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Automatic Disease Classification of Paddy Leaf images Using Deep Learning Algorithms 4

Automatic Disease Classification of Paddy Leaf Images Using ...

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4th international conference on Signal processing and Communication, ICSPC 2023

作者： Velusamy, Durgadevi Mounika, A. Ragulraj, D. Vigneshwaran, K. M.Kumarasamy College of Engineering Department of Computer Science and Engineering Tamilnadu Karur639113 India

ISBN: (纸本)9798350300772

Agriculture is considered one of the primary sectors in India. Especially rice is the primary stable food widely cultivated and consumed by 50% of the Indian population. Paddy crop is susceptible to several diseases like other crops that indirectly affect agricultural development and economic loss. To address this limitation, in this study, we have applied deep learning algorithms for the recognition and classification of diseases at an earlier stage. the paddy plant disease diagnosis is made by applying image processing techniques to identify the changes in leaves and its texture. We have used pre-processing techniques for segmentation, feature extraction and classification of disease. First, we have to perform data augmentation to improve the number of images in the dataset. Second, the segmentation of plant leaves are performed to identify the test infected region. then, we applied CNN algorithm to extract features from the data. After feature extraction, we trained different classification algorithms to recognize and classify the images into disease and non-disease images. the application of machine learning and deep learning algorithms helps in the precise location and identification of infectious leaves that help farmers to use automated methods for disease diagnosis. © 2023 IEEE.

关键词： Feature extraction

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Shared vision Transformer Helps Scene Text Retrieval 4th

Shared Vision Transformer Helps Scene Text Retrieval

引用

4th international conference on pattern recognition and Artificial Intelligence, ICPRAI 2024

作者： Luo, Hailong Ibrayim, Mayire Hamdulla, Askar Deng, Qilin School of Computer Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China

ISBN: (纸本)9789819787043

the scene text retrieval system can search all the images containing the query text in the gallery based on the input query text and locate the position of the query text at the same time. the current state-of-the-art approach is to match query images and text instances based on visual information, which somewhat mitigates the heterogeneous differences between the two modalities and can support the zero-shot training approach. But there is no consideration of two branches of features interacting on information as a way to enhance the features. In this paper, we improve this method. First, we design a structure that enhances features using a shared-visual embedding transformer (S-VET). Text instance features and query input features are brought closer visually by a visual transformer with shared parameters and then spliced and fused with the original features to enrich the visual information of the two branch features. We then design a Multi-Scale Residual Channel Attention (MRCA) module to obtain features at different scales by using convolution kernels at multiple scales and then use channel attention to mine beneficial information from features at different scales. Facilitated by the above scheme, our method achieved a more advanced performance. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Structured Query Language

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GaussianMask: Uncertainty-aware Instance Segmentation based on Gaussian Modeling 26

GaussianMask: Uncertainty-aware Instance Segmentation based ...

引用

26th international conference on pattern recognition / 8th international Workshop on image Mining - theory and Applications (IMTA)

作者： Lee, Seung Il Kim, Hyun Seoul Natl Univ Sci & Technol Seoul Res Ctr Elect & Informat Technol Dept Elect & Informat Engn Seoul South Korea

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Instance segmentation, which has been required in various applications in recent years, is aimed at reliable bounding box (bbox) detection (i.e., localization) and stable mask prediction (i.e., segmentation). However, the mask uncertainty problem is still unresolved, which hinders the ability to achieve an accurate instance segmentation. In this paper, we propose GaussianMask, an uncertainty-aware instance segmentation technique based on Gaussian modeling. We can determine the uncertainty of the network through the variance of masks extracted by redesigning the loss function based on Gaussian modeling, and the mask accuracy can be improved by constructing a robust model that adaptively applies this uncertainty to the network. In particular, the additional computations caused during this process are insignificant, and thus negligible for the processing speed of the network. Moreover, GaussianMask has the advantage of being applicable to any network due to its high compatibility. Experimental results show that the mask average precision (AP) of the representative instance segmentation models, YOLACT and Mask R-CNN, increases from 29.7% to 31.8% and from 35.7% to 37.5%, respectively, on the MS-COCO dataset.

关键词： Location awareness Adaptation models computer vision Uncertainty Computational modeling computer network reliability Predictive models

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No-reference stereo image quality evaluation method integrating monocular and binocular features 4

No-reference stereo image quality evaluation method integrat...

