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A research on prediction of bat-borne disease infection through segmentation using diffusion-weighted MR imaging in deep-machine learning approach

A research on prediction of bat-borne disease infection thro...

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2020 International Virtual Conference on Sustainable Materials, IVCSM 2k20

作者： Kannan, M. Priya, C. Chennai India Department Information Technology School of Computing Sciences Vels Institute of Science Chennai India

The theme of this study is to provide a detailed description of its recent improvements in image segmentation and lesion classification in disease prognosis. Previous studies have shown that gray-white matter hyperintensities (GWMH) is one of the hallmarks of Nipah encephalitis, which sometimes occurs during the incubation period. Predicting this type of inflammation is a challenging task because it involves some unknown medical risk factors. A typical Magnetic Resonance Imaging (MRIs) is the best non-invasive system to analyze the anatomical structure of the brain. In-depth analysis of the defined pathological structure from isolated MR imaging leads to a reduction in the processing time of the prognostic model. Modern learning techniques such as Machine learning, Computer Vision, and deep learning are the most promising techniques for determining the optimal outcome, computer can able to learn and extract useful information from historical data using various algorithms. Disease prognosis based on deep learning is sophisticated, so it can handle a variety of difficult tasks including image processing, classification, and feature extraction, noise and object detection. Diffusion-weighted imaging (DWI) in MRIs is a clinical prototype that can be used to diagnose brain abnormalities and to evaluate the microscopic architectural and molecular function of human organs or tissues. In this study, we summarize the results of diagnosing Nipah encephalitis using some publicly available brain encephalitis and encephalopathy databases. © 2021 Elsevier Ltd. All rights reserved.

关键词： Brain injury deep learning DWI Encephalitis GWMH image segmentation Machine learning Nipah disease

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deep learning based virtual point tracking for real-time target-less dynamic displacement measurement in railway applications

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MECHANICAL SYSTEMS AND SIGNAL processing 2022年第0期166卷 108482-108482页

作者： Shi, Dachuan Sabanovic, Eldar Rizzetto, Luca Skrickij, Viktor Oliverio, Roberto Kaviani, Nadia Ye, Yunguang Bureika, Gintautas Ricci, Stefano Hecht, Markus Tech Univ Berlin Inst Land & Sea Transport Syst D-10587 Berlin Germany Vilnius Gediminas Tech Univ Fac Transport Engn LT-10223 Vilnius Lithuania Sapienza Univ Rome Dept Bldg & Environm Engn I-00185 Rome Italy

In the application of computer-vision-based displacement measurement, an optical target is usually required to prove the reference. If the optical target cannot be attached to the measuring objective, edge detection and template matching are the most common approaches in target-less photogrammetry. However, their performance significantly relies on parameter settings. This becomes problematic in dynamic scenes where complicated background texture exists and varies over time. We propose virtual point tracking for real-time target-less dynamic displacement measurement, incorporating deep learning techniques and domain knowledge to tackle this issue. Our approach consists of three steps: 1) automatic calibration for detection of region of interest;2) virtual point detection for each video frame using deep convolutional neural network;3) domain-knowledge based rule engine for point tracking in adjacent frames. The proposed approach can be executed on an edge computer in a real-time manner (i.e. over 30 frames per second). We demonstrate our approach for a railway application, where the lateral displacement of the wheel on the rail is measured during operation. The numerical experiments have been performed to evaluate our approach's performance and latency in a harsh railway environment with dynamic complex backgrounds. We make our code and data available at https://github. com/quickhdsdc/Point-Tracking-for-Displacement-Measurement-in-Railway-Applications.

