Walid BarhoumiUniversity of Carthage | UCAR
Walid Barhoumi
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Publications (174)
The ever-growing development of deep learning models for skin lesion classification yields consistently to exceptional performance. These models revealed remarkable accuracy and robustness in the diagnosis of various skin lesion classes. Although this advancement enables significant aids in early detection and diagnosis, selecting the most suitable...
Potatoes are an essential global staple, but their susceptibility to various diseases poses a major threat to agricultural productivity. Thus, it is imperative to detect these diseases in good time to implement effective management strategies. In this study, we propose a reliable approach for the early detection of potato leaf diseases, which is ba...
Human emotional states encompass both basic and compound facial expressions. However, current works primarily focus on basic expressions, consequently neglecting the broad spectrum of human emotions encountered in practical scenarios. Compound facial expressions involve the simultaneous manifestation of multiple emotions on an individual's face. Th...
Facial expression recognition is a topic of significant interest in affective computing. However, prior datasets and studies have primarily focused on recognizing facial expressions in controlled environments, limiting their generalizability to realistic context. Thus, despite recent advancements, recognizing facial expressions accurately in wild s...
Since automating the early diagnosis for skin cancer is becoming a necessity for the assistance of dermatologists, different systems have been proposed through the years with competing performances. Decision making in this case is of major importance because it would affect human life. In particular, content-based skin lesion retrieval systems have...
Recognizing human actions from skeletal data is a major challenge, as it does not always deliver optimal performance due to the limited ability to discern the spatio-temporal patterns inherent in skeletal data. This study aims to enhance the precision of action recognition by conceptualizing each action as a 3D matrix, accurately capturing spatio-t...
This study introduces a novel hybrid approach to text annotation that combines rule-based regular expressions with the pretrained neural network model DistilBERT. Given limited task-specific labeled data, regular expressions are first leveraged to efficiently annotate sentences, providing a cost-effective alternative to manual labeling. The annotat...
Rich heterogeneous data provided from social networks can be very big, which imposes considerable challenges for object extraction and image classification. Therefore, the objective of this work is to propose an unsupervised object cosegmentation method that could be notably efficient to improve image classification performance. The main goal of co...
Human fall detection is a crucial topic to study, since there are a lot of cases of person’s fall at hospitals, homes and retirement homes. In fact, falls are very costly, especially for elderly people and persons with special needs, since they may cause death or serious injuries that require instance medical intervention. In order to prevent furth...
The present study addresses the segmentation and the 3D reconstruction of the corpus callosum from MRI scans. Accurate segmentation of the corpus callosum is essential in order to enable its reconstruction and 3D visualization to facilitate early diagnosis. In fact, many studies have established a strong correlation between the shape of the corpus...
Image change detection is an active research topic in the field of remote sensing, as it allows monitoring environmental changes that occur on temporal and spatial scales. However, most of the existing change detection methods suffer from a lack of adaptability to different image types and lack of large-scale validation. In this study, we propose a...
Due to the diversity of image sources, content-based multi-source image fusion and retrieval have shown promising capabilities in computer vision tasks, and especially when applied in Computer-Aided Diagnosis (CAD) to automate and improve the accuracy of medical image analysis. The combination of computer vision and CAD systems has the potential to...
Content-based mammogram retrieval (CBMR) is an effective approach to assist radiologists in diagnosing patients’ mammograms. Indeed, by studying the similar cases to diagnostic prone one, which are retrieved from a dataset containing other mammograms whose results are clinically proven, the final decision could be effectively made. In order to impr...
Accurate classification of glioma grades is crucial for effective treatment planning and patient prognosis. In this extended study, we propose a comprehensive approach combining radiomics features and machine learning techniques to classify glioma grades. We explore the effectiveness of different feature selection methods, including Recursive Featu...
A major challenge in automatic human emotion recognition is that of categorizing the very broad and complex spectrum of human emotions. In this regard, a critical bottleneck is represented by the difficulty in obtaining annotated data to build such models. Indeed, all the publicly available datasets collected to this aim are either annotated with (...
