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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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Dynamic Convolutional Feature Pyramid Based Food Image recognition Method 23

Dynamic Convolutional Feature Pyramid Based Food Image Recog...

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proceedings of the 2023 6th international conference on Artificial Intelligence and pattern recognition

作者： Shuyan Wang Chutong Cui school of computing Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9798400707674

Food image recognition has become a research hotspot in many fields such as computer vision, medical and health informatics, and data mining, and has very important research value and application prospects. In this paper, we optimize CNN networks and propose a lightweight food image recognition model MobileFoodNets based on dynamic convolutional feature pyramids, which uses full-dimensional dynamic convolution instead of conventional convolutional layers to improve the feature extraction capability of the model. For the characteristics of food images with large scale variations, this paper adds a feature pyramid structure to the model, which can improve the robustness to different recognition targets. We conducted several experiments using the currently recognized Vireo Food-172 food benchmark dataset, and the MobileFoodNets model obtained a top-1 accuracy of 85.21% and a top-5 accuracy of 94.36%. the experimental results show that the model in this paper outperforms all existing food image recognition methods on the Vireo Food-172 dataset.

关键词： Convolutional Neural Networks Dynamic Convolution Feature Pyramid Food Image recognition

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Research and implementation on automatic test verification technology for trust root and trust function 23

Research and implementation on automatic test verification t...

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proceedings of the 2023 12th international conference on Software and Information Engineering

作者： Yanfang Su Yanling Wang The Third Research Institute of Ministry of Public Security ShanghaiChina China Beijing Huatech Trusted Computing Information Technology Co. Ltd.BeijingChina China

ISBN: (纸本)9798400708107

Trusted computing technology is the basic key technology in cybersecurity. Trusted computing is based on trusted root of TPCM, Equal Protection 2.0 has make clear requirements for trusted verification, but lack of testing methods and tools for the capabilities of trust root and trust functions. this paper analyzes the characteristics and test points of trusted verification technology, proposed a detected method with the fusion of scene simulation exhaustive testing, automated scripts, hardware serial port output and other test technologies, established a testing tool system of trusted function, realized to test of trusted functions such as resource isolation mechanism, trust chain construction and active measurement, supported for testing and assessment work, improved the development of Trusted computing products.

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Multi-Scale Channel Attention for Chinese Scene Text recognition 22

Multi-Scale Channel Attention for Chinese Scene Text Recogni...

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proceedings of the 2022 11th international conference on computing and pattern recognition

作者： Haiqing Liao Xia Du Yun Wu Da-Han Wang Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology China

ISBN: (纸本)9781450397056

Scene text recognition have proven to be highly effective in solving various computer vision tasks. Recently, numerous recognition algorithms based on the encoder-decoder framework have been proposed for handling scene texts with perspective distortion and curve shape. Nevertheless, most of these methods only consider single-scale features while not taking multi-scale features into account. Meanwhile, the existing text recognition methods are mainly used for English texts, whereas ignoring Chinese texts' pivotal role. In this paper, we proposed an end-to-end method to integrate multi-scale features for Chinese scene text recognition (CSTR). Specifically, we adopted and customized the Dense Atrous Spatial Pyramid Pooling (DenseASPP) to our backbone network to capture multi-scale features of the input image while simultaneously extending the receptive fields. Moreover, we added Squeeze-and-Excitation Networks (SE) to capture attentional features with global information to improve the performance of CSTR further. the experimental results of the Chinese scene text datasets demonstrate that the proposed method can efficiently mitigate the impacts of the loss of contextual information caused by the text scale varying and outperforms the state-of-the-art approaches.

关键词： Chinese Scene Text recognition Multi-scale Dense Atrous Spatial Pyramid Pooling

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Plant Seedling recognition Using Swin-Transformer Network Architecture 23

Plant Seedling Recognition Using Swin-Transformer Network Ar...

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proceedings of the 2023 6th international conference on Artificial Intelligence and pattern recognition

作者： Xu Xuebin Gao Xiangdong Wang Ruizhe School of Computing Xi 'an University of Posts and Telecommunications China Xi 'an University of Posts and Telecommunications China

ISBN: (纸本)9798400707674

the study proposes a novel method for plant seedling image recognition using the Swin-Transformer network architecture, a convolutional neural network architecture developed by the Microsoft Research Institute. the aim of the study is to overcome the limitations of traditional manual methods of plant seedling classification through machine-based recognition to achieve precise identification of crops during field nursery, fertilizer management, and enhance crop yield and quality. the proposed approach leverages the Swin-Transformer network and various data processing techniques to achieve higher accuracy and generalization ability for fine-grained seedling classification. the study includes extensive experiments on relevant datasets using a high-performance GPU environment and compares its method with other state-of-the-art approaches, which demonstrate better classification accuracy results. Besides plant seedling recognition research, the paper also has practical applications. Finally, the paper evaluates the proposed method using commonly used machine learning evaluation metrics to assess model performance.

