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Colour-weighted rank transform and improved dynamic programming for fast and accurate stereo matching

The Journal of Photographic Science 2024年第2期72卷

作者： Mohamed Hallek Randa Khemiri Ali Aseere Abdellatif Mtibaa Mohamed Atri a Laboratory of Electronics and Microelectronics University of Monastir Monastir Tunisia b Networked Objects Control and Communication Systems Laboratory ENISO Sousse University Sousse Tunisia c College of Computer Science King Khalid University Abha Saudi Arabia d National Engineering School of Sfax University of Sfax Sfax Tunisia

ABSTRACTReal-time stereo matching with high accuracy is a dynamic research topic; it is attractive in diverse computer vision applications. This paper presents a stereo-matching algorithm that produces high-quality disparity map while maintaining real-time performance. The proposed stereo-matching method is based on three per-pixel difference measurements with adjustment elements. The absolute differences and the gradient matching are combined with a colour-weighted extension of complete rank transform to reduce the effect of radiometric distortion. The disparity calculation is realized using improved dynamic programming that optimizes along and across all scanlines. It solves the inter-scanline inconsistency problem and increases the matching accuracy. The proposed algorithm is implemented on parallel high-performance graphic hardware using the Compute Unified Device Architecture to reach over 240 million disparity evaluations per second. The processing speed of our algorithm reaches 98 frames per second on 240 × 320-pixel images and 32 disparity levels. Our method ranks fourth in terms of accuracy and runtime for quarter-resolution images in the Middlebury stereo benchmark.

关键词： Cost function rank transform guided filter dynamic programming real-time performance stereo matching GPU CUDA gradient matching

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Early Diagnosis of Diabetic Retinopathy using Random Forest Algorithm

Early Diagnosis of Diabetic Retinopathy using Random Forest ...

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International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

作者： Nihel ZAABOUB Ali DOUIK Networked Objects Control and Communication Systems (NOCCS) Laboratory ENISo University of Sousse National Engeneering School of Tunis (ENIT) University of Tunis El Manar National Engeneering School of Sousse (ENISo) University of Sousse

ISBN: (数字)9781728175133

ISBN: (纸本)9781728175140

The diabetic retinopathy is one of the most frequent causes of visual damage and vision loss. It can cause blindness in the absence of the diagnosis and the treatment. The automatic detection of the hard exudate in color fundus retinal images is an important task to early diagnosis the diabetic retinopathy. In this paper, a hard exudate detection algorithm is proposed. It is based on the application of a learning method to retinal image with removed optic disk. This paper proposes the use of Random Forest algorithm with a specific parameter from which a binary mask of exudate is obtained after intensity thresholding. It achieves 91.40% for sensitivity and 94.38% for the accuracy.

关键词： Diabetes Retinopathy Retina Sensitivity Optical sensors Optical imaging Vegetation

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Local saliency estimation and global homogeneity refinement for video saliency detection 11th

Local saliency estimation and global homogeneity refinement ...

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11th International KES Conference on Intelligent Interactive Multimedia: systems and Services, KES-IIMSS 2018

作者： Kalboussi, Rahma Abdellaoui, Mehrez Douik, Ali Networked Objects Control and Communication Systems Laboratory Pôle technologique de Sousse Route de Ceinture Sahloul Sousse4054 Tunisia University of Sousse Sousse Tunisia Higher Institute of Computer Sciences and Communications Technology Hammam SousseSousse Tunisia

ISBN: (纸本)9783319922300

Saliency detection aims to segment the object of interest from the rest of the scene. While there has been a big number of saliency detection methods in still images, video saliency is in its early stages. In this paper, we propose a two stages video saliency detection method using local saliency estimation and global homogeneity refinement. Starting from a patch, the problem of saliency detection is modeled as a growing region which starts from a patch with high saliency information to the background. Local saliency is measured by combining spatial priors presented by local surrounding contrast with temporal information issued from the motion estimation feature. Temporal and spatial information are fused and then used to label each patch as foreground and background patches and produce the final saliency maps. Finally, Global homogeneity refinement is used to refine the saliency results by evaluating the foreground and background probabilities ratio propagated from the patches. Experiments have proved that the proposed method outperforms state-of-the-art methods over two benchmark datasets. © Springer International Publishing AG, part of Springer Nature 2019.

