Hamid Tairi - Profile on Academia.edu (original) (raw)

Papers by Hamid Tairi

Moving object segmentation in video using spatiotemporal saliency and laplacian coordinates

2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA), 2016

This paper presents a new algorithm for automatic segmentation of moving objects in video based o... more This paper presents a new algorithm for automatic segmentation of moving objects in video based on spatiotemporal saliency and laplacian coordinates (LC). Our algorithm exploits the saliency and the motion information to build a spatio-temporal saliency map, used to extract a moving region of interest (MRI). This region is used to provide automatically the seeds for the segmentation of the moving object using LC. Experiments show a good performance of our algorithm for moving objects segmentation in video without a user interaction, especially on Segtrack dataset.

Signal, Image and Video Processing, 2015

Optical flow approaches for motion estimation calculate vector fields which determine the apparen... more Optical flow approaches for motion estimation calculate vector fields which determine the apparent velocities of objects in time-varying image sequences. Image motion estimation is a fundamental issue in low-level vision and is used in many applications in image sequence processing, such as robot navigation, object tracking, image coding and structure reconstruction. The accuracy of optical flow estimation algorithms has been improving steadily as evidenced by results on the Middlebury optical flow benchmark. Actually, several methods are used to estimate the optical flow, but a good compromise between computational cost and accuracy is hard to achieve. This work presents a combined local-global total variation approach with structure-texture image decomposition. The combination is used to control the propagation phenomena and to gain robustness against illumination changes, influence of noise on the results and sensitivity to outliers. The resulted method is able to compute larger displacements in a reasonable time.

Visual object tracking via the local soft cosine similarity

Pattern Recognition Letters

Abstract In this paper, we propose a robust visual tracking algorithm based on soft similarity un... more Abstract In this paper, we propose a robust visual tracking algorithm based on soft similarity under the Bayesian framework. Firstly, we propose a Local Soft Similarity based on Soft Cosine Measure (L3SCM) that measures the soft similarity between two vectors of features in Vector Space Model (VSM) by taking into account dependencies between these features. Secondly, we model the motion model component of the proposed tracker by using the Bayesian framework, then we apply the L3SCM measure into the observation model component to measure the local similarities between the template of the tracked target and the sampled candidates in incoming frame of a given image sequence. Finally, we integrate a simple scheme to update the target template throughout the tracking process in order to improve the robustness of the proposed tracker. Experimental results on several challenging image sequences illustrate that the proposed method performs better against several state-of-the-art trackers.

Brain Tumor Segmentation Based on Deep Learning

2019 International Conference on Intelligent Systems and Advanced Computing Sciences (ISACS)

Brain tumors develop rapidly and aggressively, causing brain damage and can be life threatening. ... more Brain tumors develop rapidly and aggressively, causing brain damage and can be life threatening. Determining the extent of the tumor is a major challenge in brain tumor treatment planning and quantitative assessment to ameliorate the quality of life of patients. Magnetic resonance imaging (MRI) is an imaging technique widely used to evaluate these brain tumors, but manual segmentation prevented by the large amount of data generated by the MRI is a very long task and the performance is highly dependent on operator's experience. In this context, a reliable automatic segmentation method for segmenting the brain tumor is necessary for effective measurement of the extent of the tumor. There are several image segmentation algorithms, each with its own advantages and limitations. In this paper, we propose a method based on Deep Learning, using deep convolution networks based on the U-Net model. Our method was evaluated on real images provided by Medical Image Computing and Computer-Assisted Interventions BRATS 2017 datasets, which contain both HGG and LGG patients. Based on the experiments, our method can provide a segmentation that is both efficient and robust compared to the manually delineated ground truth. Our model showed a maximum Dice Similarity Coefficient metric of 0.81805 and 0.8103 for the dataset used.