Human action recognition has gained significant attention recently as it can be adopted for various potential applications, notably for smart activity monitoring for surveillance and assisted living purposes. However, recognizing human activities is a challenging task because of the variety of human actions in daily life. In this work, we propose t...
This journal paper deals with Steganography technique which is a method for hiding secret communications inside a cover object during sender-receiver communication. From ancient times to the present, the security of secret information has been a key concern. It has long been an area of interest for researchers to create mechanisms for sending data...
Facial expressions are a crucial aspect of human communication that provide information about emotions, intentions, interactions, and social relationships. They are a universal signal used daily to convey inner behaviors in natural situations. With the increasing interest in automatic facial emotion recognition, deep neural networks have become a p...
The categorization of texture images requires the identification and extraction of meaningful keypoints, a crucial step in ensuring the precise representation of textured images. The literature has introduced numerous descriptors in order to detect and capture both local and global texture characteristics. These descriptors vary in their effectiven...
Convolutional Neural Networks (CNNs) have been highly successful in computer vision tasks, including leaf disease detection. However, their lack of interpretability limits our understanding of their decision-making process and undermines trust in their predictions. In this study, we aim to unravel the black box of CNNs for leaf disease detection th...
Machine learning applications are increasingly challenged by the growing volume of data. In this context, selecting relevant features from the vast pool of extracted data remains a critical issue. This challenge is particularly prominent in facial expression recognition tasks, where the number of features extracted from facial images can be overwhe...
The presence of plant leaf diseases poses a threat to food security and their detection becomes increasingly challenging when multiple types of diseases affecting various plant species are present. In such scenarios, the high number of classes necessitates careful consideration of the choice of model, as a single Convolutional Neural Network (CNN)...
Image change detection in remote sensing is crucial for monitoring environmental changes at different temporal and spatial scales. The primary goal is to identify changed pixels in multi-temporal images accurately. However, challenges such as limited large-scale validation and response latency persist. In this study, we propose an accurate automate...
Computer vision technologies have attracted significant interest in agricultural applications in recent years. In fact, at the core of the artificial intelligence, computer vision and machine learning techniques have proved their effectiveness for various tasks, such as the maturity classification, within the framework of precision agriculture. In...
Fog Computing is a term made by Cisco that insinuates extending cloud computing to the edge of a network. Indeed, fog computing supports the operation of Fog/cloud, storage and networking services between end devices and conveyed processing data centers. While depending on the fog network will enhance the performance by eliminating the upper layer...
Accurate segmentation of the Corpus Callosum (CC) plays a crucial role in studying brain connectivity and understanding neurological disorders. However, limited availability of annotated data poses a significant challenge for developing robust segmentation models. In order to deal with this issue, we propose in the study an effective approach that...
In this paper, a multi-stage deep learning model is proposed for brain glioma tumor detection and segmentation from MRI scans. The model consists of two stages: object detection using YOLOv7 with EfficientNet-B0 backbone, and active contour snake model for boundary refinement and segmentation. The proposed method also includes a customized CNN with...
Precision agriculture plays a crucial role in optimizing crop yield, reducing environmental impact, and ensuring sustainable agricultural practices. Early detection and accurate diagnosis of leaf diseases are essential for preventing significant losses in crop production and maintaining food security. However, the inherent challenge of class imbala...
The latest computer vision and machine learning technologies have introduced various computer-aided diagnosis (CAD) systems to automate the early diagnosis of skin lesions. Nevertheless, improvements made by CAD systems are not optimal because of the similarity in the appearance of skin lesions of different classes as well as the limitations of seg...
In this paper, we present an effective method to extract the change in two optical remote-sensing images. The proposed method is mainly composed of the following steps. First, the two input Normalized Difference Vegetation Index (NDVI) images are smoothed using the Shearlet transform. Then, we used ResNet152 architecture in order to extract the fin...
Low-Grade Gliomas (LGG) are the most common malignant brain tumors that greatly define the rate of survival of patients. LGG segmentation across Magnetic Resonance Imaging (MRI) is common and necessary for diagnosis and treatment planning. To achieve this challenging clinical need, a deep learning approach that combines Convolutional Neural Network...
