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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 and Graphics 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Huchuan Lu Risheng Liu Wanli Ouyang Hui Huang Jiwen Lu Jing Dong Min Xu

ISBN: (数字)9783031463174

ISBN: (纸本)9783031463167

the five-volume set LNCS 14355, 14356, 14357, 14358 and 14359 constitutes the refereed proceedings of the 12th international conference on Image and Graphics, ICIG 2023, held in Nanjing, China, during September 22–24, 2023.;the 166 papers presented in the proceedings set were carefully reviewed and selected from 409 submissions. they were organized in topical sections as follows: computer vision and pattern recognition; computer graphics and visualization; compression, transmission, retrieval; artificial intelligence; biological and medical image processing; color and multispectral processing; computational imaging; multi-view and stereoscopic processing; multimedia security; surveillance and remote sensing, and virtual reality.;the ICIG 2023 is a biennial conference that focuses on innovative technologies of image, video and graphics processing and fostering innovation, entrepreneurship, and networking. It will feature world-classplenary speakers, exhibits, and high-quality peer reviewed oral and poster presentations.

关键词： Image Processing and Computer Vision Artificial Intelligence Computer Systems Organization and Communication Networks pattern recognition

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Concrete Compressive Strength Prediction Based Web Application Using Gradient Boosting Based ML Ensemble: A Case Study 12

Concrete Compressive Strength Prediction Based Web Applicati...

引用

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

作者： Nasier, Sandeep Lallotra, Balwinder Hooda, Nishtha Chopra, Hetarth Chandigarh University Department of Civil Engineering Mohali India School of Computing Iiit Una India Thapar Institute Of Engineering & Technology Department of Mechanical Engineering Patiala India

ISBN: (纸本)9798350369861

the escalating demand of high strength concrete for the construction of the high-rise buildings is straining the cement industry. the experimental data for calculating compressive strength (MPa) is produced at the laboratory for various sets of trial batches of concrete under a specific age with ingredients like cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, and fine aggregate. the raw data is pre-processed and used to train the state-of-the-art machine learning models for predicting the concrete compressive strength. Gradient boosting based ensemble using blending and stacking is implemented in order to optimize the predictive performance. However, despite of that, a fine-tuned Cat Boost model, using Random and Grid search parameters, performed the best, and was selected as the proposed model. © 2023 IEEE.

关键词： Compressive strength

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Identity and variation spaces: Revisiting the fisher linear discriminant

Identity and variation spaces: Revisiting the fisher linear ...

引用

2009 IEEE 12th international conference on Computer Vision Workshops, ICCV Workshops 2009

作者： Zhang, Sheng Sim, Terence Yeh, Mei-Chen Department of Psychology University of California Santa Barbara CA United States School of Computing National University of Singapore Singapore Department of Computer Science and Information Engineering National Taiwan Normal University Taipei Taiwan

ISBN: (纸本)9781424444427

the Fisher Linear Discriminant (FLD) is commonly used in classification to find a subspace that maximally separates class patterns according to the Fisher Criterion. It was previously proven that a pre-whitening step can be used to truly optimize the Fisher Criterion. In this paper, we study the theoretical properties of the subspaces induced by this whitened FLD. Of the four subspaces induced, two are most important for classification and representation of patterns. We call these Identity Space and Variation Space. We show that only the between-class variation remains in Identity Space, and only the within-class variation remains in Variation Space. Both spaces can be used for decomposition and representation of class data. Moreover, we give sufficient conditions for these spaces to exist. Finally, we also run experiments to show how Identity and Variation Spaces may be used for classification and image synthesis. ©2009 IEEE.

关键词： pattern recognition

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Construction Method of Power Knowledge Map Based on Deep neural Network Model 12

Construction Method of Power Knowledge Map Based on Deep neu...

引用

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

作者： Zheng, Chuang Li, Tong He, Jin Wang, Zhanguo Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang110168 China University of Chinese Academy of Sciences Beijing100049 China Northeastern University Shenyang110004 China State Grid Liaoning Electric Power Research Institute Shenyang110055 China Information Communication Branch of Liaoning Electric Power Co. Ltd Shenyang110000 China

ISBN: (纸本)9798350332919

With the increasing demand for automation and efficiency of the power grid, the power industry needs to accelerate the pace of building a highly intelligent and intelligent platform. However, the power operation and maintenance knowledge base for the professional model field is different from the common knowledge bases on the market. Some conventional application technologies cannot be used directly. the technical difficulties and practical business problems faced by the power operation and maintenance knowledge base in real scenarios are still not very good. solution. this paper uses technologies such as natural language processing, combined with deep neural network models, to construct a set of portable and easy-to-retrieve power knowledge graphs through knowledge extraction, knowledge fusion, knowledge reasoning and other technologies. © 2023 IEEE.