Facial expression recognition based on geometric features

2020 International Conference on Intelligent Systems and Computer Vision (ISCV)

Meaningful Learning for Deep Facial Emotional Features

Neural Processing Letters

Solving sub-pixel image registration problems using phase correlation and Lucas-Kanade optical flow method

2017 Intelligent Systems and Computer Vision (ISCV)

Vehicle counting system in real-time

2018 International Conference on Intelligent Systems and Computer Vision (ISCV)

Modelling and implementation of an energy management simulator based on agents using optimised fuzzy rules: application to an electric vehicle

International Journal of Innovative Computing and Applications

Human motion tracking under indoor and outdoor surveillance system

International Journal of Innovative Computing and Applications

Computational Visual Media

Image segmentation is one of the most basic tasks in computer vision and remains an initial step ... more Image segmentation is one of the most basic tasks in computer vision and remains an initial step of many applications. In this paper, we focus on interactive image segmentation (IIS), often referred to as foreground-background separation or object extraction, guided by user interaction. We provide an overview of the IIS literature by covering more than 150 publications, especially recent works that have not been surveyed before. Moreover, we try to give a comprehensive classification of them according to different viewpoints and present a general and concise comparison of the most recent published works. Furthermore, we survey widely used datasets, evaluation metrics, and available resources in the field of IIS.

Saliency-guided automatic detection and segmentation of tumor in breast ultrasound images

Biomedical Signal Processing and Control

PV-DAE: A hybrid model for deceptive opinion spam based on neural network architectures

Expert Systems with Applications

A robust system for road sign detection and classification using LeNet architecture based on convolutional neural network

Soft Computing

A robust classification to predict learning styles in adaptive E-learning systems

Education and Information Technologies

A breast tumors segmentation and elimination of pectoral muscle based on hidden markov and region growing

Multimedia Tools and Applications

Application of MEEMD in post-processing of dimensionality reduction methods for face recognition

IET Biometrics

Moving object detection zone using a block-based background model

IET Computer Vision

Automatic detection of the tumour on mammogram images based on hidden Markov and active contour with quasi-automatic initialisation

International Journal of Medical Engineering and Informatics

Transactions on Machine Learning and Artificial Intelligence

We propose in this article a study that illustrates the techniques used to represent the educatio... more We propose in this article a study that illustrates the techniques used to represent the educational objects which serve to facilitate their reuse. We will enrich this study by taking into account the semantics of the contents of the learning objects while including the metadata as parameters of indexing. Another contribution of our study concerns the fact that this indexation relies on ontologies which allow a better semantic representation and facilitate communication between the machine and the users. On the other hand, we will discuss the current standards of indexing while discussing the cases of their use to try to extend the use of these standards in other situations requiring the indexing of resources in E Learning systems.

Moving object segmentation in video using spatiotemporal saliency and laplacian coordinates

2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA), 2016

This paper presents a new algorithm for automatic segmentation of moving objects in video based o... more This paper presents a new algorithm for automatic segmentation of moving objects in video based on spatiotemporal saliency and laplacian coordinates (LC). Our algorithm exploits the saliency and the motion information to build a spatio-temporal saliency map, used to extract a moving region of interest (MRI). This region is used to provide automatically the seeds for the segmentation of the moving object using LC. Experiments show a good performance of our algorithm for moving objects segmentation in video without a user interaction, especially on Segtrack dataset.

Signal, Image and Video Processing, 2015

Optical flow approaches for motion estimation calculate vector fields which determine the apparen... more Optical flow approaches for motion estimation calculate vector fields which determine the apparent velocities of objects in time-varying image sequences. Image motion estimation is a fundamental issue in low-level vision and is used in many applications in image sequence processing, such as robot navigation, object tracking, image coding and structure reconstruction. The accuracy of optical flow estimation algorithms has been improving steadily as evidenced by results on the Middlebury optical flow benchmark. Actually, several methods are used to estimate the optical flow, but a good compromise between computational cost and accuracy is hard to achieve. This work presents a combined local-global total variation approach with structure-texture image decomposition. The combination is used to control the propagation phenomena and to gain robustness against illumination changes, influence of noise on the results and sensitivity to outliers. The resulted method is able to compute larger displacements in a reasonable time.

Visual object tracking via the local soft cosine similarity

Pattern Recognition Letters

Abstract In this paper, we propose a robust visual tracking algorithm based on soft similarity un... more Abstract In this paper, we propose a robust visual tracking algorithm based on soft similarity under the Bayesian framework. Firstly, we propose a Local Soft Similarity based on Soft Cosine Measure (L3SCM) that measures the soft similarity between two vectors of features in Vector Space Model (VSM) by taking into account dependencies between these features. Secondly, we model the motion model component of the proposed tracker by using the Bayesian framework, then we apply the L3SCM measure into the observation model component to measure the local similarities between the template of the tracked target and the sampled candidates in incoming frame of a given image sequence. Finally, we integrate a simple scheme to update the target template throughout the tracking process in order to improve the robustness of the proposed tracker. Experimental results on several challenging image sequences illustrate that the proposed method performs better against several state-of-the-art trackers.

