作者:
Dakhli, RymBarhoumi, WalidUniv Tunis El Manar
Inst Super Informat Lab Rech Informat Modelisat & Traitement Informat Res Team Intelligent Syst Imaging & Artificial Vis 2 Rue Abou Rayhane Bayrouni Ariana 2080 Tunisia Univ Carthage
Ecole Natl Ingenieurs Carthage Tunis 2035 Tunisia
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 syste...
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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 segmentation. In addition, according to dermatologists, the shape of the lesion and its infiltration into the surrounding skin are decisive information for the diagnosis. Inspired by this idea, the proposed method is based on gradually expanding the lesion border by including the lesion-shaped border area in order to associate the input image with the corresponding skin cancer type using an end-to-end Inception-ResNet-v2 classifier. The main contribution of this work lies in investigating the Inception-ResNet-v2 model exclusively on the expanded lesion-shaped border. In fact, the obtained results showed that the proposed method is effective in achieving precision rates of 95.6% and 97.26% on HAM10000 and PH2 datasets, respectively.
vision based runway identification using 'marked or unmarked terrain' image sequences captured from a fixed wing unmanned aerial vehicle through onboard stereovision sensor is presented in this paper. An innov...
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vision based runway identification using 'marked or unmarked terrain' image sequences captured from a fixed wing unmanned aerial vehicle through onboard stereovision sensor is presented in this paper. An innovative convolutional neural netwok (CNN) based YOLO-v8 object detection algorithm is used to detect the runway during approach segment of UAv. This deep learning algorithm detects the region of interest in real time and in a computationally efficient manner. The captured unknown road segment or runway image frames are processed and examined for width, length, level and smoothness aspects to qualify as a suitable runway for UAv landings. Also, it is ensured that there are no obstacles, patches or holes on the detected road or runway. Runway start and end threshold lines and regions, touchdown point and runway edge lines are considered as the region of interest. imageprocessing algorithms are applied on the captured runway or road images to detect strong features in the region of interest. Feature detector based imageprocessing algorithm with stereo vision constraint is used to establish the relation between unmanned aerial vehicle's center of gravity and detected runway feature points imageprocessing algorithms like hough line detection, RANSAC, Oriented FAST and Rotated BRIEF (ORB), median filters, morphological methods are applied to extract terrain features. Based on the detected runway orientation and position with respect to UAv position. An automatic landing manoeuvre is performed by UAv autopilot to land the UAv on intended touchdown point on runway computed through detected feature points.
In the field of imageprocessing and machinevision, object tracking is a significant and rapidly developing subfield. The numerous potential applications of object tracking have garnered much attention in recent year...
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In the field of imageprocessing and machinevision, object tracking is a significant and rapidly developing subfield. The numerous potential applications of object tracking have garnered much attention in recent years. The effectiveness of tracking and detecting moving targets is directly related to the quality of motion detection algorithms. This paper presents a new method for estimating the tracking of objects by linearizing their trajectories. Estimating the movement paths of objects in dynamic and complex environments is one of the fundamental challenges in various fields, such as surveillance systems, autonomous navigation, and robotics. Existing methods, such as the Kalman filter and particle filter, each have their strengths and weaknesses. The Kalman filter is suitable for linear systems but less efficient in nonlinear systems, while the particle filter can better handle system nonlinearity but requires more computations. The main goal of this research is to improve the accuracy and efficiency of estimating the movement paths of objects by combining path linearization techniques with existing advanced methods. In this method, the nonlinear model of the object's path is first transformed into a simpler linear model using linearization techniques. The Kalman filter is then used to estimate the states of the linearized system. This approach simplifies the calculations while increasing the estimation accuracy. In the subsequent step, a particle filter-based method is employed to manage noise and sudden changes in the object's trajectory. This combination of two different methods allows leveraging the advantages of both, resulting in a more accurate and robust estimate. Experimental results show that the proposed method performs better than traditional methods, achieving higher accuracy in various conditions, including those with high noise and sudden changes in the movement path. Specifically, the proposed approach improves movement forecasting accuracy by abo
This tutorial discusses optical communication systems that propagate light carrying orbital angular momentum through random media and use machine learning (aka artificial intelligence) to classify the distorted images...
