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A Survey on Facial Emotion Recognition for the Elderly 2nd

A Survey on Facial Emotion Recognition for the Elderly

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3rd international conference on Digital Technologies and Applications

作者： Labzour, Nouhaila El Flcihi, Sanaa Benaissa, Soukayna Zennayi, Yahya Bourja, Omar Mohammed V Univ Rabat ADMIR Lab Rabat IT Ctr ENSIAS IRDA Grp Rabat Morocco MAScIR Embedded Syst & Artificial Intelligence Dept Rabat Morocco

ISBN: (纸本)9783031298561;9783031298578

The elderly have a sensitive period of life in terms of physical and mental health and require close assistance or a caregiver. Medical assistance has the ability to recognize the emotional states of older adults through facial expressions and take care of them in real time. This paper provides a comprehensive Facial Emotion Recognition (FER) review, especially for the elderly. Several studies have been conducted on the facial emotion recognition of young and middle-aged adults. Very few studies have focused on automatic emotion recognition for the elderly. Aging comes with a decline in the ability to recognize emotions and impacts emotion perception in humans. Furthermore, older people are suffering from cognitive impairment worldwide, which leads to abnormal emotional patterns. This paper is a literature review of FER techniques in computer vision;FER approaches, and FER databases, and discusses the main challenge of facial expression recognition across age and lifespan.

关键词： Facial Emotion Recognition image processing Elderly Human-Robot-Interaction Review

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Handling Data Heterogeneity in Federated Learning with Global Data Distribution

Handling Data Heterogeneity in Federated Learning with Globa...

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3rd international conference on image processing and vision Engineering, IMPROVE 2023

作者： Nagaraju, C. Sen, Mrinmay Mohan, C. Krishna Deparment of Computer Science IIT Hyderabad India Department of Artificial Intelligence IIT Hyderabad India

ISBN: (纸本)9789897586422

Federated learning, a different direction of distributed optimization, is very much important when there are restrictions of data sharing due to privacy and communication overhead. In federated learning, instead of sharing raw data, information from different sources are gathered in terms of model parameters or gradients of local loss functions and these information is fused in such way that we can find the optima of average of all the local loss functions (global objective). Exiting analyses on federated learning show that federated optimization gets slow convergence when data distribution across all the clients or sources are not homogeneous. Heterogeneous data distribution in federated learning causes objective inconsistency which means global model converges to a another stationary point which is not same as the optima of the global objective which results in poor performance of the global model. In this paper, we propose a federated Learning(FL) algorithm in heterogeneous data distribution. To handle data heterogeneity during collaborative training, we generate data in local clients with the help of a globally trained Gaussian Mixture Models(GMM). We update each local model with the help of both original and generated local data and then perform the similar operations of the most popular algorithm called FedAvg. We compare our proposed method with exiting FedAvg and FedProx algorithms with CIFAR10 and FashionMNIST Non-IID data. Our experimental results show that our proposed method performs better than the exiting FedAvg and FedProx algorithm in terms of training loss, test loss and test accuracy in heterogeneous system. © 2023 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Federated learning

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FC-NT: A crime prediction algorithm incorporating formal context and neural networks 3

FC-NT: A crime prediction algorithm incorporating formal con...

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3rd international conference on Advanced Algorithms and Signal image processing, AASIP 2023

作者： Zao, Feifei Lv, Lingling Ding, Feng Jin, Shixian Guo, Xiaoding Henan Province Engineering Research Center of Spatial Information Processing Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan University Zhengzhou450046 China School of Electrical Engineering North China University of Water Resources and Electric Power Zhengzhou450046 China School of Internet of Things Engineering Jiangnan University Wuxi214122 China Henan Provincial Educational Resources Guarantee Center Zhengzhou450003 China

ISBN: (纸本)9781510668522

The task of crime prediction is to predict the charges of a case based on the given description of the case, which has become a research hot spot. Most of the existing methods use neural networks and machine learning algorithms to predict. The prediction results have high accuracy, but the prediction results are often not well interpretable. In addition, some early traditional methods can explain the prediction results, but their accuracy is often very low. Formal concept analysis is an effective data extraction tool. Therefore, this paper combines formal concept analysis with neural network to improve the accuracy of the algorithm. The experimental results show that our method has higher prediction results and can better explain the prediction results than the original neural network model. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Forecasting

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Single target tracking method in low illumination video based on computer vision

Single target tracking method in low illumination video base...

