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2023 International Conference on machine vision, image processing and Imaging Technology, MVIPIT 2023

作者： Song, Xu Xiao, Yongbiao Li, Hui Wu, Xiao-Jun Sun, Jun Palade, Vasile Jiangnan University School of Artificial Intelligence and Computer Science Wuxi China Coventry University Faculty of Engineering Environment and Computing Conventry United Kingdom

ISBN: (纸本)9798350306545

The fusion of visible light and infrared images has garnered significant attention in the field of imaging due to its pivotal role in various applications, including surveillance, remote sensing, and medical imaging. Therefore, this paper introduces a novel fusion framework using Res2Net architecture, capturing features across diverse receptive fields and scales for effective extraction of global and local features. Our methodology is structured into three fundamental components: the first part involves the Res2Net-based encoder, followed by the second part, which encompasses the fusion layer, and finally, the third part, which comprises the decoder. The encoder based on Res2Net is utilized for extracting multi-scale features from the input image. Simultaneously, with a single image as input, we introduce a pioneering training strategy tailored for a Res2Net-based encoder. We further enhance the fusion process with a novel strategy based on the attention model, ensuring precise reconstruction by the decoder for the fused image. Experimental results unequivocally showcase our method's unparalleled fusion performance, surpassing existing techniques, as evidenced by rigorous subjective and objective evaluations. © 2023 IEEE.

关键词： image fusion

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A critical review on diagnosis of diabetic retinopathy using machine learning and deep learning

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MULTIMEDIA TOOLS AND applications 2022年第18期81卷 25613-25655页

作者： Das, Dolly Biswas, Saroj Kr Bandyopadhyay, Sivaji Natl Inst Technol Silchar Cachar Assam India

Diabetic Retinopathy (DR) is a health condition caused due to Diabetes Mellitus (DM). It causes vision problems and blindness due to disfigurement of human retina. According to statistics, 80% of diabetes patients battling from long diabetic period of 15 to 20 years, suffer from DR. Hence, it has become a dangerous threat to the health and life of people. To overcome DR, manual diagnosis of the disease is feasible but overwhelming and cumbersome at the same time and hence requires a revolutionary method. Thus, such a health condition necessitates primary recognition and diagnosis to prevent DR from developing into severe stages and prevent blindness. Innumerable machine Learning (ML) models are proposed by researchers across the globe, to achieve this purpose. Various feature extraction techniques are proposed for extraction of DR features for early detection. However, traditional ML models have shown either meagre generalization throughout feature extraction and classification for deploying smaller datasets or consumes more of training time causing inefficiency in prediction while using larger datasets. Hence Deep Learning (DL), a new domain of ML, is introduced. DL models can handle a smaller dataset with help of efficient data processing techniques. However, they generally incorporate larger datasets for their deep architectures to enhance performance in feature extraction and image classification. This paper gives a detailed review on DR, its features, causes, ML models, state-of-the-art DL models, challenges, comparisons and future directions, for early detection of DR.

关键词： Diabetic retinopathy image processing machine learning Retinal lesions Feature extraction Deep learning

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Comparison of machine Learning Methods for Satellite image Classification: A Case Study of Casablanca Using Landsat imagery and Google Earth Engine

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Journal of Environmental & Earth Sciences 2023年第2期5卷 118-134页

作者： Hafsa Ouchra Abdessamad Belangour Allae Erraissi Laboratory of Information Technology and Modeling LTIM Hassan II UniversityFaculty of Sciences Ben M’sikCasablanca20670Morocco Chouaib Doukkali University Polydisciplinary Faculty of Sidi BennourEl Jadida24000Morocco