关键词： Point tracking Computer vision Displacement measurement Photogrammetry deep learning Railway

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Analyzing lower half facial gestures for lip reading applications: Survey on vision techniques

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COMPUTER VISION AND image UNDERSTANDING 2023年第1期233卷

作者： Preethi, S. J. Krupa, B. Niranjana PES Univ Dept ECE Bangalore 560085 India

Lip reading has gained popularity due to the proliferation of emerging real-world applications. This article provides a comprehensive review of benchmark datasets available for lip-reading applications and pioneering works that analyze lower facial cues for lip-reading applications. A comprehensive review of lip reading applications is broadly classified into five distinct applications: Lip Reading Biometrics (LRB), Audio Visual Speech Recognition (AVSR), Silent Speech Recognition (SSR), Voice from Lips, and Lip HCI (Human-computer interaction). LRB entails extensive research in the fields of authentication and liveness detection. AVSR covers key findings that have contributed significantly to applications such as voice assistants, video-totext transcription, hearing aids, and pronunciation-correcting systems. SSR analyzes the efforts made for silent-video-to-text transcription and surveillance camera applications. The voice from lips section discusses applications such as voice for the voiceless and vision-infused speech inpainting. In lip HCI, LR-HCI for smartphones, smart TVs, computers, robots, and musical instruments is reviewed in detail. Comprehensive coverage is given to cutting-edge techniques in computer vision, signal processing, machine learning, and deep learning. The advancements that aid the system in learning to lip-read and authenticate lip gestures, generate text transcription, synthesize voice based on lip movements, and control systems via lip movements (lip HCI) are covered. The work concludes by highlighting the limitations of existing frameworks, the road maps of each application illustrating the evolution of techniques employed over time, and future research avenues in lip-reading applications.

关键词： Lip reading Audio visual speech recognition Silent speech recognition Voice from lips Lip HCI Machine learning deep learning

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Low Latency real-time Seizure Detection Using Transfer deep learning

Low Latency Real-Time Seizure Detection Using Transfer Deep ...

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2021 IEEE Signal processing in Medicine and Biology Symposium, SPMB 2021

作者： Khalkhali, V. Shawki, N. Shah, V. Golmohammadi, M. Obeid, I. Picone, J. Temple University Neural Engineering Data Consortium PhiladelphiaPA United States Internet Brands El SegundoCA United States

ISBN: (纸本)9781665428972

Scalp electroencephalogram (EEG) signals inherently have a low signal-To-noise ratio due to the way the signal is electrically transduced. Temporal and spatial information must be exploited to achieve accurate detection of seizure events. Most popular approaches to seizure detection using deep learning do not jointly model this information or require multiple passes over the signal, which makes the systems inherently non-causal. In this paper, we exploit both simultaneously by converting the multichannel signal to a grayscale image and using transfer learning to achieve high performance. The proposed system is trained end-To-end with only very simple pre-and postprocessing operations which are computationally lightweight and have low latency, making them conducive to clinical applications that require real-time processing. We have achieved a performance of 42.05% sensitivity with 5.78 false alarm per 24 hours on the development dataset of v1.5.2 of the Temple University Hospital Seizure Detection Corpus. On a single core CPU operating at 1.7 GHz, the system runs faster than real-time (0.58 xRT), uses 16 Gbytes of memory, and has a latency of 300 msec. © 2021 IEEE.

关键词： Electroencephalography

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Fire Segmentation Using a SqueezeSegv2 27

Fire Segmentation Using a SqueezeSegv2

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Conference on image and Signal processing for Remote Sensing XXVII

作者： Harkat, H. Nascimento, J. Bernardino, A. Inst Super Tecn Inst Telecomunicacoes Av Rovisco Pais 1 P-1049001 Lisbon Portugal Univ Sidi Mohamed Ben Abdellah Fac Sci & Technol Route ImouzzerBP 2626 Fes 30000 Morocco IPL Inst Super Engn Lisboa Lisbon Portugal ISR Inst Sistemas & Robot Av Rovisco Pais 1 P-1049001 Lisbon Portugal

ISBN: (数字)9781510645691

ISBN: (纸本)9781510645691;9781510645684

In the last decade, the limitation of the propagation of Wildfire had become a higher necessity. In fact, it is important to optimize the resources used for dislocation to verify the probabilistic signaled fire zones. Hence, using sophisticated and low-cost techniques to sense the previously mentioned zones is highly demanded. Models with high computational necessity are not interesting for real time application. More simple models are requested, to fulfill the desired tasks with an admitted response time. Squeezesegv2 is a model applied initially for LiDAR (Light Detection And Ranging) Point Cloud data segmentation, which gives a high IoU value compared with other state of art architectures. The model was experimented in this paper, it is robust against dropout noise. Experiments were run over RGB pictures of Corsican public French dataset with 1135 RGB images. It is common that highly unbalanced datasets, which is our case, induce high precision low sensitivity. Therefore, several validation measures criterions were adopted to access the performance. In fact, the capability of the model was tested with four different metrics: Accuracy, mean Intersection over Union (IoU), Mean Boundary F1 (BF) Score, and Mean Dice coefficient. The experimental results demonstrate that the trained model, over the Corsican French dataset, with five-fold cross validation procedure can accurately detect the fire flame. The results were collected for different loss function types: Focal loss, Dice and Tversky loss. In general, the given results are very encouraging for further study using deep learning approaches.

