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检索条件"任意字段=Proceedings of the 2023 12th International Conference on Computing and Pattern Recognition"

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Image Segmentation Algorithm in Complex Environment Based on Improved SOLOV2 12

Image Segmentation Algorithm in Complex Environment Based on...

引用

12th international conference of Information and Communication Technology, ICTech 2023

作者： Zhou, RenJian Zheng, LiaoMo Ren, ChuLan Wang, ShiYu Shenyang University of Chemical Technology Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Shenyang Casnc Technology Co. Ltd. Shenyang China

ISBN: (纸本)9798350332919

Previous industrial robots combined with vision systems in target grasping tasks have high requirements for the task environment. Once the vision system is confronted with a target object in a complex environment such as stacking, tilting and occlusion, it will be difficult to provide the robot control system with the correct target pose information due to the lack of target feature information, which is not conducive to the dynamic configuration capability of the production system. this study proposes an industrial target image segmentation algorithm based on improved SOLOv2 to solve the problem of low accuracy of industrial target image segmentation caused by small targets as well as occlusion. Building upon the SOLOv2 model, the optimization of fusing multi-layer features within the mask feature branch is deaigned to integrate feature information from the deepest to the shallowest layers, in a layer-by-layer manner;the Mish activation function is selected in the upsampling process to enhance the model's ability to extract small target features in images while improving the generalization ability and convergence speed of the model;to mitigate the impact of occlusion on image segmentation, the weights of the loss function are adaptively adjusted based on the degree of occlusion. this allows for a reduction in the negative influence of occlusion on image segmentation, as the loss function is optimezed to account for varying degrees of occlusion. © 2023 IEEE.

关键词： Image segmentation

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Hybrid CNN-LSTM Model for Automated Violence Detection and Classification in Surveillance Systems 12

Hybrid CNN-LSTM Model for Automated Violence Detection and C...

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12th international conference on System Modeling and Advancement in Research Trends, SMART 2023

作者： Tiwari, Raj Gaurang Maheshwari, Himani Agarwal, Ambuj Kumar Jain, Vishal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computing Graphic Era Hill University Uttrakhand Dehradun India School of Engineering and Technology Sharda University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350369861

the proliferation of digital images in recent years has prompted the development of automated algorithms that can identify violent and non-violent material in a variety of settings. this study introduces a unique method for categorising violent and nonviolent pictures using a combined Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model. Careful data collection from a publicly accessible source yielded a dataset of 11,043 original 416x416 photos. the photos were downsampled to 256x256 as part of the preprocessing phase of the suggested technique for more effective model training. the suggested model was compared against both more conventional Machine Learning methods and state-of-the-art Deep Learning systems to determine how well each performed. the findings reported here are for a 30-epoch value and a learning rate of 0.01. the experiment explored several hyperparameters. An outstanding 98.63 % accuracy was achieved by the suggested hybrid model, demonstrating its better performance. this proves the usefulness and promise of using the hybrid CNN-LSTM model for automated image classification, which in turn improves detection and classification skills for violent and nonviolent images. © 2023 IEEE.

关键词： Image classification

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Matching images by comparing their gradient fields

Matching images by comparing their gradient fields

引用

international conference on pattern recognition

作者： D. Scharstein Department of Computer Science Cornell University Ithaca NY USA

We present a simple yet powerful method to perform point-to-point matching between two images. the method uses an evidence measure, whose value for a given displacement reflects both the similarity between two locations and the confidence in a correct match. the measure is based on the gradient fields of the images, and can be computed quickly and in parallel. Accumulating the evidence measure for different displacements allows (1) stable computation, of correspondences without smoothing across motion boundaries, and (2) detection of dominant motions. the method works well both on highly textured images and on images containing regions of uniform intensities, and can be used for a variety of applications, including stereo, motion, and object tracking.