Satellite image classification is crucial in various applications such as urban planning,environmental monitoring,and land use *** this study,the authors present a comparative analysis of different supervised and unsupervised learning methods for satellite image classification,focusing on a case study in Casablanca using Landsat 8 *** research aims to identify the most effective machine-learning approach for accurately classifying land cover in an urban *** methodology used consists of the pre-processing of Landsat imagery data from Casablanca city,the authors extract relevant features and partition them into training and test sets,and then use random forest(RF),SVM(support vector machine),classification,and regression tree(CART),gradient tree boost(GTB),decision tree(DT),and minimum distance(MD)*** a series of experiments,the authors evaluate the performance of each machine learning method in terms of accuracy,and Kappa *** work shows that random forest is the best-performing algorithm,with an accuracy of 95.42%and 0.94 Kappa *** authors discuss the factors of their performance,including data characteristics,accurate selection,and model influencing.

关键词： Supervised learning Unsupervised learning Satellite image classification machine learning Google Earth Engine

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A multi-feature-based intelligent redundancy elimination scheme for cloud-assisted health systems

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CAAI Transactions on Intelligence Technology 2024年第2期9卷 491-510页

作者： Ling Xiao Beiji Zou Xiaoyan Kui Chengzhang Zhu Wensheng Zhang Xuebing Yang Bob Zhang School of Computer Science and Engineering Central South UniversityChangshaChina Hunan Engineering Research Center of Machine Vision and Intelligent Medicine Central South UniversityChangshaChina The College of Literature and Journalism Central South UniversityChangshaChina Institute of Automation Chinese Academy of SciencesBeijingChina Department of Computer and Information Science University of MacaoMacaoChina

Redundancy elimination techniques are extensively investigated to reduce storage overheads for cloud-assisted health *** eliminates the redundancy of duplicate blocks by storing one physical instance referenced by multiple *** compression is usually regarded as a complementary technique to deduplication to further remove the redundancy of similar blocks,but our observations indicate that this is disobedient when data have sparse duplicate *** addition,there are many overlapped deltas in the resemblance detection process of post-deduplication delta compression,which hinders the efficiency of delta compression and the index phase of resemblance detection inquires abundant non-similar blocks,resulting in inefficient system ***,a multi-feature-based redundancy elimination scheme,called MFRE,is proposed to solve these *** similarity feature and temporal locality feature are excavated to assist redundancy elimination where the similarity feature well expresses the duplicate ***,similarity-based dynamic post-deduplication delta compression and temporal locality-based dynamic delta compression discover more similar base blocks to minimise overlapped deltas and improve compression ***,the clustering method based on block-relationship and the feature index strategy based on bloom filters reduce IO overheads and improve system *** demonstrate that the proposed method,compared to the state-of-the-art method,improves the compression ratio and system throughput by 9.68%and 50%,respectively.

关键词： big data cloud computing compression data compression medical applications performance evaluation

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OpenCV and its applications in Artificial Intelligent Systems 5

OpenCV and its Applications in Artificial Intelligent System...

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5th International Conference on Intelligent Computing, Communication, Networking and Services, ICCNS 2024

作者： Odeh, Ayman Odeh, Nada College Of Engineering Al Ain University Department of Software Engineering Al Ain United Arab Emirates College Of Information Technology UAEU Department of Computer Science and Software Engineering Al Ain United Arab Emirates

ISBN: (纸本)9798350354690

This paper explores the utilization of OpenCV (Open-Source Computer vision Library) in artificial intelligence (AI) systems, elucidating its pivotal role in advancing various applications across diverse domains. OpenCV, renowned for its comprehensive functionalities and real-time processing capabilities, has become instrumental in tasks such as object detection (OD), facial recognition (FR), and image processing (IP). However, despite its widespread adoption, there remains a need for a thorough examination of its integration within AI frameworks and the optimization of its functionalities. Through a comprehensive analysis, this study delineates the advantages, limitations, and potential areas for improvement of OpenCV in AI applications. Case studies exemplifying its efficacy in OD, FR, and autonomous systems further elucidate its practical implications. By delving into these aspects, this paper not only underscores the significance of OpenCV in AI but also provides valuable insights for researchers, developers, and practitioners aiming to leverage its capabilities in their endeavors. This study collectively underscores the significant role of OpenCV in enhancing the capabilities of AI systems. © 2024 IEEE.