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4th international conference on Artificial Intelligence and computer Engineering, ICAICE 2023

作者： Bai, Jinxin Hu, Xiaojuan School of Computer Science and Technology Changchun University of Science and Technology Jilin Changchun130000 China

ISBN: (纸本)9798400708831

In recent years, the widespread adoption of 3D technology has created a need for accurate and efficient stereoscopic image quality assessment methods. Such methods aim to automatically supervise and optimize stereoscopic image and video processing systems. Based on the characteristics of human vision, we propose a method that integrates monocular and binocular features. Initially, monocular quality-sensitive features are extracted separately for the left and right views through two parallel CNN sub-networks. Subsequently, a third CNN sub-network extracts binocular quality-sensitive features for stereo image pairs. Quality scores are then computed based on both monocular and binocular features, and the final predicted quality score is obtained through quality fusion. the proposed method is validated on the Waterloo dataset, achieving a PLCC value of 0.979 and SROCC value of 0.975 on Waterloo P-I, and a PLCC value of 0.973 and SROCC value of 0.970 on Waterloo P-II. the results demonstrate that the predicted scores obtained by the proposed method exhibit excellent consistency with subjective evaluation scores. © 2023 ACM.

关键词： Binoculars

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A Federated Learning Approach for Enhancing Autonomous Vehicles image recognition 4

A Federated Learning Approach for Enhancing Autonomous Vehic...

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4th international conference on computer Communication and the Internet, ICCCI 2022

作者： Stergiou, Konstantinos D. Psannis, Konstantinos E. Vitsas, Vasileios Ishibashi, Yutaka University of Macedonia Department of Applied Informatics Thessaloniki Greece International Hellenic University Department of Information and Electronic Engineering Thessaloniki Greece Nagoya Institute of Technology Graduate School of Engineering Nagoya Japan

ISBN: (数字)9781665469920

ISBN: (纸本)9781665469920

Traffic sign recognition is vital for the success of the autonomous vehicles paradigm in mobile edge computing. We present a Federated Learning implementation based on a Lenet-5 CNN architecture which can enhance edge computing systems on unsupervised feature classification problems. Experiments demonstrated that our model can deliver satisfactory performance on traffic sign image recognition. Also our research has shown that less resources can be utilized on client devices to run the local training models. However, heavy data processing must be shifted to intermediate edge computing layers requiring more computing resources and the optimization of parameters such as latency, bandwidth, and privacy. © 2022 IEEE.

关键词： image recognition

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Artificial Intelligence Techniques for Medical image Segmentation: A Technical Overview and Introduction to Advanced Applications 4th

Artificial Intelligence Techniques for Medical Image Segment...

引用

4th international conference on Digital Technologies and Applications (ICDTA)

作者： Sabbar, Hanan Silkan, Hassan Abbad, Khalid Chouaib Doukkali Univ Comp Sci Dept LAROSERI Lab El Jadida Morocco Sidi Mohamed Ben Abdellah Univ Sci Dept Intelligent Syst & Applicat Lab Fes Morocco

ISBN: (纸本)9783031686597;9783031686603

Medical image segmentation plays a crucial role in enhancing diagnostic, treatment, and research applications within the medical field, increasingly relying on sophisticated artificial intelligence (AI) technologies. this survey explores various AI architectures employed in medical image segmentation, including Deep Neural Networks, Encoder-Decoder Networks, Attention-Based Networks, and hybrid models that integrate features from multiple architectures. It methodically assesses these models' performances, focusing on their effectiveness across different medical imaging types. the analysis delves into each technique's specific advantages and limitations, offering valuable insights into their practical applications in medical diagnostics and treatment planning. the comparative study highlights the differences in accuracy, processing time, and the ability to manage complex image structures among the methods reviewed. It also explores the synergistic benefits of hybrid models, which are shown to achieve superior segmentation results. By providing a comprehensive comparison of AI-driven segmentation techniques, the paper significantly enhances our understanding of how these technologies can be optimized and implemented effectively in medical image analysis. the ultimate goal is to guide future research and the practical application of AI in medical image segmentation, aiming to develop more efficient and precise diagnostic tools.

关键词： Segmentation Medical image Artificial intelligent Deep learning computer vision

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A Multi-stage Feature Enhancement Network with Second-Attention for image Captioning 6

A Multi-stage Feature Enhancement Network with Second-Attent...

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6th international conference on Artificial Intelligence and pattern recognition, AIPR 2023

作者： Yang, Xiaobao Yang, Yang Wu, Junsheng Ma, Sugang Hou, Zhiqiang Song, Bohui Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China School of Computer Science Northwestern Polytechnical University Xi'an710071 China

ISBN: (纸本)9798400707674

In the past few years, self-attention in Transformer has been widely used in natural language processing (NLP) and computer vision (CV) due to its excellent ability to capture global information, especially in image captioning, where the use of self-attention can significantly improve the representation of visual information. However, the obtained image feature from Transformer-based models suffers from two main problems, one is information redundancy due to the global aggregation of self-attention, and the other is the lack of semantic information caused by single-scale feature extraction. therefore, we first propose second-attention in this paper, by redistributing image attention weights, second-attention can effectively remove irrelevant information and enhance the attention to essential objects and relations. Meanwhile, to enrich semantic information and further enhance the role of second-attention, we design a Multi-stage Feature Enhancement (MFE) network to improve the ability to represent visual information. After conducting extensive experiments on the MS COCO dataset, we achieve significant improvements in all popular benchmarks, particularly BLEU4 increased by 1.6%, CIDEr increased by 3.4%. © 2023 ACM.

