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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems III"

共 14659 条记录，以下是841-850 订阅

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Deep Learning Techniques for image Recognition in IoT-Enabled Surveillance systems

Deep Learning Techniques for Image Recognition in IoT-Enable...

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2024 Asian conference on Intelligent Technologies, ACOIT 2024

作者： Dankan Gowda, V. Vishnu Tej, Y. Potharaju, Vidya Sagar Jakkidi, Premkumar Reddy Sharma, Aanchal Sudhakar Reddy, N. Department of Electronics and Communication Engineering BMS Institute of Technology and Management Karnataka Bangalore India Department of Computer Science and Systems Engineering Andhra University Visakhapatnam India Department of ECE Vignana Bharati Institute of Technology Ghatkesar Hyderabad India 15950 paramount way FriscoTX75189 United States DCSA Panjab University Chandigarh India Department of ECE School of Engineering Mohan Babu University Tirupati India

ISBN: (纸本)9798350374933

This paper proposed an improved method that can be used in the identification of images within IoT based surveillance systems through the use of deep learning techniques especially the CNNs. The growth of concern and complexity therefore entails massive feeding from bursts of data from surveillance cameras, hence the need for reliable algorithms for processing the data in real-time with reasonable accuracy. The described system can be described as inclusive of IoT devices communicating with edge computing and cloud services for the purpose of scalability and performance enhancement. In this study, a very sophisticated experimental setup was employed;the authors used a number of datasets for surveillance purposes to evaluate the model. The results revealed that the segments of the method achieve a high accuracy and are continuously running through data in diverse surveillance scenarios while also being scalable. In the results section, we realized that CNN model was able to handle various environmental conditions such as;extreme lightening and occlusion. On the basis of the above mentioned data, it will be possible to suggest that this approach is rather superior compared to the other conventional approaches and therefore can be suggested as the possible solution of the modern conditions of surveillance. It thus provides platform for the advancement of intelligent surveillance system for use in smart city, security and safety application. © 2024 IEEE.

关键词： Convolutional neural networks

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Application of Intelligent Schedule Management in Volunteer Service System 1

Application of Intelligent Schedule Management in Volunteer ...

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1st IEEE International conference on Data Intelligence and Innovative Application, DIIA 2024

作者： Li, Yongxin Luo, Xiaonan Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9798331504434

As the demand for volunteer services grows, enhancing the efficiency and management of these services has become a key issue. This paper explores methods of integrating intelligent scheduling management functions with volunteer service systems to optimize volunteer time management and task allocation. By analyzing existing volunteer service systems, we propose a novel system framework that integrates intelligent scheduling management. This framework updates volunteer schedules in real- time, automatically matches tasks with volunteers' time windows, and optimizes task distribution using smart algorithms. Experimental results show that volunteer service systems with integrated intelligent scheduling management significantly improve task allocation accuracy and volunteer satisfaction. This research provides new ideas and practical basis for the intelligent development of volunteer services. © 2024 IEEE.

关键词： Digital family digital platform volunteer service

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An Improved Low-Light image Enhancement Algorithm Based on Deep Learning 2nd

An Improved Low-Light Image Enhancement Algorithm Based on D...

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2nd International conference on Advanced Intelligent Technologies (ICAIT)

作者： Chen, Wen Hu, Chao Jiangxi Univ Sci & Technol Ganzhou 341099 Jiangxi Peoples R China NingboTech Univ Ningbo Zhejiang Peoples R China

ISBN: (纸本)9789811697357;9789811697340

At present, most low-light image enhancement algorithms rely on artificial design with a priori information and constraints, but it cannot accurately capture deep structural features of the images. In this paper, we propose an improved algorithm with EnlightenGAN as the base framework and consider the phenomenon that the distance reflected by Mean Square Error (MSE) is quite different from human intuition. By introducing Structural Similarity (SSIM) in the loss function, we try to improve the structural similarity of the enhanced images to make them more consistent with natural and human intuition. For the instability problem during Generative Adversarial Networks (GAN) training, the relatively loose and easy-to-compute Energy-Based GAN (EBGAN) is used instead of Wasserstein GAN (WGAN). The improved algorithm is finally tested by using Low-light image Enhancement (LIME) dataset as well as the test dataset of EnlightenGAN. The Peak Signal to Noise Ratio (PSNR) and SSIM values of the images processed by using the modified algorithm are calculated and compared with other algorithms, and the results show the proposed method is effective.