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Communications Technology and computer Science (ACCTCS), Asia-Pacific conference on

作者： Yanlin Yin Rui Han Yingjie Song Shiguang Sun School of Electrical and Information Engineering Heilongjiang University of Technology Jixi Heilongjiang China School of International Education Heilongjiang University of Technology Jixi Heilongjiang China

Aiming at the problem that the single target tracking effect of low illumination video is poor, a low illumination video single target tracking method based on computer vision is proposed. The computer vision processing algorithm is used to enhance the video image, and the sharpening components in the images at all levels are controlled by parameters to suppress the high-frequency noise of low illumination video. In order to better expand the dynamic range of the overall gray level of the enhanced image, this paper adopts an improved computer vision method to adjust the contrast of the video image, so as to achieve the requirements of low illumination video but target tracking. Finally, from the simulation results, it can be seen that the low illumination video single target tracking method based on computer vision has better integrity and accuracy, and the short-term occlusion of moving targets can achieve a better tracking effect.

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Lung cancer detection using convolutional neural network and transfer learning

Lung cancer detection using convolutional neural network and...

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Advancement in Computation & computer Technologies (InCACCT), international conference on

作者： Rahul Sharma Akanksha Dubey Manoj Diwakar Shagun Gupta Upendra Pratap Singh Monika Kumari Nath D. Y. Patil International University Pune India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Lung cancer has emerged as a significant cause for concern among individuals worldwide. Consequently, several nations provide financial resources and extend invitations to researchers and medical professionals to collaborate in addressing this issue. Lung cancer is a significant contributor to mortality subsequent to heart illness. The timely identification of lung cancer in its first stages has the potential to enhance patient survival rates. The use of computer vision is of utmost importance in the timely identification of lung cancer and the implementation of early therapeutic interventions, hence enhancing the likelihood of patient survival. The proposed deep learning model incorporates Convolutional Neural Network (CNN) and transfer learning techniques, along with other image processing methods like image compression and picture enhancement. These approaches are used to analyze medical images and make correct predictions. Our study demonstrates a CNN model accuracy of 91.29% and a transfer learning accuracy of 95.9%.

关键词： Deep learning computer vision Accuracy image coding Computational modeling Transfer learning Lung cancer Feature extraction Convolutional neural networks Biomedical imaging

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Leveraging Swin Transformer for Local-to-Global Weakly Supervised Semantic Segmentation

Leveraging Swin Transformer for Local-to-Global Weakly Super...

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Iranian Machine vision and image processing (MVIP)

作者： Rozhan Ahmadi Shohreh Kasaei Department of Computer Engineering Sharif University of Technology Tehran Iran

In recent years, weakly supervised semantic segmentation using image-level labels as supervision has received significant attention in the field of computer vision. Most existing methods have addressed the challenges arising from the lack of spatial information in these labels by focusing on facilitating supervised learning through the generation of pseudolabels from class activation maps (CAMs). Due to the localized pattern detection of Convolutional Neural Networks (CNNs), CAMs often emphasize only the most discriminative parts of an object, making it challenging to accurately distinguish foreground objects from each other and the background. Recent studies have shown that vision Transformer (ViT) features, due to their global view, are more effective in capturing the scene layout than CNNs. However, the use of hierarchical ViTs has not been extensively explored in this field. This work explores the use of Swin Transformer by proposing "SWTformer" to enhance the accuracy of the initial seed CAMs by bringing local and global views together. SWTformer-V1 generates class probabilities and CAMs using only the patch tokens as features. SWTformer-V2 incorporates a multi-scale feature fusion mechanism to extract additional information and utilizes a background-aware mechanism to generate more accurate localization maps with improved cross-object discrimination. Based on experiments on the PascalVOC 2012 dataset, SWTformer-V1 achieves a 0.98% mAP higher localization accuracy, outperforming state-of-the-art models. It also yields comparable performance by 0.82% mIoU on average higher than other methods in generating initial localization maps, depending only on the classification network. SWTformer-V2 further improves the accuracy of the generated seed CAMs by 5.32% mIoU, further proving the effectiveness of the local-to-global view provided by the Swin transformer. Code available at: https://***/RozhanAhmadi/SWTformer

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Detection of Overall Fruit Maturity of Local Fruits Using Support Vector Machine through image processing 12

Detection of Overall Fruit Maturity of Local Fruits Using Su...