关键词： Semantics

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Enhancing image Classification with Bayesian Neural Networks: Improved Training Techniques and Decision Strategies for Reliable AI Support in Uncertainty-Sensitive Tasks 4

Enhancing Image Classification with Bayesian Neural Networks...

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4th international conference on computer, Big Data and Artificial Intelligence, ICCBD+AI 2023

作者： Liu, Yan.Cheng. Huang, Hua.Jun Wang, Yi.Wen Hunan University of Finance and Economics School of Information Technology and Management Changsha China

ISBN: (纸本)9798350373233

Bayesian theory is a probabilistic statistical framework for dealing with uncertainty and incomplete information, while neural networks are widely applied in deep learning fields such as image processing. Bayesian Neural Networks (BNNs) combine Bayesian theory and neural networks to provide more comprehensive predictive results. Although BNNs have significant advantages in expressing model uncertainty, their training methods and network structure design are still to be explored. Effectively training BNNs and exploring their network structure design to enhance predictive performance and computational efficiency are the core of this research. the study aims to improve the performance of BNNs in image classification tasks, specifically including the quantification and utilization of uncertainty to enhance model reliability and decision support capabilities. We use efficient training methods such as variational inference to redesign Bayesian neural networks and conduct deep image recognition training on the CIFAR-10 dataset. the improved training method allows for the rapid and accurate estimation of model weight uncertainty, while the proposed decision strategy utilizes uncertainty information to make reliable decisions. this study demonstrates that BNNs have tremendous potential in addressing real-world uncertainty issues, with the results expected to provide safer AI decision support for risk-sensitive tasks and promote the reliability and practicality of deep learning technology in various critical applications. © 2023 IEEE.

关键词： image classification

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FIGHTING OVER-FITTING WIth QUANTIZATION FOR LEARNING DEEP NEURAL NETWORKS ON NOISY LABELS 30

FIGHTING OVER-FITTING WITH QUANTIZATION FOR LEARNING DEEP NE...

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30th IEEE international conference on image processing (ICIP)

作者： Tallec, Gauthier Yvinec, Edouard Dapogny, Arnaud Bailly, Kevin Sorbonne Univ CNRS ISIR 4 Pl Jussieu F-75005 Paris France Datakalab 114 Blvd Malesherbes F-75017 Paris France

ISBN: (纸本)9781728198354

the rising performance of deep neural networks is often empirically attributed to an increase in the available computational power, which allows complex models to be trained upon large amounts of annotated data. However, increased model complexity leads to costly deployment of modern neural networks, while gathering such amounts of data requires huge costs to avoid label noise. In this work, we study the ability of compression methods to tackle both of these problems at once. We hypothesize that quantization-aware training, by restricting the expressivity of neural networks, behaves as a regularization. thus, it may help fighting overfitting on noisy data while also allowing for the compression of the model at inference. We first validate this claim on a controlled test with manually introduced label noise. Furthermore, we also test the proposed method on Facial Action Unit detection, where labels are typically noisy due to the subtlety of the task. In all cases, our results suggests that quantization significantly improve the results compared with existing baselines, regularization as well as other compression methods.

关键词： Quantization Deep Learning computer vision Facial Expression recognition Noisy labels

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Research on image recognition Method Based on Improved Neural Network 4

Research on Image Recognition Method Based on Improved Neura...

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4th international conference on Communications, Information System and computer Engineering, CISCE 2022

作者： Yang, Shen Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (数字)9781665498487

ISBN: (纸本)9781665498487

In the 1960s, Hubel et al. proposed the concept of receptive field through the study of cat visual cortex cells[1]. In the 1980s, Fukushima [2] proposed the concept of neurocognitive machine based on the concept of receptive field, which can be regarded as the first implementation of convolutional neural network (CNN). the neurocognitive machine will decompose a visual pattern into many sub-patterns (features), and then enter the hierarchical hierarchical connected feature plane for processing. It tries to model the visual system so that it can complete recognition even when the object has displacement or slight deformation. CNN is a variant of multi-layer perceptron (MLP), developed from biologists Huber and Wessel's [3] early research on the visual cortex of cats. there is a complex structure in the cells of the visual cortex, and these cells are very sensitive to the sub-regions of the visual input space, called the receptive field.[4] CNN was proposed by Yann Lecun of New York University in 1998 [5]. In essence, CNN is widely used neural network architecture. the success lies in the local connection and weight sharing adopted by CNN. On the other hand, the complexity of the model is reduced, that is, the risk of overfitting is reduced. In this paper, using CNN as the research method, image recognition is studied by illustrating a set of comparative experiments. In the comparative experiment, I will optimize the network model of previous experiments, and finally effectively and significantly improve the efficiency of model training. © 2022 IEEE.

关键词： image recognition

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