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image recognition of soybean leaf disease based on bilateral branching network BBN 4

Image recognition of soybean leaf disease based on bilateral...

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4th international conference on Bioinformatics and Intelligent Computing (BIC)

作者： Sun, Jianming Hao, Xuyao Zhao, Mengxin Harbin Univ Commerce Sch Comp Informat & Engn Harbin Peoples R China

ISBN: (纸本)9798400716645

In recent years, with the rapid growth of China's economy, environmental pollution has become more and more serious. Fog and haze weather, water pollution and other factors have led to the occurrence of soybean diseases, seriously affecting the quality and yield of soybean. With the development of computer vision technology and deep learning and gradually applied to the development of agriculture, it is possible to use computers to intelligently diagnose crop diseases. Due to the different incidence of different types of soybean diseases in different regions and at different times, the long tail distribution problem of soybean leaf disease datasets is caused, which leads to the overall performance of disease recognition models. To solve this problem, BBN_IC_MobileNetV3 network model is proposed. the new network is based on MobileNetV3. Firstly, the multi-scale feature extraction module is used to replace the first 3 x 3 convolution layer of the original network to improve the feature extraction ability of the network for different areas of disease spots. Secondly, the CA attention mechanism is used to replace the SE attention mechanism of the original network to distinguish between the target and background. Finally, the improved MobileNetV3 network is combined with the BBN network to improve the recognition accuracy of the tail data by modeling the tail data, so as to improve the overall performance of the model. Experiments show that the overall disease recognition rate of the BBN_IC_MobileNetV3 network model reaches 95.882%, and the recognition accuracy of the tail data is close to 90%, which has been improved compared with the original network.

关键词： Deep learning Long-tail distribution Disease identification Multiscale feature extraction Attention mechanism

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Semi-Supervised Depth Estimation by Multi-Task Learning 26

Semi-Supervised Depth Estimation by Multi-Task Learning

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

作者： Fu, Qingshun Dong, Xuan Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Depth estimation is a historical problem in computer vision. It is essential to get accurate depth information from cameras to implement autopilot. Most of the early studies were based on multiple observation points. therefore, monocular depth estimation was proposed to overcome this restriction, trying to estimate depth information of every pixel from a single image. In this paper, we propose a semi-supervised multi-task model to predict depth map and semantic segmentation by making use of the implicit relation between the two tasks by sharing parameters. Moreover, our presented model is able to predict favorable depth map against the state-of-the-art on the KITTI benchmark. the code we used to train and evaluate our models is available at: https://***/fyhfly/Multi-task-Depth-Estimation.

关键词： computer vision Semantic segmentation Estimation Predictive models Multitasking Natural language processing Decoding

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Enhanced Hand Gesture recognition Using image Transformer Model and Particle Swarm Optimization 4

Enhanced Hand Gesture Recognition Using Image Transformer Mo...

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4th international conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Sonu Nisha Narayan, Satya Jain, Vinesh Kumar Engineering College Ajmer India

ISBN: (纸本)9798350353266

this study introduces the image Transformer Model, a novel approach to enhance hand gesture recognition, leveraging recent advancements in deep learning and computer vision. By utilizing the Transformer's attention mechanism, the model excels in capturing intricate spatial correlations within visual data. It processes input as flattened patches, enabling comprehensive interpretation of spatial relationships in hand gesture images. the model thoughtfully combines various elements, including Multi-Head Self-Attention, FFN Network, Layer Normalization, & residual connections, resulting in superior performance compared to established models. the research further extends the model's capabilities through the incorporation of a Multi-Head Attention-based Transformer Network, complemented by Particle Swarm Optimization (PSO). By successfully optimizing hyperparameters with PSO, the model demonstrates improved recognition of complex spatial correlations in hand gesture images, outperforming such methods. the fusion of the image Transformer Model & PSO tuning represents a significant advancement in the field of hand gesture identification, promising valuable applications across diverse domains. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Real-Time Monitoring of Physiological Signals Using Millimeter-Wave Radar and Convolutional Neural Network 4

Real-Time Monitoring of Physiological Signals Using Millimet...