关键词： Displacement measurement Displacement control Motion measurement Smoothing methods Motion detection Stereo vision Area measurement Computer science Concurrent computing Application software

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Audio-Visual Emotion Challenge 2012: A Simple Approach 12

Audio-Visual Emotion Challenge 2012: A Simple Approach

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14th ACM international conference on Multimodal Interaction (ICMI)

作者： van der Maaten, Laurens Delft Univ Technol Pattern Recognit & Bioinformat Grp NL-2628 CD Delft Netherlands

ISBN: (纸本)9781450314671

the paper presents a small empirical study into emotion and affect recognition based on auditory and visual features, which was performed in the context of the Audio-Visual Emotion Challenge (AVEC) 2012. the goal of this competition is to predict continuous-valued affect ratings based on the provided auditory and visual features, e.g., local binary pattern (LBP) features extracted from aligned face images, and spectral audio features. Empirically, we found that there are only very weak (linear) relations between the features and the continuous-valued ratings: our best linear regressors employ the off set-feature to exploit the fact that the ratings have a dominant direction (more increasing than decreasing). Much to our surprise, only exploitation of this bias already leads to results that improve over the baseline system presented in [10]. the best performance we obtained on the AVEC 2012 test set (averaged over the test set and over four affective dimensions) is a correlation between predicted and ground-truth ratings of 0.2255 when making continuous predictions, and 0.1921 when making word-level predictions.

关键词： Social signal processing Affective computing

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Attention-LSTM-Based Prediction Model of PM2.5 Hourly Concentration 12

Attention-LSTM-Based Prediction Model of PM2.5 Hourly Concen...

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12th international conference of Information and Communication Technology, ICTech 2023

作者： Yang, Liu Zhang, Di Li, JianXi Kang, LiXing Zhao, SiTong Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang110168 China University of Chinese Academy of Sciences Beijing100049 China Liaoning Ecological and Environmental Monitoring Center Shenyang110161 China Shenyang No.22 Middle School Shenyang110075 China

ISBN: (纸本)9798350332919

As an important indicator to measure the concentration of air pollutants, the monitoring and prediction of PM2.5 concentration can effectively protect the atmospheric environment and further reduce the harm caused by air pollution. With the large-scale establishment of automatic air quality monitoring stations, the air quality prediction model built by traditional machine learning can no longer meet the current needs. In this paper, an attention-LSTM model based on Long Short Term Memory network (LSTM) and multi-head attention mechanism is proposed to predict PM2.5 hourly concentration, and the data from a monitoring station in Shenyang are trained and tested. this model takes into account the influence of PM2.5 concentration on other air quality data, uses the two-layer LSTM network to capture the complex time series correlation features between air quality data, and uses the multi-head attention mechanism to extract the time series correlation features of different subspaces, which can obtain more complete and effective characteristic information. Experiments prove that compared with mainstream air quality prediction methods such as ARIMA, AutoEncoder and LSTM, the Attention-LSTM model proposed in this paper effectively improves the prediction accuracy and can accurately predict PM2.5 concentration. © 2023 IEEE.

关键词： Long short-term memory

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Leveraging Deep Learning Models and Ethereum Smart Contracts to Secure EHR in HL7 Environment 12

Leveraging Deep Learning Models and Ethereum Smart Contracts...

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12th international conference on System Modeling and Advancement in Research Trends, SMART 2023

作者： Ginavanee, A. Prasanna, S. School of Computing Sciences Vistas Chennai India Agurchand Manmull Jain College Department of Computer Applications Chennai India Vistas Department of Computer Applications Chennai India

ISBN: (纸本)9798350369861

the secure and efficient sharing of healthcare data is crucial in the rapidly evolving healthcare landscape. this paper addresses the critical need for secure and efficient healthcare data sharing in today's rapidly evolving healthcare landscape. While sharing health information can significantly improve healthcare quality and treatment coordination, challenges like security, privacy concerns, data consistency, and timely access across healthcare facilities remain. To overcome these obstacles, our proposed solution harnesses Ethereum Blockchain technology, Artificial Intelligence (AI), and the Health Level 7 standard (HL7). this integrated approach redefines data modeling, creating a flexible and scalable system that prioritizes medical data and service privacy and security. the fusion of AI and Ethereum Blockchain technology effectively tackles Health Information Exchange challenges. Additionally, our BERT-CNN classifier, achieving an impressive 94.2% accuracy, excels in classifying medical data, outperformed MLP, SVM, and Random Forest classifiers. We employ modern cryptographic methods like Fully Homomorphic Encryption over the Torus (TFHE) to ensure secure computation and protect sensitive medical data. these strategies promise more effective and secure medical data sharing, leading to improved patient care, advances in medical research, and increased confidence across the healthcare ecosystem. © 2023 IEEE.