关键词： Deep learning Generating adversarial networks image enhancement image processing

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Design of English Teaching Quality Evaluation System Based on Deep Learning and Data Mining

Design of English Teaching Quality Evaluation System Based o...

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image processing and Computer Applications (ICIPCA), IEEE International conference on

作者： Yang Chen Guangzhou Huashang Vocational College Guangzhou China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

This research delves into the prospects of using deep learning and data mining to design an English teaching and quality system model. The paper first conducts a research and analysis on the relevant literature that examine the evaluation systems designed through data mining and deep learning algorithms. As presented in the findings, deep learning and data mining technologies that include the use of neural networks play a critical role in the development of English language assessment and evaluation systems. The findings equally establish that data mining algorithms and techniques significantly decrease the delay times caused in the traditional English teaching and evaluation system designed through the use of deep learning and data mining techniques.

关键词： Deep learning Training Volume measurement image processing Education Neural networks Time measurement Quality assessment Data mining Time factors

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Deep Neural Network for visual Emotion Recognition based on ResNet50 using Song-Speech characteristics 5

Deep Neural Network for visual Emotion Recognition based on ...

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5th IEEE International conference on Advanced systems and Emergent Technologies (IC_ASET)

作者： Ayadi, Souha Lachiri, Zied Natl Sch Engn Tunis ENIT Dept Elect Engn Signal Image & Informat Technol Lab SITI BP 37 Tunis 1002 Tunisia

ISBN: (纸本)9781665427357

Visual emotion recognition is a very large field. It plays a very important role in different domains such as security, robotics, and medical tasks. The visual tasks could be either image or video. Unlike the image processing, the difficulty of video processing is always a challenge due to changes in information over time variation. Significant performance improvements when applying deep learning algorithms to video processing. This paper presents a deep neural network based on ResNet50 model. The latter is conducted on the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) due to the variance of the nature of the data exists which is speech and song. The choice of ResNet model is based on the ability of facing different problems such as of vanishing gradients, the performing stability offered by this model, the ability of CNN for feature extraction which is considered to be the base architecture for ResNet, and the ability of improving the accuracy results and minimizing the loss. The achieved results are 57.73% for song and 55.52% for speech. Results shows that the Resnet50 model is suitable for both speech and song while maintaining performance stability.

关键词： Visual emotion recognition CNN ResNet50

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International conference on Actual Problems of Applied Mathematics and Computer systems, APAMCS 2022

International Conference on Actual Problems of Applied Mathe...

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International conference on Actual Problems of Applied Mathematics and Computer systems, APAMCS 2022