关键词： Squeezesegv2 Fire detection Rgb pictures loss function deep learning

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Convolution neural network joint with mixture of extreme learning machines for feature extraction and classification of accident images

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JOURNAL OF real-time image processing 2020年第4期17卷 1051-1066页

作者： Pashaei, Ali Ghatee, Mehdi Sajedi, Hedieh Amirkabir Univ Technol Dept Comp Sci Tehran Iran Univ Tehran Coll Sci Sch Math Stat & Comp Sci Tehran Iran

This paper considers the accident images and develops a deep learning method for feature extraction together with a mixture of experts for classification. For the first task, the outputs of the last max-pooling layer of a Convolution Neural Network (CNN) are used to extract the hidden features automatically. For the second task, a mixture of advanced variations of Extreme learning Machine (ELM) including basic ELM, constraint ELM (CELM), On-Line Sequential ELM (OSELM) and Kernel ELM (KELM), is developed. This ensemble classifier combines the advantages of different ELMs using a gating network and its accuracy is very high while the processing time is close to real-time. To show the efficiency, the different combinations of the traditional feature extraction and feature selection methods and the various classifiers are examined on two kinds of benchmarks including accident images' data set and some general data sets. It is shown that the proposed system detects the accidents with 99.31% precision, recall and F-measure. Besides, the precisions of accident-severity classification and involved-vehicle classification are 90.27% and 92.73%, respectively. This system is suitable for on-line processing on the accident images that will be captured by Unmanned Aerial Vehicles (UAV) or other surveillance systems.

关键词： Feature extraction Accident images' classification Convolutional neural networks Mixture of ELM Ensemble learning

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Supervised deep semantics-preserving hashing for real-time pulmonary nodule image retrieval

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JOURNAL OF real-time image processing 2020年第6期17卷 1857-1868页

作者： Qi, Yongjun Gu, Junhua Zhang, Yajuan Wu, Gengshen Wang, Feng Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equip Tianjin Peoples R China Hebei Univ Technol Lab Electromagnet Field & Elect Apparat Reliabil Tianjin Peoples R China Informat Technol Ctr North China Inst Aerosp Engn Langfang Peoples R China Hebei Univ Technol Sch Artificial Intelligence Tianjin Peoples R China Univ Lancaster Sch Comp & Commun Lancaster England Hebei Univ Technol Hebei Prov Key Lab Big Data Calculat Tianjin Peoples R China

Hashing-based medical image retrieval has drawn extensive attention recently, which aims at providing effective aided diagnosis for medical personnel. In the paper, a novel deep hashing framework is proposed in the medical image retrieval, where the processes of deep feature extraction, binary code learning, and deep hash function learning are jointly carried out in supervised fashion. Particularly, the discrete constrained objective function in the hash code learning is optimized iteratively, where the binary code can be directly solved with no need for relaxation. In the meantime, the semantic similarity is maintained by fully exploring supervision information during the discrete optimization, where the neighborhood structure of training data is preserved by applying a graph regularization term. Additionally, to gain the fine-grained ranking of the returned medical images sharing the same Hamming distance, a novel image re-ranking scheme is proposed to refine the similarity measurement by jointly considering Euclidean distance between the real-valued feature descriptors and their category information between those images. Extensive experiments on the pulmonary nodule image dataset demonstrate that the proposed method can achieve better retrieval performance over the state of the arts.