关键词： Motion estimation

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Optimal Placement and Capacity of Combined DGs and SCs in Radial Distribution Networks Based on PSO-OS Algorithm

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2021年第2期48卷 1-14页

作者： Chen, Gonggui Zhang, Ao Zhao, Chenxu Zhang, Zhizhong Professor of Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing400065 China A master degree candidate of Chongqing University of Posts and Telecommunications Chongqing400065 China Co. Ltd Inner Mongolia Power Economy and Technology Research Institute Hohhot010010 China A professor of Key Laboratory of Communication Network and Testing Technology Chongqing University of Posts and Telecommunications Chongqing400065 China

The distribution of power flow can be effectively improved by optimizing the configuration of Distributed Generators (DGs) and Shunt Capacitors (SCs). Proper placements and capacities of DGs or SCs for real power loss minimization at the radial distribution network (RDN) is attracting more attention as people become more and more dependent on electric power resources. In order to overcome the slow convergence and poor effect of intelligent algorithms in this problem, Particle Swarm Optimization with Orientation and Shrinking factor (PSO-OS) is proposed in this paper. In the suggested PSO-OS algorithm, sensitivity analysis sort of distribution network is introduced to guide the direction of variation to simplify the search space, and shrinking factor is introduced to balance search capabilities in various RDN systems. The proposed method is applied to IEEE-33, IEEE-69 and IEEE-119 systems to solve the problem of simultaneous allocation of DGs and SCs. Simulation results show that the energy loss of the system is reduced and the stability is improved after reconfiguration. By repeating the experiment independently with the traditional PSO algorithm, the comparative analysis of the results proves the superior performance of the improved algorithm. By comparing with the results in recent literature, it reveals the effectiveness of the proposed PSO-OS algorithm in finding the optimal position and capacity of DGs and SCs in the distribution network. © 2021. All Rights Reserved.

关键词： Sensitivity analysis

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Video saliency using supervoxels 10th

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10th KES International Conference on Intelligent Interactive Multimedia systems and Services, IIMSS 2017

作者： Kalboussi, Rahma Abdellaoui, Mehrez Douik, Ali Networked Objects Control and Communication Systems Laboratory National Engineering School of Sousse Pôle technologique de Sousse Route de Ceinture Sahloul Sousse4054 Tunisia

ISBN: (纸本)9783319594798

Physiology and neural systems researchers revealed that the visual system is attracted by some parts of an image more than others. Different computational models were developed to simulate the visual system. In this paper we propose a video saliency model that helps to predict and detect the regions of interest in each video frame. We use a supervoxel segmentation as an indicator of dynamic objects. Based on the observation that dynamic objects attract attention when an observer watches a video sequence, supervoxel segmentation provides a first estimation for what belongs to foreground and background. Then, a saliency score is attributed to each supervoxel according to its motion distinctiveness. Experiments over two benchmark datasets, using several evaluation metrics have shown that our proposed method outperforms five saliency detection methods. © Springer International Publishing AG 2018.

关键词： Computers

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control of the chaotic phenomenon in robot path using differential flatness 15

Control of the chaotic phenomenon in robot path using differ...

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15th International Conference on Informatics in control, Automation and Robotics, ICINCO 2018

作者： Nasr, Salah Abadi, Amine Bouallegue, Kais Mekki, Hassen Laboratory of Networked Objects Control and Communication Systems National Engineering School of Sousse University of Sousse Tunisia Department of Electrical Engineering Higher Institute of Applied Sciences and Technology of Sousse Tunisia

ISBN: (纸本)9789897583216

This paper deals with the complex chaotic behavior that can appear in the dynamic trajectory of a mobile robot, when the robot is broken or partly damaged. However, a flatness-based controller is designed to ensure the trajectory planning and tracking. Different mathematical tools have been used such as the flatness control technique and non linear chaotic systems. The simulation results for the kinematic controller are presented to demonstrate the effectiveness of this approach. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

关键词： Mobile robots

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Chaos engineering and control in mobile robotics applications 15

Chaos engineering and control in mobile robotics application...

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15th International Conference on Informatics in control, Automation and Robotics, ICINCO 2018

作者： Nasr, Salah Abadi, Amine Bouallegue, Kais Mekki, Hassen Networked Objects Control and Communication Systems-Laboratory National Engineering School of Sousse University of Sousse Tunisia Department of Electrical Engineering Higher Institute of Applied Sciences and Technology of Sousse Tunisia

ISBN: (纸本)9789897583216

This article briefly summarizes the theory of chaos and its applications. Firstly, we begin by describing chaos as an aperiodic bounded deterministic motion, which is sensitive to initial states and therefore unpredictable after a certain time. Then, fundamental tools of the chaos theory, used for identifying and quantifying chaotic dynamics, are shared. The paper covers a main numerical approach to identify chaos such as the Lyapunov exponents. Many important applications of chaos in several areas such as chaos in electrical and electronic engineering and chaos applications in robotics have been presented. An analysis of the reviewed publications is presented and a brief survey is reported as well. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

关键词： Chaos control

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Deep learning traffic sign detection, recognition and augmentation 17

Deep learning traffic sign detection, recognition and augmen...