ISBN: (纸本)9783031341267

The proceedings contain 49 papers. The special focus in this conference is on Actual Problems of Applied Mathematics and Computer systems. The topics include: Initial-Boundary Value Problems for the Loaded Hallaire Equation with Gerasimov–Caputo Fractional Derivatives of Different Orders;grid Method for Solving Local and Nonlocal Boundary Value Problems for a Loaded Moisture Transfer Equation with Two Fractional Differentiation Operators;determining Frequencies of Free Longitudinal Vibrations of Rods by Analytical and Numerical Methods;forced Longitudinal Oscillations of a Rod with a Mass at the End;on the Unassociated Matrices Number of the n Order and a Given Determinant;fast Calculation of Parameters of Parallelepipedal Nets for Integration and Interpolation;Modeling of the Adjustable DC Voltage Source for Industrial Greenhouse Lighting systems;modeling of Temperature Contribution to the Interphase Energy of the Faces of Cadmium Crystals at the Boundary with Organic Liquids;mathematical Concept of a Model for processing Metadata of Employee’s Psycho-States for Identifying Him as an Internal Violator (Insider);difference Method for Solving the Dirichlet Problem for a Multidimensional Integro-Differential Equation of Convection-Diffusion;analysis of Influence of Byzantine Robots with Random Behaviour Strategy on Collective Desicion-Making in Swarms;beamforming for Dense Networks-Trends and Techniques;Modified CLNet: A Neural Network Based CSI Feedback Compression Model for Massive MIMO System;facemask Wearing Correctness Detection Using Deep Learning Approaches;an Approach to the Implementation of Nonparametric algorithms for Controlling Multidimensional Processes in a Production Problem;analysis of Neural Networks for image Classification;factors of a Mathematical Model for Detection an Internal Attacker of the Company;comparative Analysis of Methods and algorithms for Building a Digital Twin of a Smart City;Model of Error Correction Device in RNS-FRNN.

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Scaling Vision-based End-to-End Autonomous Driving with Multi-View Attention Learning

Scaling Vision-based End-to-End Autonomous Driving with Mult...

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IEEE/RSJ International conference on Intelligent Robots and systems (IROS)

作者： Xiao, Yi Codevilla, Felipe Porres, Diego Lopez, Antonio M. Univ Autonoma Barcelona UAB Dept Comp Sci Comp Vis Ctr CVC Barcelona Spain Montreal Inst Learning Algorithms MILA Montreal PQ Canada

ISBN: (纸本)9781665491907

On end-to-end driving, human driving demonstrations are used to train perception-based driving models by imitation learning. This process is supervised on vehicle signals (e.g., steering angle, acceleration) but does not require extra costly supervision (human labeling of sensor data). As a representative of such vision-based end-to-end driving models, CILRS is commonly used as a baseline to compare with new driving models. So far, some latest models achieve better performance than CILRS by using expensive sensor suites and/or by using large amounts of human-labeled data for training. Given the difference in performance, one may think that it is not worth pursuing vision-based pure end-to-end driving. However, we argue that this approach still has great value and potential considering cost and maintenance. In this paper, we present CIL++, which improves on CILRS by both processing higher-resolution images using a human-inspired HFOV as an inductive bias and incorporating a proper attention mechanism. CIL++ achieves competitive performance compared to models which are more costly to develop. We propose to replace CILRS with CIL++ as a strong vision-based pure end-to-end driving baseline supervised by only vehicle signals and trained by conditional imitation learning.

关键词： image enhancement

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Boosting With Multiple Clustering Memberships For Hyperspectral image Classification

Boosting With Multiple Clustering Memberships For Hyperspect...

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2023 IEEE SoutheastCon, SoutheastCon 2023

作者： Bellio, Giovanni Russell, Randy Kursun, Olcay Auburn University at Montgomery Department of Computer Science MontgomeryAL36117 United States Auburn University at Montgomery Department of Chemistry MontgomeryAL36117 United States

ISBN: (纸本)9781665476119

A novel hyperspectral image classification algorithm is proposed and demonstrated on benchmark hyperspectral images. We also introduce a hyperspectral sky imaging dataset that we are collecting for detecting the amount and type of cloudiness. The algorithm is designed to be applied to the Such systems could improve the spatial and temporal resolution of cloud information vital to understanding Earth's climate. We discuss the nature of our HSI-Cloud dataset being collected and an algorithm we propose for processing the dataset using a categorical-boosting method. The proposed method utilizes multiple clusterings to augment the dataset and achieves higher pixel classification accuracy. Creating categorical features via clustering enriches the data representation and improves boosting ensembles. For the experimental datasets used in this paper, gradient boosting methods performed favorably to the benchmark algorithms. © 2023 IEEE.