关键词： Adversarial machine learning

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Comparative Performance Analysis of Edge-AI Devices in Deep Learning applications 19

Comparative Performance Analysis of Edge-AI Devices in Deep ...

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IEEE 19th Conference on Industrial Electronics and applications (ICIEA)

作者： Samsuri, Muhammad Hafiz Yuen, Shang Li Lau, Phooi Yee Wong, Chin Wee Kamarudin, Nur Afiqah Hussin, Zarina Talib, Muhammad Syukri Mohd Hon, Hock Woon MIMOS Berhad Adv Intelligence Lab Kuala Lumpur Malaysia

ISBN: (纸本)9798350360875;9798350360868

The fusion of edge computing and artificial intelligence, known as Edge AI, represents a paradigm shift that facilitates the direct execution of AI algorithms on edge devices. As these devices become increasingly powerful, their role in developing and deploying AI systems becomes more significant. By eliminating the need to transmit and analyze data at remote machines, Edge AI applications can significantly reduce latency and enhance efficiency by processing data closer to the source. In this study, we thoroughly investigate the performance of our object classification model deployed in a vision inspection system on four types of edge devices (Jetson AGX Orin, Jetson Orin Nano, NUC, and Raspberry Pi). Our object classification models are trained using proprietary industrial datasets provided by industry partners. These models, in FP32, are converted into lower precision processing, being INT8, to evaluate the accuracy variation between FP32 and INT8 precision, and inference speed for different edge devices. In our experiments, we identified that the average accuracy deviation for INT8 models is -2.78%, with some models exhibiting variations exceeding - 10.95%. Most devices have an average inference speed less than 100 ms per image (as requested by industrial partners), except the Raspberry Pi, which records more than 2 seconds of inferencing an image. Intel NUC consumes 107 W, which is averagely comparable with a server PC, while AGX Orin, Orin Nano, and Raspberry Pi consume less than 20 W of power. The outcomes of our evaluations offer valuable insights for selecting appropriate devices for specific scenarios. These detailed observations on the strengths and limitations of different edge devices can guide future research and advancements in Edge AI technology.

关键词： Edge AI machine Learning Visual Inspection Jetson AGX Orin Jetson Orin Nano INT8 Precision Intel NUC Raspberry Pi

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Detection and Segmentation of Grape Bunch by Integrating Channel Attention and Large Kernel Attention 2

Detection and Segmentation of Grape Bunch by Integrating Cha...

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2nd International Conference on image processing, Computer vision and machine Learning, ICICML 2023

作者： Huang, Zhitao Li, Guo Guangxi Normal University School of Computer Science and Engineering Guilin China

ISBN: (纸本)9798350331417

Detecting and segmenting fruits in an orchard environment is a vital technique in multiple applications of precision agriculture, such as automated harvesting and yield estimation. This study aims to improve the accuracy and robustness of detectors for detecting and segmenting fruits on trees by integrating detection and segmentation models based on channel and large kernel attention. The proposed Triplet-Large Kernel Attention (TLKA) module inherits the advantages of channel and large kernel attention. It was integrated with YOLOv7 to achieve real-time object detection and segmentation. Several experiments were conducted to verify the effectiveness of the proposed TLKA module. These included comparative attention mechanisms from small to large input image scales, comparative analysis of different attention mechanisms through Grad-CAM visualization, and test experiments with integrated comparisons on mid-term fruits (immature, intermediate, and mature) under three different light conditions (morning, noon, and afternoon). The proposed TLKA module achieved higher accuracy than comparative attention at different input scales. Finally, the proposed model was used to predict the yield of both grapes. The TLKA-YOLOv7 outperformed all other investigated models in terms of grape bunch detection and segmentation and obtained more competitive results in yield prediction. © 2023 IEEE.