Facial expressions are natural and universal reactions for persons facing any situation, while being extremely associated with human intentions and emotional states. In this framework, Facial Emotion Recognition (FER) aims to analyze and classify a given facial image into one of several emotion states. With the recent progress in computer vision, m...
Feature extraction is an important task for texture image classification. Many descriptors have been proposed in the literature in order to describe textured images locally as well as globally. Researchers’ interpretations differ on the effectiveness of these descriptors depending on the field of application, but no one can deny their complementari...
Facial emotions reflect the person’s moods and show the human affective state that is correlative with non-verbal intentions
and behaviors. Despite the advances on computer vision techniques, capturing automatically the facial expressions in-the-wild remains a very difficult task. In this context, we propose a multichannel convolutional neural netw...
Facial expressions are among the most powerful ways to reveal the emotional state. Therefore , Facial Expression Recognition (FER) has been widely introduced to wide fields of applications, such as security, psychotherapy, neuromarketing, and advertisement. Feature extraction and selection are two essential key issues for the design of efficient FE...
Breast cancer, which is the second-most common and leading cause of cancer death among women, has witnessed growing interest in the two last decades. Fortunately, its early detection is the most effective way to detect and diagnose breast cancer. Although mammography is the gold standard for screening, its difficult interpretation leads to an incre...
Visual inspection, inside an industrial environment, has attracted considerable research attention, in terms of its relation to improving productivity and its impact on building the Industry 4.0. One of the pillars of Industry 4.0 is the Augmented Reality (AR) technology, given its beneficiary for several tasks such as maintenance, assembly, and in...
Automatic facial expression recognition is essential for many potential applications. Thus, having a clear overview on existing datasets that have been investigated within the framework of face expression recognition is of paramount importance in designing and evaluating effective solutions, notably for neural networks-based training. In this surve...
Classifying texture images relies heavily on the quality of the extracted features. However, producing a reliable set of features is a difficult task that often requires human intervention to select a set of prominent primitives. The process becomes more difficult when it comes to fuse low-level descriptors because of data redundancy and high dimen...
Feature selection for tumor treatment outcome prediction in PET scans amounts to determine the best predictors in order to classify treatment outcomes for unseen data. Several challenges have to be addressed, notably the dissensus about the most predictive radiomic features and the relatively small-sized and imbalanced training samples. To overcome...
Analysing local texture and generating features are two key issues for automatic cancer detection in mammo-graphic images. Recent researches have shown that deep neural networks provide a promising alternative to hand-driven features which suffer from curse of dimensionality and low accuracy rates. However, large and balanced training data are fore...
Facial Expression Recognition (FER) is an active area of research in computer vision with a plethora of applications that have invested several techniques to improve recognition performance. We notice that most of these applications are oriented much more towards posed and environment-controlled emotions. However, FER in the wild remains an area de...
Face recognition has attracted the attention of many researchers during the last years due to its many applications in various fields. However, this task faces several challenges related to many changes that can affect the human face. In particular, make-up faces represent a major challenge for facial verification. To deal with this issue, we propo...
To describe image data, prominent keypoints are commonly detected before running an extraction process to generate a feature vector. However, producing a reliable set of features is difficult and often requires human intervention. Indeed, images can undergo different changes that can affect the result and decrease the classification performance. To...
Content-Based Dermatological Lesion Retrieval (CBDLR) systems retrieve similar skin lesion images, with a pathology-confirmed diagnosis, for a given query image of a skin lesion. By producing an intuitive support to both inexperienced and experienced dermatologists, the early diagnosis through CBDLR screening can significantly enhance the patients’...
Person re-identification from videos taken by multiple cameras from different views is a very challenging problem that has attracted growing interest in last years. In fact, the same person from significant cross-view has different appearances from clothes change, illumination, and cluttered background. To deal with this issue, we use the skeleton...