关键词： Image Classification Swin-Transformer Image recognition of Plant Seedlings

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Basic Research on Machine Vision Underpinned by Image Frame Algebra (VFA) and Visual Semantic Algebra (VSA)

Basic Research on Machine Vision Underpinned by Image Frame ...

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international conference on Machine Vision and Information Technology (CMVIT)

作者： Yingxu Wang President International Institute of Cognitive Informatics and Cognitive Computing (I2CICC) Visiting Professor: Stanford Univ. (2008|2016) MIT (2012) UC Berkeley (2008) Oxford Univ. (1995|2018–2022) Distinguished Visiting Professor Tsinghua Univ. (2019–22) Schulich School of Engineering and Hotchkiss Brain Institute University of Calgary 2500 University Drive NW Calgary Alberta Canada

Computer vison [1], [2], [3], [4], [5] studies properties of machine vision, its semantic understanding, and general manipulations by Intelligent Mathematics (IM) [6], [7], [8], [9], [10] [11], [12], [13], [14], [15] [16], [17]. Computer vison has been studies from various aspects such as algorithmic methods, analysis methods, pattern recognitions, and neural-network-regression (AI) technologies [2], [3]. However, there is a lack of fundamental theories for enabling autonomous image recognition and processing by machines. Basic research on contemporary IM has revealed that formal manipulations of visual objects by intelligent machines may be rigorously implemented by Image Frame Algebra (IFA) [8], [18] in the front-end and Visual Semantic Algebra (VSA) [19] in the backend. IFA formally manipulates visual images as general 2D matrixes by a set of algebraic operators such as modeling, analyses, syntheses, feature elicitation, and pattern recognition [4], [5], [18]. then, its counterpart, VSA, transforms the geographic relations of visual objects to their semantic interpretations by algebraic analyses and compositions. the coherent theory of IFA and VSA provides a formal methodology for machine-enabled image processing and comprehension. this keynote presents a theoretical framework of machine vision underpinned by IFA and VSA for the structural denotations of visual objects and functional manipulations of visual mechanisms [3], [8], [9]. It demonstrates how the persistent challenges to machine vision may be rigorously and efficiently solved by the IFA/VSA methodology. Case studies on applying IFA/VSA for rigorous visual pattern detection, recognition, analysis, and composition in real world will be demonstrated [5], [18], [20]. As two coherent paradigms of IM, among others [21], [22], [23], [24], [25] [26], [27], [28], [29], [30], IFA and VSA have been applied not only in robot visual and spatial reasoning, but also in computational intelligence and AI for rigorously

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ResAsapp: An Effective Convolution to Distinguish Adjacent Pixels For Scene Text Detection 22

ResAsapp: An Effective Convolution to Distinguish Adjacent P...

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proceedings of the 2022 11th international conference on computing and pattern recognition

作者： Kangming Weng Xia Du Kunze Chen Dahan Wang Shunzhi Zhu Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology China

ISBN: (纸本)9781450397056

the segmentation-based approach is an essential direction of scene text detection, and it can detect arbitrary or curved text, which has attracted the increasing attention of many researchers. However, extensive research has shown that the segmentation-based method will be disturbed by adjoining pixels and cannot effectively identify the text boundaries. To tackle this problem, we proposed a ResAsapp Conv based on the PSE algorithm. this convolution structure can provide different scale visual fields about the object and make it effectively recognize the boundary of texts. the method's effectiveness is validated on three benchmark datasets, CTW1500, Total-Text, and ICDAR2015 datasets. In particular, on the CTW1500 dataset, a dataset full of long curve text in all kinds of scenes, which is hard to distinguish, our network achieves an F-measure of 81.2%.

关键词： Pixel Interference Scene Text Detection Curved Text

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Low-Cost Mobile-Based Augmented Reality Viewer: Bridging the Gap Between Affordable AR Glasses and Smartphones 23

Low-Cost Mobile-Based Augmented Reality Viewer: Bridging the...