关键词： Blockchain

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COVID-19 Trend Prediction Using CLS-Net Hybrid Model 12

COVID-19 Trend Prediction Using CLS-Net Hybrid Model

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12th international conference of Information and Communication Technology, ICTech 2023

作者： Chen, YaShu Liu, Jun Yu, Bo College of Computer Science and Technology Shenyang University of Chemical Technology Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9798350332919

the break out of Corona Virus Disease 2019 has caused great danger and effect for people's health and life. therefore, predicting the trend of the epidemic is crucial for politicians and medical conductors go for make correct policy ahead of time. the SEIR model is now a more mature epidemic prediction model, but the dynamic prediction of the outbreak cannot be achieved because in this model, the infection rate of virus can be calculated mathematically. Based on this problem, this thesis proposes a CLS-Net model to predict the trend of global epidemic of Corona Virus Disease 2019 under the influence of human mobility. the model extracts features from a large amount of population migration data by CNN model, and then predicts the transmission rate of New Coronavirus in real time with the excellent time-series learning ability of LSTM. Finally, the live updated virus infection rate apply into the SEIR model to realize the dynamic prediction of epidemic development trend. To validate the approach proposed in paper, the statistical data of neocrown pneumonia from various countries around the world were collected for experiments. Take Mean Absolute Error (MAE), R2(R-Squared) and Root Mean Square Error (RMSE) as the evaluation indexes of the model in this paper. the experimental results show that the model proposed in this paper has better performance compared with the classical infectious disease prediction model. © 2023 IEEE.

关键词： Forecasting

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A new approach to qualitative stereo

A new approach to qualitative stereo

引用

international conference on pattern recognition

作者： Y. Hel-Or S. Edelman Department of Applied Mathematics and Computer Science Weizmann Institute of Science Rehovot Israel

Nonmetric multidimensional scaling (MDS) is a family of algorithms that allow one to derive a quantitative representation of data from a set of qualitative measurements which must satisfy certain simple constraints. As a tool for vision, MDS combines the advantages of both qualitative and classical approaches, by relying, on the one hand, on an ordinal-scale input representation, and by supporting, on the other hand, the extraction of metric information. the present paper illustrates an application of MDS to the recovery of depth from the rank order of binocular disparity differences for a set of points. Our results indicate that multidimensional scaling constitutes a promising approach to the integration of biological and computational insights into the problem of depth perception.

关键词： Multidimensional systems Computer vision Stereo vision Mathematics Computer science Data mining Biology computing Noise robustness Cameras Difference equations

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Research on Industrial Non-Destructive Testing Technology Based on Improved YOLOv5s 12

Research on Industrial Non-Destructive Testing Technology Ba...

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12th international conference of Information and Communication Technology, ICTech 2023

作者： Yu, Bo Chen, Wenhao Wang, Wei Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China School of Computer Sciences and Engineering Northeastern University Shenyang China

ISBN: (纸本)9798350332919

Non-destructive testing technology plays a pivotal role in the manufacturing process of modern industrial products, and is of incredible importance for moving forward the item quality of workpieces. Currently, the commonly used non-destructive testing technology has disadvantages such as complicated operation, high testing cost, and limited scope of application. this paper suggests an upgraded multi-scale target detection algorithm based on YOLOv5s in light of the aforementioned issues. Firstly, an improved channel attention mechanism module ECA-Net is added into the backbone network. Second, the maximum pooling operation in the SPP module is replaced with atrous convolution in this paper. Finally, the channel weight information is added to the concat operation of the PAN structure, which improves the sensitivity of the network to the channel information of the feature map. this paper uses the NEU-DET steel surface defect data set to do a series of experiments to verify the detection effect of the model. According to the experimental results, the mAP of the algorithm proposed in this paper is 73.47%, which is 7.18% higher than that of YOLOv5s, and the FPS is 68.5 frames/s, which satisfies the needs of industrial production environments. © 2023 IEEE.

关键词： Convolution

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A maximum entropy approach to pairwise data clustering

A maximum entropy approach to pairwise data clustering

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international conference on pattern recognition

作者： J.M. Buhmann T. Hofmann Institut für Informatik III Universität Bonn Bonn Germany

Partitioning a set of data points which are characterized by their mutual dissimilarities instead of an explicit coordinate representation is a difficult, NP-hard combinatorial optimization problem. the authors formulate this optimization problem of a pairwise clustering cost function in the maximum entropy framework using a variational principle to derive corresponding data partitionings in a d-dimensional Euclidian space. this approximation solves the embedding problem and the grouping of these data into clusters simultaneously and in a selfconsistent fashion.

关键词： Entropy Extraterrestrial measurements Embedded computing Symmetric matrices Cost function Data analysis Psychology Noise reduction Noise measurement Data visualization

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