Content-Based Mammogram Retrieval (CBMR) methods using Multi-View Information Fusion (MVIF) have triggered a growing interest in the last years given their ability to help radiologists make the right breast-cancer related decision. To further improve the retrieval performance, this paper introduces an efficient MVIF-CBMR method based on late fusion...
Retinal blood vessel segmentation in fundus images has become essential for various applications of computer-aided anomaly analysis. In this work, we propose an automated segmentation method based on mathematical morphology combined with entropy information, what allows an accurate classification of each pixel depending on its neighbors' comportmen...
Content-Based Mammogram Retrieval (CBMR) represents the most effective method for the breast cancer diagnosis, especially CBMR based on the fusion of different mammogram views. In this work, an efficient four-view CBMR method is proposed in order to further improve the mammogram retrieval performance. The proposed method consists in combining the r...
Deaths and injuries resulting from road traffic crashes remain a serious problem globally and current trends suggest that this will continue to be the case in the foreseeable future. In this paper, we propose a reliable method towards drowsiness detection by analyzing images of the driver's face. In fact, the shadows caused by wearing glasses and/o...
Breast cancer, which is the second-most common and leading cause of cancer death among women, has witnessed growing interest in the two last decades. Fortunately, its early detection is the most effective way to detect and diagnose breast cancer. Although mammography is the gold standard for screening, its difficult interpretation leads to an incre...
Describing texture is a very challenging problem for many image-based expert and intelligent systems (e.g. defective product detection, people re-identification, abnormality investigation in medical imaging and remote sensing applications) since the process of texture classification relies on the quality of the extracted features. Indeed, detecting...
In this paper, we introduce an unsupervised method for the parcellation of the Corpus Callosum (CC) from MRI images. Since there are no visible landmarks within the structure that explicit its parcels, non-geometric CC parcellation is a challenging task especially that almost of proposed methods are geometric or data-based. In fact, in order to sub...
Several methods of brain image registration have been proposed in order to overcome the requirement of clinicians. In this paper, we assess the performance of a hybrid method for brain image registration against the most used standard registration tools. Most traditional registration tools use different methods for mono- and multi-modal registratio...
Texture offers an effective characterization of image shape and orientation. Thus, a predominant task is to detect and extract texture features that discriminate accurately images within different semantic classes. The challenge resides in making these features invariant to several changes, such as affine transformation and viewpoint change, in ord...
The similarity measure is an essential part of medical image retrieval systems for assisting in radiological diagnosis. Attempts have been made to use distance metric learning approaches to improve the retrieval performance while decreasing the semantic gap. However, existing approaches did not resolve the problem of dependency between images (e.g...
Segmentation of tumors from hybrid PET/MRI scans plays an essential role in accurate diagnosis and treatment planning. However, when treating tumors, several challenges, notably heterogeneity and the problem of leaking into surrounding tissues with similar high uptake, have to be considered. To address these issues, we propose
an automated method f...
3D motion analysis by projecting trajectories on manifolds in a given video can be useful in different applications. In this work, we use two manifolds, Grassmann and Special Orthogonal group SO(3), to analyse accurately complex motions by projecting only skeleton data while dealing with rotation invariance. First, we project the skeleton sequence...
Inspection has been always a topic of interest in the industrial environment. Indeed, the inspection process is particularly very essential for controlling the quality of the finished products. Within this context, this work is proposed an automated artificial vision system in order to control the non-conformities in vehicles. In fact, the main ide...
The main goal of this work is to design an automated solution based on RGB-D data for quantitative analysis, perceptible evaluation and comparison of handball player’s performance. To that end, we introduced a new RGB-D dataset that can be used for an objective comparison and evaluation of handball player’s performance during throws. We filmed 62 h...
This work proposes a framework for simultaneously segmenting foreground objects in a
collection of images having heterogeneous contents. Rather than resorting to image co-segmentation to segment similar objects in multiple images, which requires the use of categorized images, our idea disseminates segmentation information within images. In this way...