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This tutorial discusses optical communication systems that propagate light carrying orbital angular momentum through random media and use machine learning (aka artificial intelligence) to classify the distorted images of the received alphabet symbols. We assume the reader is familiar with either optics or machine learning but is likely not an expert in both. We review select works on machine learning applications in various optics areas with a focus on beams that carry orbital angular momentum. We then discuss optical experimental design, including generating Laguerre-Gaussian beams, creating and characterizing optical turbulence, and engineering considerations when capturing the images at the receiver. We then provide an accessible primer on convolutional neural networks, a machine learning technique that has proved effective at image classification. We conclude with a set of best prac-tices for the field and provide an example code and a benchmark dataset for researchers looking to try out these techniques.(c) 2022 Optica Publishing Group
Tuberculosis (TB) remains a global health threat, and rapid, automated and accurate diagnosis is crucial for effective control. The tedious and subjective nature of Ziehl-Neelsen (ZN) stained smear microscopy for iden...
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Tuberculosis (TB) remains a global health threat, and rapid, automated and accurate diagnosis is crucial for effective control. The tedious and subjective nature of Ziehl-Neelsen (ZN) stained smear microscopy for identifying Mycobacterium tuberculosis (MTB) motivates the exploration of alternative approaches. In recent years, machine learning (ML) methods have emerged as promising tools for automated TB detection in ZN-stained images. This systematic literature review (SLR) comprehensively examines the application of ML methods for TB detection between 2017 and 2023, focusing on their performance metrics and employed dataset characteristics. The study identifies advancements, establishes the state of the art, and pinpoints areas for future research and development in this domain. It sheds light on the discussion about the readiness of machine-learning methods to be confidently, reliably and cost-effectively used to automate the process of tuberculosis detection in ZN slides, being it significant for the health systems worldwide. Following established SLR guidelines, we defined research questions, retrieved 175 papers from 7 well-known sources, and discarded those not complying with the inclusion criteria. Data extraction and analysis were performed on the resulting 65 papers to address our research questions. The key contributions of this review are as follows. First, it presents a characterization of the state of the art of ML methods for ZN-stained TB detection, especially in sputum and tissue. Second, it analyzes top-performing methods and pre-processing techniques. Finally, it pinpoints key research gaps and opportunities.
The proceedings contain 31 papers. The special focus in this conference is on Internet of Everything and Quantum Information processing. The topics include: Revolutionizing Agriculture: A Mobile App for Rapid Plant Di...
ISBN:
(纸本)9783031619281
The proceedings contain 31 papers. The special focus in this conference is on Internet of Everything and Quantum Information processing. The topics include: Revolutionizing Agriculture: A Mobile App for Rapid Plant Disease Prediction and Sustainable Food Security;EMG Based Human machine Integration for IoT Based Instruments;medrack: Bridging Trust and Technology for Safer Drug Supply Chain Using Ethereum and IoT;a Review on Tuberculosis Pattern Detection Based on various machine Learning Techniques;sensor Based Hand Gesture Identification for Human machine Interface;an Improved Detection System Using Genetic Algorithm and Decision Tree;a Detailed Analysis of Colorectal Polyp Segmentation with U-Network;a Review on Internet of Things (IoT): Parkinson’s Disease Monitoring Device;machine Learning-Based Prediction of Temperature Rise in Squirrel Cage Induction Motor (SCIM);quantum Many-Body Problems: Quantum machine Learning applications;Experimental Study on the Impact of Airborne Dust Deposition on Pv Modules Using Internet of Things;bidirectional Converter with Time Utilization-Based Tariff Investigation and IoT Monitoring of Charging Parameters Based on G2v and v2G Operations;predictive Analysis of Telecom Customer Churn Using machine Learning Techniques;baker’s Map Based Chaotic image Encryption in Military Surveillance Systems;Cyber Security Investigation of GPS-Spoofing Attack in Military UAv Networks;ioT Based Enhanced Safety Monitoring System for Underground Coal Mines Using LoRa Technology;ioT Based Hydroponic System for Sustainable Organic Farming;predicting Stride Length from Acceleration Signals Using Lightweight machine Learning Algorithms;unveiling Hate: Multimodal Perspectives and Knowledge Graphs;vision-Based Toddler Activity Recognition: Challenges and applications;automated W-Sitting Posture Detection in Toddlers.