关键词： deep learning Semantics-preserving hashing Pulmonary nodule real-time image retrieval

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deep learning Based real-time Face Tracking System in Multi-Camera

SSRN

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SSRN 2022年

作者： Ozdemir, Mehmet F. Hanbay, Davut Dept. of Computer Engineering Inonu University Malatya Turkey

Face detection and tracking have become popular in recent years. It has critical importance in security, defense, and robotic uses encountered in daily life. For this purpose, many decision support systems or expert systems using artificial intelligence and machine learning have been developed. Thanks to the developments in the field of deep learning and hardware many effective and reliable face track- ing systems realized. But there are still very few real-time scalable end-to-end systems. Also, the realization of this system on multiple cameras is a real challenge, too. In this study, a real-time deep learning-based face tracking system working on multiple cameras was developed. In the realized sys- tem, SCRFD model is used for face recognition, ArcFace model is used for face recognition, and an updated deepSORT algorithm is used for more stable face tracking. In addition, Apache Kafka stream processing system and *** bidirectional communication library were used to process multicam- era data in real-time and scalable. In the proposed system, when an image is entered into the system, it can be displayed on the web page after approximately 127 ms. © 2022, The Authors. All rights reserved.

关键词： deep learning

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Robust Adversarial Defence: Use of Auto-inpainting 20th

Robust Adversarial Defence: Use of Auto-inpainting

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20th International Conference on Computer Analysis of images and Patterns (CAIP)

作者： Sharma, Shivam Joshi, Rohan Bhilare, Shruti Joshi, Manjunath V. Dhirubhai Ambani Inst Informat & Commun Technol Gandhinagar 382007 Gujarat India

ISBN: (纸本)9783031442360;9783031442377

Adversarial patch attacks have become a primary concern in recent years as they pose a significant threat to the security and reliability of deep neural networks. Modifying benign images by introducing adversarial patches comprising localized adversarial pixels alters the salient features of the image resulting in misclassification. The novelty of our approach is in the use of image inpainting technique as an adversarial defence for rectifying the patch region. Adversarial patch is automatically localized using Fast Score Class Activation Map and superseded by inpainting using Fast Marching Method which efficiently propagates pixel information from the surrounding areas into the patch region. This approach ensures original image's structural integrity while simultaneously inpainting the adversarial pixels. Moreover, at the time of the attack it is not expected to have prior knowledge about the patch. Therefore, we propose our novel adversarial defence technique in a black-box setting assuming no knowledge about the patch location, shape or its size. Furthermore, we do not rely on re-training our victim model on adversarial examples, indicating its potential usefulness for real-world applications. Our experimental results show that the proposed approach achieves accuracy up to 76.37% on imageNet100 despite the adversarial patch attack amounting to a considerable improvement of 76.28% points. Moreover, on benign images our approach gives decent accuracy of 81.11% thereby suggesting that our defence pipeline is applicable irrespective of whether the input image is adversarial or clean.

关键词： Adversarial Machine learning Adversarial Defence Inpainting

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SafeARUnity: real-time image processing to Enhance Privacy Protection in LBARGs 14th

SafeARUnity: Real-Time Image Processing to Enhance Privacy ...

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14th International Conference on Videogame Sciences and Arts, VJ 2024

作者： Ribeiro, Tiago Marto, Anabela Gonçalves, Alexandrino Santos, Leonel Rabadão, Carlos de C. Costa, Rogério Luís CIIC ESTG Polytechnic University of Leiria Leiria Portugal

ISBN: (纸本)9783031817120

Augmented reality applications overlay our physical world with digital components in an interactive 3D space. These applications generally capture information about the physical world around the user through cameras and sensors, which can identify user movements and interactions with objects in the real world. In recent years, Location-Based Augmented reality Games (LBARGs) have been used in several contexts, such as entertainment, tourism, and education. However, by capturing information about the environment, AR applications can lead to failures in maintaining user and bystander privacy. This paper addresses the identification and protection of sensitive data in LBARGs. We introduce LootAR, a location-based mobile AR game, and the SafeARUnity library, a real-time image processing middleware that acts as a layer between the AR application and the device’s camera, identifying and sanitizing sensitive data prior to rendering. Implementation aspects are discussed, involving Unity Sentis, a toolkit for running machine learning models in Unity, and YOLO, a fast single-stage object detector optimized for real-time applications. We also demonstrate the integration of SafeARUnity in mobile games, using LootAR as a case study. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Middleware

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