关键词： attention mechanism component computer vision deep learning detection YOLO

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Deep Learning Based Gender Identification Using Ear images

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TRAITEMENT DU SIGNAL 2023年第4期40卷 1629-1639页

作者： Kilic, Safak Dogan, Yahya Kayseri Univ Dept Software Engn TR-38100 Kayseri Turkiye Siirt Univ Dept Comp Engn TR-56100 Siirt Turkiye

The classification of an individual as male or female is a significant issue with several practical implications. In recent years, automatic gender identification has garnered considerable interest because of its potential applications in e-commerce and the accumulation of demographic data. Recent observations indicate that models based on deep learning have attained remarkable success in a variety of problem domains. In this study, our aim is to establish an end-to-end model that capitalizes on the strengths of competing convolutional neural network (CNN) and vision transformer (ViT) models. To accomplish this, we propose a novel approach that combines the MobileNetV2 model, which is recognized for having fewer parameters than other CNN models, with the ViT model. Through rigorous evaluations, we have compared our proposed model with other recent studies using the accuracy metric. Our model attained state-of-the-art performance with a remarkable score of 96.66% on the EarVN1.0 dataset, yielding impressive results. In addition, we provide t-SNE results that demonstrate our model's superior learning representation. Notably, the results show a more effective disentanglement of classes.

关键词： deep learning gender identification ear images convolutional neural network (CNN) biometric identification image processing machine learning facial recognition

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Computer vision Based Hybrid Classroom Attention Monitoring

Computer Vision Based Hybrid Classroom Attention Monitoring

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2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Rawat, Saniya Rodrigues, Malivia Sheregar, Prateeksha Wagaskar, Kalpita Ajinkya Tripathy, Amiya Kumar Mumbai India

ISBN: (纸本)9798350382693

This research presents a novel computer vision-based attention monitoring system designed for both online and offline contexts. Leveraging advanced image processing and machine learning algorithms, the system analyzes human gaze patterns, eye movements, and facial expressions to accurately gauge attention levels. In online scenarios, the system employs real-time webcam-based gaze tracking and facial recognition to provide immediate insights into user engagement during activities like video conferencing and virtual meetings. For offline analysis, recorded video footage is retrospectively examined, facilitating applications in education, workplace productivity, and user experience assessments. Privacy considerations are addressed through the implementation of privacy-preserving techniques. Experimental results demonstrate the system's efficacy in monitoring attention dynamics across diverse settings, contributing to a deeper understanding of human attention in various domains. © 2024 IEEE.

关键词： Video conferencing

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Deep Learning Hybrid Technique for Generation of image Caption

Deep Learning Hybrid Technique for Generation of Image Capti...

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2024 International Conference on Signal processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Rakshith, N. Manoj Gowda, B.K. Preetham, N. Tejas, M. Baig, Mohammed Ifraz PES college of enginnering mandya Dept. of Information Science and Engineering Mandya India

ISBN: (纸本)9798331540685

image captioning is a fascinating and demanding work with applications in many different fields, including image retrieval, organizing and finding user-interested images, etc. It has enormous potential to replace the tedious process of creating captions for photos, and it works particularly well with massive amounts of picture data. Deep neural network-based techniques have recently shown significant success in the domains of language synthesis, machine translation, and computer vision. In this research, we present a paradigm based on encoder-decoders that may produce grammatically acceptable image captions. This model uses LSTM as the decoder and VGG16 Hybrid Places 1365 as the encoder. Model is evaluated using all standard metrics such as BLEU. Experimental results indicate that the proposed model obtained a BLEU-1 score 0.603774, BLEU-2 score 0.388514, BLUE-3 score 0.244706 on Flickr8k dataset. By comparison with the state-of-the-art techniques, the suggested strategy produced a noteworthy performance. We also present the outcomes of caption creation from real sample photographs, which supports the validity of the suggested method, in order to assess the model's effectiveness even further. © 2024 IEEE.

关键词： Decoding

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