Brain Tumor Segmentation Based on Deep Learning

2019 International Conference on Intelligent Systems and Advanced Computing Sciences (ISACS)

Brain tumors develop rapidly and aggressively, causing brain damage and can be life threatening. ... more Brain tumors develop rapidly and aggressively, causing brain damage and can be life threatening. Determining the extent of the tumor is a major challenge in brain tumor treatment planning and quantitative assessment to ameliorate the quality of life of patients. Magnetic resonance imaging (MRI) is an imaging technique widely used to evaluate these brain tumors, but manual segmentation prevented by the large amount of data generated by the MRI is a very long task and the performance is highly dependent on operator's experience. In this context, a reliable automatic segmentation method for segmenting the brain tumor is necessary for effective measurement of the extent of the tumor. There are several image segmentation algorithms, each with its own advantages and limitations. In this paper, we propose a method based on Deep Learning, using deep convolution networks based on the U-Net model. Our method was evaluated on real images provided by Medical Image Computing and Computer-Assisted Interventions BRATS 2017 datasets, which contain both HGG and LGG patients. Based on the experiments, our method can provide a segmentation that is both efficient and robust compared to the manually delineated ground truth. Our model showed a maximum Dice Similarity Coefficient metric of 0.81805 and 0.8103 for the dataset used.

Facial expression recognition based on geometric features

2020 International Conference on Intelligent Systems and Computer Vision (ISCV)

Meaningful Learning for Deep Facial Emotional Features

Neural Processing Letters

Solving sub-pixel image registration problems using phase correlation and Lucas-Kanade optical flow method

2017 Intelligent Systems and Computer Vision (ISCV)

Vehicle counting system in real-time

2018 International Conference on Intelligent Systems and Computer Vision (ISCV)

Modelling and implementation of an energy management simulator based on agents using optimised fuzzy rules: application to an electric vehicle

International Journal of Innovative Computing and Applications

Human motion tracking under indoor and outdoor surveillance system

International Journal of Innovative Computing and Applications

Computational Visual Media

Image segmentation is one of the most basic tasks in computer vision and remains an initial step ... more Image segmentation is one of the most basic tasks in computer vision and remains an initial step of many applications. In this paper, we focus on interactive image segmentation (IIS), often referred to as foreground-background separation or object extraction, guided by user interaction. We provide an overview of the IIS literature by covering more than 150 publications, especially recent works that have not been surveyed before. Moreover, we try to give a comprehensive classification of them according to different viewpoints and present a general and concise comparison of the most recent published works. Furthermore, we survey widely used datasets, evaluation metrics, and available resources in the field of IIS.

Saliency-guided automatic detection and segmentation of tumor in breast ultrasound images

Biomedical Signal Processing and Control

PV-DAE: A hybrid model for deceptive opinion spam based on neural network architectures

Expert Systems with Applications

A robust system for road sign detection and classification using LeNet architecture based on convolutional neural network

Soft Computing

A robust classification to predict learning styles in adaptive E-learning systems

Education and Information Technologies

A breast tumors segmentation and elimination of pectoral muscle based on hidden markov and region growing

Multimedia Tools and Applications

Application of MEEMD in post-processing of dimensionality reduction methods for face recognition

IET Biometrics

Moving object detection zone using a block-based background model

IET Computer Vision

Automatic detection of the tumour on mammogram images based on hidden Markov and active contour with quasi-automatic initialisation

International Journal of Medical Engineering and Informatics

Transactions on Machine Learning and Artificial Intelligence

We propose in this article a study that illustrates the techniques used to represent the educatio... more We propose in this article a study that illustrates the techniques used to represent the educational objects which serve to facilitate their reuse. We will enrich this study by taking into account the semantics of the contents of the learning objects while including the metadata as parameters of indexing. Another contribution of our study concerns the fact that this indexation relies on ontologies which allow a better semantic representation and facilitate communication between the machine and the users. On the other hand, we will discuss the current standards of indexing while discussing the cases of their use to try to extend the use of these standards in other situations requiring the indexing of resources in E Learning systems.