关键词： Knowledge graph

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Enhanced Face recognition Intelligent System on Smart Home Security and Control with CNN 12

Enhanced Face Recognition Intelligent System on Smart Home S...

引用

12th IEEE international conference on Communication, Networks and Satellite, COMNETSAT 2023

作者： Mandala, Satria Ramadhan, Rifqi Irsan, Muhamad Engineering School of Computing Bandung Indonesia Telkom University School of Computing Bandung Indonesia

ISBN: (纸本)9798350341102

In recent years, there has been a growing interest in research related to facial recognition and its integration into home security systems. However, the challenge of achieving high face detection accuracy remains unresolved. Many studies based on Convolutional Neural Networks (CNNs) have yielded only moderate results in face detection, and their real-time implementation remains a concern. To address these issues, this research proposes a solution by implementing three transfer learning methods: ResNeXt, GhostNet, and MobileNet V2. the aim is to enhance the accuracy of CNN-based face detection. Additionally, this study combines transfer learning-based facial detection with Internet of things (IoT) technology to improve home security. Experimental results demonstrate the impressive capability of the GhostNet algorithm, achieving an accuracy rate of 98.75% in face detection. Moreover, the recall, precision, and F1 score metrics stand at 98.73%, 98.78%, and 98.75%, respectively. these findings conclude that GhostNet is superior compared to the other two algorithms, i.e., ResNeXt and MobileNet V2. this research concludes that transfer learning offers a promising solution to overcome the accuracy challenges in face detection for home security. © 2023 IEEE.

关键词： Face recognition

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YOLO-RS: An Efficient YOLO-Based Approach for Remote Sensing Object Detection 12

YOLO-RS: An Efficient YOLO-Based Approach for Remote Sensing...

引用

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

作者： Hashmi, Hina Dwivedi, Rakesh Kumar, Anil College of Computing Sciences & Information Technology Teerthanker Mahaveer University Moradabad India Dehradun India

ISBN: (纸本)9798350369861

this research introduces 'YOLO-RS,' an efficient object detection system based on YOLO tailored for remote sensing applications. YOLO-RS excels in real-time object detection in satellite and aerial imagery, optimizing performance while maintaining high accuracy. this study evaluates YOLO-RS on diverse remote sensing datasets, showcasing its precision and recall. the framework's speed and adaptability make it a valuable tool for tasks such as land cover mapping and disaster response in remote sensing scenarios. In this analysis, we have chosen the SENTINEL-2 dataset. With the help of the SENTINEL-2 dataset, we have evaluated the performance and accuracy of YOLOv3 and analyzed its strengths and weaknesses. Using the results obtained from the implementations, we determined and optimized the detection of building and trees in satellite imagery. the computed results indicate an average accuracy of 85.6%, precision of 98.9%, and recall of 86.6%. © 2023 IEEE.

关键词： Object detection

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Regional Air Quality Prediction Model Based on Deep Belief Network 12

Regional Air Quality Prediction Model Based on Deep Belief N...

引用

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

作者： Bai, Xue Zhang, Zhe Shenyang Institute of Computing Technology China Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350332919

Air quality prediction is of great significance for the daily travel of the public, the government to control air pollution and promote green development. In view of the problem that most current researches in the field is the daily prediction of air quality index, there are few studies on the prediction of a variety of specific pollutant concentrations. In this paper, a Deep Belief Network-Extreme Learning Machine (DBN-ELM) air quality prediction model combined with attention mechanism is proposed. Firstly, DBN is used to extract the features of air quality data, and the robustness of network is enhanced by using the improved particle swarm optimization algorithm to optimize initial weights. To address the spatial correlation of air quality in the region, the attention mechanism is used to fuse the features of data from different monitoring sites, and the fused feature data are input into ELM for prediction, then the hourly concentrations of six air pollutants are obtained. the experimental results show that the prediction results of the proposed model are better than other comparison models, and it has good prediction performance, which provides a new idea for the field of air quality prediction. © 2023 IEEE.