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12th international conference on Software Technology and Engineering (ICSTE)

作者： Tomas, Mary Christine Celino, Jose Pedro Luis Escalambre, Isaiah Emmanuelle Secreto, Bernard Paolo Mapua Univ Sch Informat Technol Makati Philippines

ISBN: (纸本)9781665463553

It is crucial for a farmer to determine the ripening of the fruit since the harvested crop can be sold at a higher price if it is ripe. In line with this, fruits are considered one of the country's most exported goods (3rd as of 2020). The researchers proposed a system using Support Vector Machine (SVM) to determine the fruit maturity of Banana, Mango, and Calamansi. Said fruits are classified into three categories for fruit maturity, namely: unripe, ripe, and overripe. 140 fruits were gathered and used starting when it was unripe until it was overripe. This was done in a 12-hour interval, every 9 am and 9 pm every day for two weeks. The data gathered consists of 1729 images of bananas (Cavendish), 711 images of mangos, and 589 images of calamansi. The model was written in Python, and the integrated development environment (IDE) used was a jupyter notebook. HSV Conversion, Hyperparameter tuning, and Feature Extraction were used to increase the accuracy of the model. Three models were used, namely, Model 1, the model after fitting the dataset with background and Hyperparameter Tuning, Model 2, the model after fitting the dataset with removed background and features identified, and Model 3, the model after fitting the dataset with removed background, identified features and underwent hyperparameter tuning.

关键词： computer vision support vector machine fruit maturity image processing machine learning hyperparameter tuning HSV conversion feature extraction

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Single-Photon Cameras image Reconstruction Using vision Transformer

Single-Photon Cameras Image Reconstruction Using Vision Tran...

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computer Communication and Artificial Intelligence (CCAI), international conference on

作者： Xingzheng Wang Beijing Institute of Technology Beijing China

Single-photon camera is a novel camera type that utilizes image sensor with photon-counting capability. Recently, the potential of such sensors to achieve high spatial resolutions (e.g., 10^9pixels/chip) and frame rates (e.g., 10^6frames/sec) is well-established. However, there is a significant difference in the output data between single-photon cameras and traditional CMOS sensor cameras. Therefore, conventional image reconstruction algorithms cannot be utilized. Vison Transformer has impressive performance on image processing tasks, and this paper will design a plug-and-play algorithm for reconstructing images from single-photon cameras and integrate image processing algorithms based on deep neural networks like ViT. The results demonstrate our methods can achieve improvement both in PSNR and SSIM.

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Research on Application of Sports Video image Analysis Based on Fuzzy Clustering Algorithm

Research on Application of Sports Video Image Analysis Based...

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Smart Generation Computing, Communication and Networking (SMART GENCON), international conference on

作者： Tianming Liu Shandong Polytechnic College Jining Shandong China

With the wide application of computer vision technology in sports field, how to analyze sports video efficiently and accurately has become an important research direction. Fuzzy clustering algorithm as a nonlinear data mining method can deal with uncertainty and noise provides a new idea for the analysis of sports video images. This paper mainly discusses the application of fuzzy clustering algorithm in sports video image processing, and analyzes its influence on sports video image

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Application Research of image processing Technology in Safe Driving Distance Measurement

Application Research of Image Processing Technology in Safe ...

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Data Science and computer Application (ICDSCA), IEEE international conference on

作者： Weitao Gao Tiehua Ma Changxin Chen Chenbin Wang Na Feng State Key Laboratory of Dynamic Testing Technology North University of China Taiyuan 030051 China School of Electrical and Control Engineering North University of China Taiyuan China

In this paper, a new vehicle distance detection technology based on image processing is proposed to solve the traditional ultrasonic vehicle distance detection errors, many blind areas and other problems. The binocular vision sensing system was used to collect real-time images in front of vehicles. The vehicle in front of the collected image is identified through template matching, and the parallax principle is used to calculate the vehicle distance after confirming the presence of the vehicle. Experimental results show that this method can detect the distance between different angles and different positions with high accuracy.

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