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32nd Annual ACM Symposium on Applied Computing, SAC 2017

作者： Abdi, Lotfi Meddeb, Aref National Engineering School of Tunis University of Tunis Networked Objects Control and Communication Systems Laboratory El Manar Tunisia National Engineering School of Sousse University of Sousse Networked Objects Control and Communication Systems Laboratory Tunisia

ISBN: (纸本)9781450344869

Driving is a complex, continuous, and multitask process that involves driver's cognition, perception, and motor movements. The way road traffic signs and vehicle information is displayed impacts strongly driver's attention with increased mental workload leading to safety concerns. Drivers must keep their eyes on the road, but can always use some assistance in maintaining their awareness and directing their attention to potential emerging hazards. Research in perceptual and human factors assessment is needed for relevant and correct display of this information for maximal road traffic safety as well as optimal driver comfort. In-vehicle contextual Augmented Reality (AR) has the potential to provide novel visual feedbacks to drivers for an enhanced driving experience. In this paper, we present a new real-time approach for fast and accurate framework for traffic sign recognition, based on Cascade Deep learning and AR, which superimposes augmented virtual objects onto a real scene under all types of driving situations, including unfavorable weather conditions. Experiments results show that, by combining the Haar Cascade and deep convolutional neural networks show that the joint learning greatly enhances the capability of detection and still retains its realtime performance.

关键词： Augmented reality

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Semantic recognition: Unified framework for joint object detection and semantic segmentation 17

Semantic recognition: Unified framework for joint object det...

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32nd Annual ACM Symposium on Applied Computing, SAC 2017

作者： Abdi, Lotfi Meddeb, Aref National Engineering School of Tunis University of Tunis El Manar Networked Objects Control and Communication Systems Laboratory Tunisia National Engineering School of Sousse University of Sousse Networked Objects Control and Communication Systems Laboratory Tunisia

ISBN: (纸本)9781450344869

Understanding contents of an image, or scene labeling, is an important yet very challenging problem in artificial intelligence and computer vision to improve road safety. Semantic labeling and object detection in road scenes are strongly correlated tasks. Motivated by the complementary effect of the two tasks, we presented a novel framework to address the scene understanding problem. In this paper we propose a new framework for semantic labeling and object detection problem which is able to combine ideas from deep Convolutional Neural Network (CNN) for object detection and fully-connected Conditional Random Field (CRF) for segmenting and labeling. Specifically, we develop a new framework uses global image features to predict detection which drastically reduces its errors from background detections and a pairwise CRF is used as a post-processing step to enforce spatial consistency in the structured prediction. By combining the consistency between final detection results of CNN and CRF based graphical models, our unified framework can effectively leverage the advantages of leading techniques i.e., CRF and CNN for these two tasks. Exten- sive experiments on the PASCAL VOC 2007/2012 data sets demonstrate the effectiveness of our framework for Scene Understanding tasks. Copyright 2017 ACM.

关键词： Semantic Segmentation

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Driver information system: A combination of augmented reality and deep learning 17

Driver information system: A combination of augmented realit...

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32nd Annual ACM Symposium on Applied Computing, SAC 2017

ISBN: (纸本)9781450344869

Improving traffic safety is one of the important goals of Intelligent Transportation systems (ITS). In vehicle-based safety systems, it is more desirable to prevent an accident than to reduce severity of injuries. One of the solutions to prevent accidents is to provide information on the surrounding environment of the driver. Augmented Reality Head-Up Display (AR-HUD) can facilitate a new form of dialogue between the vehicle and the driver;and enhance ITS by superimposing surrounding traffic information on the users view and keep drivers view on roads. In this paper, we propose a fast deeplearning-based object detection approaches for identifying and recognizing road obstacles types, as well as interpreting and predicting complex traffic situations. A single Convolutional Neural Network (CNN) predicts region of interest and class probabilities directly from full images in one evaluation. We also investigated potential costs and benefits of using dynamic conformal AR cues in improving driving safety. Copyright 2017 ACM.

关键词： Augmented reality

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