关键词： image classification

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Latency-aware Spatial-wise Dynamic Networks 36

Latency-aware Spatial-wise Dynamic Networks

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36th conference on Neural Information processing systems (NeurIPS)

作者： Han, Yizeng Yuan, Zhihang Pu, Yifan Xue, Chenhao Song, Shiji Sun, Guangyu Huang, Gao Tsinghua Univ Dept Automat BNRist Beijing Peoples R China Peking Univ Sch Elect Engn & Comp Sci Beijing Peoples R China

ISBN: (纸本)9781713871088

Spatial-wise dynamic convolution has become a promising approach to improving the inference efficiency of deep networks. By allocating more computation to the most informative pixels, such an adaptive inference paradigm reduces the spatial redundancy in image features and saves a considerable amount of unnecessary computation. However, the theoretical efficiency achieved by previous methods can hardly translate into a realistic speedup, especially on the multi-core processors (e.g. GPUs). The key challenge is that the existing literature has only focused on designing algorithms with minimal computation, ignoring the fact that the practical latency can also be influenced by scheduling strategies and hardware properties. To bridge the gap between theoretical computation and practical efficiency, we propose a latency-aware spatial-wise dynamic network (LASNet), which performs coarse-grained spatially adaptive inference under the guidance of a novel latency prediction model. The latency prediction model can efficiently estimate the inference latency of dynamic networks by simultaneously considering algorithms, scheduling strategies, and hardware properties. We use the latency predictor to guide both the algorithm design and the scheduling optimization on various hardware platforms. Experiments on image classification, object detection and instance segmentation demonstrate that the proposed framework significantly improves the practical inference efficiency of deep networks. For example, the average latency of a ResNet-101 on the imageNet validation set could be reduced by 36% and 46% on a server GPU (Nvidia Tesla-V100) and an edge device (Nvidia Jetson TX2 GPU) respectively without sacrificing the accuracy. Code is available at https://***/LeapLabTHU/LASNet.

关键词： Graphics processing unit

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Low-Textural image Registration: Comparative Analysis of Feature Descriptors 7th

Low-Textural Image Registration: Comparative Analysis of Fea...

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7th International conference on Computer Vision and image processing, CVIP 2022

作者： Subramanyam, Vasanth Kumar, Jayendra Singh, Shiva Nand Kumar, Roshan Yadav, Arvind R. Tata Steel Ltd Jharkhand Jamshedpur India National Institute of Technology Jharkhand Jamshedpur India Miami College of Henan University Henan China Parul Institute of Engineering and Technology Parul University Gujarat Vadodara India

ISBN: (纸本)9783031314162

Industrial machine-vision (MV) applications require high-speed stitching of low-textural images from multiple high-resolution cameras for Field-of-View expansion. The most vital step in the stitching process is the effective and efficient extraction of features, which becomes challenging for low-textural images. This paper presents a comparative study of five popular feature descriptor algorithms for image stitching viz. Scale Invariant Feature Transform (SIFT), Speeded Up Robust Feature (SURF), Oriented Fast and Rotated BRIEF (ORB), Binary Robust invariant scalable keypoints (BRISK), and Accelerated-KAZE (AKAZE). The focus of this paper is to present a study of the performance comparison among these feature extraction methods for low-textural images from real-time steel surface inspection systems. Primarily, synchronized images of steel rolled at room temperatures are obtained from a two-camera network with overlapping regions. Feature descriptor algorithms extract features from two images with an overlapping area and further match the features using K-Nearest Neighbour (KNN) algorithm. The performance of the five feature descriptor algorithms is evaluated using a low-textural dataset that consists of a set of 177 images captured from two cameras placed at a fixed distance from each other. The efficiency of these algorithms is quantitatively and qualitatively evaluated using execution time, sensitivity, and specificity. Finally, this paper provides guidelines for future research on problems with FOV expansion in industrial scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Nearest neighbor search

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