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proceedings of the 2023 12th international conference on Networks, Communication and computing

作者： Kent T. Espejon Jaymer M. Jayoma Elbert S. Moyon College of Computing and Information Sciences Caraga State University Philippines

ISBN: (纸本)9798400709265

this research project aims to enhance the accessibility of Augmented Reality (AR) technology by developing a low-cost mobile-based AR viewer. the study delves into the hardware and software setup, conducting a comprehensive feature analysis of both the mobile-based AR viewer and traditional AR glasses. Emphasizing the accessibility advantages inherent to the mobile-based AR viewer while candidly acknowledging its limitations, the research also explores the broader implications for AR technology adoption and outlines strategies for future improvements in this evolving field.

关键词： Augmented Reality Cost Analysis Cost-Effectiveness Mobile-Based AR Viewer Traditional AR Glasses

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Generative Data Augmentation for Arabic Handwritten Digit recognition Boosting Real-time OCR Capabilities 23

Generative Data Augmentation for Arabic Handwritten Digit Re...

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proceedings of the 2023 6th international conference on Artificial Intelligence and pattern recognition

作者： Majid Memari Khaled R. Ahmed Shahram Rahimi School of Computing Southern Illinois University USA Department of Computer Science Mississippi State University USA

ISBN: (纸本)9798400707674

this study assesses the effectiveness of Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) in enhancing Optical Character recognition (OCR) accuracy for Arabic handwritten digits, an area with limited research and significant challenges arising from character similarities. By employing data augmentation to generate varied images, we aim to address the issues OCR systems face with low-quality or distorted inputs. We introduce a novel procedure to calculate the Fréchet Inception Distance (FID), to accurately measure the quality of synthetic images during training, facilitating early stopping to optimize model performance. Additionally, using Saliency Maps allows for detailed analysis of OCR improvements. Our findings highlight the potential of synthetic data in advancing real-time OCR systems, with our evaluation procedure offering a faster, more accurate alternative to traditional quality metrics.

关键词： Generative Adversarial Networks Generative Models Optical Character recognition Real-time Data Augmentation Synthetic Image Evaluation Variational Autoencoders

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Trusted Authentication Mechanism for Perceptual layer-oriented Network Routing 22

Trusted Authentication Mechanism for Perceptual layer-orient...

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proceedings of the 2022 12th international conference on Communication and Network Security

作者： Wei Zheng Naixin Kang Tao Ye Qi Wang faculty of Information Technology Beijing University of Technology China Shenzhen High-Tech Industrial Trusted Computing Technology Co.Ltd China

ISBN: (纸本)9781450397520

IoT routing is the basis for efficient operation of perception layer networks. the current routing technology of IoT lacks a trusted authentication mechanism, and the adopted traditional routing algorithm can hardly meet the routing demand of massive IoT terminals. Based on this, this paper proposes a trusted authentication mechanism for IoT routing, and combines reinforcement learning and trickle timer to propose an intelligent adaptive trickle timer algorithm, which ensures the secure and trustworthy and efficient operation of IoT routing.

关键词： perceptual layer credible routing authentication

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Anomaly Detection of System Behavior in Cloud computing Environments based on One-Class SVM 23

Anomaly Detection of System Behavior in Cloud Computing Envi...

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proceedings of the 2023 6th international conference on Artificial Intelligence and pattern recognition

作者： Shuaibo Nan Ruilian Xie Lijun Chen Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9798400707674

With the rapid development and widespread adoption of cloud computing, ensuring the stability and security of cloud computing environments has become increasingly important. However, due to the complexity and highly dynamic nature of cloud computing environments, detecting abnormal behaviors has become more challenging. To address the issue of abnormal behaviors in cloud computing environments, this paper proposes an anomaly detection model based on One-Class SVM and applies it to the cloud computing context. the model leverages the unsupervised learning capability of One-Class SVM, training solely on normal sample data to detect abnormal behaviors. In this study, a set of key performance indicators representing system performance is extracted using eBPF technology, and these data are preprocessed and subjected to feature selection. By monitoring and analyzing these indicators and training the model with different parameter combinations, a system behavior model with high accuracy can be established, capable of detecting anomalies that deviate from normal behaviors. Experimental results demonstrate that the anomaly detection model based on One-Class SVM exhibits high accuracy and robustness in identifying abnormal behaviors in cloud computing environments.

关键词： Linux One-Class SVM anomaly detection cloud computing eBPF

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