关键词： Forecasting

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Enhancing Toxoplasmosis Chorioretinitis Detection: A Hybrid ResNet-YOLO Classifier with Fitness Sorted-Shark Smell Optimization 12

Enhancing Toxoplasmosis Chorioretinitis Detection: A Hybrid ...

引用

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

作者： Tiwari, Raj Gaurang Maheshwari, Himani Gautam, Vinay Agarwal, Ambuj Kumar Trivedi, Naresh Kumar 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

Ophthalmologists often examine fundus pictures to detect toxoplasmosis chorioretinitis, a serious condition of the eye. Preventing blindness in people who may be impacted by this disorder requires prompt and correct diagnosis. In this study, we present a new method for improving Toxoplasmosis chorioretinitis diagnosis accuracy by using a hybrid deep learning model that fuses features from the ResNet and YOLO (You Only Look Once) architectures. the core difference in our method is that we use the YOLO classifier for illness identification instead of the ResNet model's fully connected layer. We use the Fitness Sorted-Shark Smell Optimization (FS-SSO) technique to fine-tune crucial model parameters to get a high recognition rate with this hybrid deep learning model. Using a wide range of performance indicators and accuracy measures, we rigorously assess the effectiveness of the suggested strategy in our experimental investigation. the findings show that our hybrid model outperformed the state-of-the-art methods for detecting Toxoplasmosis chorioretinitis with an accuracy of 99%. Using the FS-SSO approach for parameter optimization, this study demonstrates the promise of combining the strengths of the ResNet and YOLO models for medical picture analysis. Our method's excellent accuracy suggests that it might be used to enhance patient outcomes and minimize the incidence of blindness caused by Toxoplasmosis chorioretinitis by allowing for earlier diagnosis and treatments. © 2023 IEEE.

关键词： Computer aided diagnosis

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Robust Facial Expression recognition Based on Local Tri-directional Coding pattern 12th

引用

12th international conference on Complex, Intelligent, and Software Intensive Systems (CISIS)

作者： Tong, Ying Chen, Rui Yang, Jie Wu, Minghu PLA Univ Sci & Technol Coll Commun Engn Nanjing 210007 Jiangsu Peoples R China Nanjing Inst Technol Dept Commun Engn Nanjing 211167 Jiangsu Peoples R China Hubei Univ Technol Hubei Key Lab High Efficiency Utilizat Solar Ener Wuhan Hubei Peoples R China

ISBN: (纸本)9783319936598;9783319936581

Automatic facial expression recognition system has broad applications prospect in the field of computer vision, and of which the precision of facial expression feature extraction is the crucial factor for improving recognition accuracy. Whereas the traditional Local Binary pattern has the shortcomings of inaccurate feature description and large feature size, in this paper we propose a novel feature descriptor, namely local tri-directional coding pattern (LtriDCP), to overcome the above two flaws. First, we adopt Kirsch masks to compute convolution values of each pixel in 8 directions, which can accurately represent the texture information of facial expression compared with the coding scheme of LBP. Second, considering that the difference of facial expression is mainly characterized in the texture change of horizontal, vertical and diagonal directions in eye, mouth, forehead and other facial areas, we only encode three convolution values in the corresponding directions to reduce feature size but still retain superior performance. Experimental results on JAFFE database show that LtriDCP outperforms several state-of-the-art feature descriptors and demonstrate its effectiveness.

关键词： Facial Expression recognition Local Binary pattern Kirsch mask

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A Sensitive Image Generation Method Based on Improved PatchGAN 12

A Sensitive Image Generation Method Based on Improved PatchG...

引用

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

作者： Zhang, Shilin Wang, Hongliang Wang, Liang Shenyang Institute of Computing Technology Shenyang Chemical University Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China

ISBN: (纸本)9798350332919

Aiming at the problem of less data and unbalanced number of sensitive image samples in the deep learning task of sensitive image detection, this paper proposes a sensitive image generation method SI-GAN based on improved PatchGAN. the SI-GAN generator uses the DUD module, and uses the concat method to increase the number of channels of the feature map in the quantitative dimension, which improves the generation ability of the generator. the SI-GAN discriminator uses the RBL module to improve PatchGAN, extract more advanced feature information, and increase the discriminant's discriminative ability. the experimental results show that SI-GAN has better results in terms of generated image quality. the FID value of SI-GAN is 5.87 lower than that of PatchGAN. the IS value of SI-GAN is 5.09 more than PatchGAN. the SI-GAN proposed in this paper is able to optimize the quality of the generated images and generate images with more details. © 2023 IEEE.

关键词： Deep learning

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