorted. Despite the considerable efforts to fix this issue, there is no commonly accepted metric. Most of the attempts consisted in developing full reference quality metrics, while very few efforts have been dedicat...