In the framework of computer-aided diagnosis of breast cancer, many systems were designed for the detection, the classification and/or the content-based mammogram retrieval (CBMR); in order to serve as a second source of decision for the radiologists. Nevertheless, to improve the final decision-making, the concept of multi-view information fusion (...
This paper presents an effective method for automated 3D/4D facial expression recognition based on Mesh-Local Binary Pattern Difference (mesh-LBPD). In contrast to most of existing methods, the proposed mesh-LBPD is based on a unified set of geometric and appearance features of different facial regions. Indeed, multiple features are combined into a...
Segmenting the foreground objects from an image is an essential low-level step for many expert and intelligent systems, and the success of this key process largely depends on the amount of available training data. However, the cost for obtaining annotations is still a bottleneck, requiring enormous human effort. Thus, in order to obtain higher segm...
This work is about simultaneous segmentation of different foreground objects from a collection of images with heterogeneous contents. Our idea is to propagate the segmentation information between images in order to detect foreground objects in all these images simultaneously, under the hypothesis of using categorized or uncategorized images, rather...
In this paper, we introduce an unsupervised method for the segmentation of the Corpus Callosum (CC) from Magnetic Resonance Imaging (MRI) scans. In fact, in order to extract the CC from sagittal scans in brain MRI, we adopted the Probabilistic Neural Network (PNN) as a clustering technique. Then, we used k-means to obtain the target classes. After...
Human action recognition based on RGB-D sequences is an important research direction in the field of computer vision. In this work, we incorporate the skeleton on the Grassmann manifold in order to model the human action as a trajectory. Given the couple of matched points on the Grassmann manifold, we introduce the special orthogonal group SO(3) to...
Automatic recognition of mammographic images in breast cancer is a complex issue due to the confusing appearance of some perfectly normal tissues which look like masses. The existing computer-aided systems suffer from non-satisfactory accuracy of cancer detection. This paper addresses this problem and proposes two alternative techniques of mammogra...
Deep learning is widely used in medical applications regarding the high performance it can achieve. In this paper, we propose a segmentation recommender based on crowdsourcing and transfer learning for skin lesion extraction. In fact, after collecting and pre-processing data from the ISIC2017 segmentation challenge, we tested two pre-trained archit...
Voxel coloring is a popular method of reconstructing a three-dimensional surface model from a set of calibrated 2D images. However, the reconstruction quality is largely dependent on a thresholding procedure allowing the authors to decide, for each voxel, whether it is photo-consistent or not. Even so, this method is widely used because of its simp...
In this work, we propose an efficient image annotation approach based on visual content of regions. We assume that regions can be described using low-level features as well as high-level ones. Indeed, given a labeled dataset, we adopt a probabilistic semantic model to capture relationships between low-level features and semantic clusters of regions...
Malaria is a life-threatening disease that is caused by the infection of Red Blood Cells (RBC). Its diagnosis is still an urgent need, especially in areas with limited access to quality health care services. In fact, the task is challenging mainly with the contiguity/overlap of cells and low resolution/poor quality of images, we propose an automate...
Background and objective
The aim of computer-aided-detection (CAD) systems for mammograms is to assist radiologists by marking region of interest (ROIs) depicting abnormalities. However, the confusing appearance of some normal tissues that visually look like masses results in a large proportion of marked ROIs with normal tissues. This paper copes w...
This work is about simultaneous segmentation of different foreground objects from a collection of images with heterogeneous contents. Our idea is to propagate the segmentation information between images in order to detect foreground objects in all these images simultaneously, under the hypothesis of using categorized or uncategorized images, rather...
In this paper, we are interested in people re-identification using skeleton information provided by a consumer RGB-D sensor. We perform the modelling and the analysis of human motion by focusing on 3D human joints given by skeletons. In fact, the motion dynamic is modeled by projecting skeleton information on Grassmann manifold. Moreover, in order...
With recent advances in artificial intelligence and pattern recognition, automatic facial expression recognition draws a great deal of interest. In this area, most of works involved 2D imagery. However, they present some challenges related to pose, illumination variation and self-occlusion. To deal with these problems, we propose to reconstruct the...