Text-to-image generation is a cutting-edge technology that enables computers to generate images from textual descriptions. While this technology has been extensively researched and applied to English language text, ap...
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ISBN:
(纸本)9783031804373;9783031804380
Text-to-image generation is a cutting-edge technology that enables computers to generate images from textual descriptions. While this technology has been extensively researched and applied to English language text, applying it to Arabic language text is still in its early stages. Additionally, the Arabic language is challenging due to its right-to-left writing system and extensive vocabulary of 1.3 million words. In this paper, we explore text-to-image generation for generating images from Arabic language text descriptions. Firstly, we fine-tune a transformer-based model pre-trained on the Arabic text to transform the text information into affine transformation within the DF-GAN generator. Secondly, we present a text transformer that combines LSTM layers to address the limitation of unrecognized words. Thirdly, a mask predictor is trained into the generator using a weakly supervised method and incorporated into the affine transformation for a more effective integration of image and text features. In addition, we add the DAMSM loss function as a regularization to the loss function to achieve convergences and stability in the training phase. The experiment on two challenging datasets CUB and Oxford-flower shows that our architectures can accurately generate high-quality images faithfully representing the Arabic textual descriptions. We believe the scaling of this task could have critical applications in fields such as Arabic visual learning, e-commerce, advertising, and entertainment.
Chromosome analysis and classification are essential in clinical applications to diagnose various structural and numerical abnormalities. Recently, karyotype analysis using intelligent imageprocessing methods, especi...
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Recent works have shown that objects discovery can largely benefit from the inherent motion information in video data. However, these methods lack a proper background processing, resulting in an over-segmentation of t...
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ISBN:
(纸本)9798350318920;9798350318937
Recent works have shown that objects discovery can largely benefit from the inherent motion information in video data. However, these methods lack a proper background processing, resulting in an over-segmentation of the non-object regions into random segments. This is a critical limitation given the unsupervised setting, where object segments and noise are not distinguishable. To address this limitation we propose BMOD, a Background-aware Motion-guided Objects Discovery method. Concretely, we leverage masks of moving objects extracted from optical flow and design a learning mechanism to extend them to the true foreground composed of both moving and static objects. The background, a complementary concept of the learned foreground class, is then isolated in the object discovery process. This enables a joint learning of the objects discovery task and the object/non-object separation. The conducted experiments on synthetic and real-world datasets show that integrating our background handling with various cutting-edge methods brings each time a considerable improvement. Specifically, we improve the objects discovery performance with a large margin, while establishing a strong baseline for object/non-object separation.
Cardiac arrhythmia refers to irregular heartbeats caused by anomalies in electrical transmission in the heart muscle, and it is an important threat to cardiovascular health. Conventional monitoring and diagnosis still...
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Cardiac arrhythmia refers to irregular heartbeats caused by anomalies in electrical transmission in the heart muscle, and it is an important threat to cardiovascular health. Conventional monitoring and diagnosis still depend on the laborious visual examination of electrocardiogram (ECG) devices, even though ECG signals are dynamic and complex. This paper discusses the need for an automated system to assist clinicians in efficiently recognizing arrhythmias. The existing machine-learning (ML) algorithms have extensive training cycles and require manual feature selection;to eliminate this, we present a novel deep learning (DL) architecture. Our research introduces a novel approach to ECG classification by combining the vision transformer (viT) and the capsule network (CapsNet) into a hybrid model named viT-Cap. We conduct necessary preprocessing operations, including noise removal and signal-to-image conversion using short-time Fourier transform (SIFT) and continuous wavelet transform (CWT) algorithms, on both normal and abnormal ECG data obtained from the MIT-BIH database. The proposed model intelligently focuses on crucial features by leveraging global and local attention to explore spectrogram and scalogram image data. Initially, the model divides the images into smaller patches and linearly embeds each patch. Features are then extracted using a transformer encoder, followed by classification using the capsule module with feature vectors from the viT module. Comparisons with existing conventional models show that our proposed model outperforms the original viT and CapsNet in terms of classification accuracy for both binary and multi-class ECG classification. The experimental findings demonstrate an accuracy of 99% on both scalogram and spectrogram images. Comparative analysis with state-of-the-art methodologies confirms the superiority of our framework. Additionally, we configure a field-programmable gate array (FPGA) to implement the proposed model for real-time ar
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