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4th international conference on Electronic Information Engineering and computer Communication, EIECC 2024

作者： Jia, Ye Liu, Yulei Tan, Wenjun School of Computer Science and Engineering Northeastern University Hunnan Liaoning Shenyang China Gd Midea Air-Conditioning Equipment Co. Ltd. Shunde Guangdong Foshan China

ISBN: (纸本)9798331534622

this paper presents a study on real-time monitoring technology for respiratory and heartbeat processes based on millimeter-wave radar. the core focus of this research is on achieving high real-time signal monitoring and recognition, capable of identifying the peak and smooth intervals of the respiratory process, as well as the moments of P, R, and T waves in the heartbeat process, all with minimal latency. this approach provides a more convenient and user-friendly solution for medical gating in CT, MRI, and other smart home applications. through an in-depth investigation of millimeter-wave radar signal processing methods, an efficient algorithm for respiratory and heartbeat detection is proposed, which includes noise suppression, real-time respiratory-heartbeat separation, heartbeat signal reconstruction based on deep learning, signal peak finding, and signal gradual period identification. the algorithm is verified in real-world environments, and the fusion of traditional signal processing techniques with deep learning methods enables real-time monitoring and recognition of respiratory and heartbeat signals using cost-effective 24 GHz millimeter-wave radar. © 2024 IEEE.

关键词： image segmentation

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A fast bundle adjustment method based on track selection 4

A fast bundle adjustment method based on track selection

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4th international conference on computer vision and pattern Analysis, ICCPA 2024

作者： Liu, Changwei Xiong, Pingfan Yang, Luman Chen, Song Hu, Jinquan School of Remote Sensing and Information Engineering Wuhan University Hubei Wuhan430072 China School of Geodesy and Geomatics Wuhan University Hubei Wuhan430072 China Guangdon Shenzhen518000 China Wuhan Tianjihang Information Technology Co. Ltd. Hubei Wuhan430074 China

ISBN: (纸本)9781510682528

Bundle adjustment is the core of the Structure from Motion algorithm, and it is also a very time-consuming part, in which redundant observations and initial parameter values with large errors increase the time consumption of the algorithm. In order to improve the efficiency of bundle adjustment, we propose an track selection method based on uniformity, accuracy, coverage and connectivity criteria. Firstly, we divide the space of tracks into several 3D grids. Secondly, we start from the grid with the largest number of tracks, and eliminate the redundant tracks with low connectivity and low accuracy in each grid, while ensuring the connectivity and coverage of tracks. Finally, we use a variety of experimental data to verify this algorithm. the result shows that the algorithm can delete a large number of redundant tracks, and effectively improve the efficiency of the bundle adjustment method on the premise of ensuring the accuracy. When the track retention rate is 0.4, the efficiency of the bundle adjustment method is increased by about 2 times, and the corresponding precision loss value is 0.026. © 2024 SPIE.

关键词： Tunable filters 3D tracking image restoration Point clouds 3D image reconstruction image filtering image processing Pose estimation 3D modeling Atomic force microscopy

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Bone tumor boundary demarcation based on multi-modal image fusion 4

Bone tumor boundary demarcation based on multi-modal image f...

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4th international conference on computer vision, Application, and Algorithm, CVAA 2024

作者： Shang, Ruizhe Dong, Jiaming Wang, Xinhuai School of Electronic Engineering Xidian University Xi’an China Electronic Information Class Xidian University Xi’an China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

the delineation of bone tumor boundaries is a critical issue in the field of medical image segmentation due to the unique positioning of these tumors and the complexity of the associated surgical procedures. In recent years, researchers have made significant strides in determining tumor boundaries through computer version methods. Doctors can achieve precise localization of bone tumors using the U-Net network or the improvements. However, using tumor boundary segmentation models based on single-modal images makes it challenging to capture the complete characteristics of the tumor. In this paper, we propose a multi-modal image fusion network to achieve more accurate segmentation of tumor boundaries. Experimental results show that the accuracy of the segmentation results can be improved by about 6.8% when using fused images for segmentation. therefore, this image processing process is of great significance for improving the accuracy of bone tumor boundary demarcation and the efficiency of clinical diagnosis. © 2025 SPIE.

关键词： Magnetic resonance imaging

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Machine vision Applied to Backscattered Electron Imaging 6

Machine Vision Applied to Backscattered Electron Imaging

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

作者： Huang, Junyuan Zhang, Wei Chen, Jingyun Beihang University School of Mechanical Engineering and Automation Beijing China Beijing Institute of Space Launch Technology Beijing China

ISBN: (纸本)9798350325485

Electron beam processing usually needs to be carried out in a vacuum environment. the influence of high temperature and high concentration of metal vapor makes it difficult for traditional machine vision observation systems to perform long-Term observation. In this paper, a machine vision algorithm was used, a quadrupole plate sensor and image fusion algorithm were adopted, and the combination of software and hardware was realized. Finally, we solved the problems of scale imbalance and slice model scanning order in the imaging process and successfully realized clear observation of the surface in all directions. the backscattered electron signal imaging technology has been successfully applied to the electron beam processing process, which meets the online observation requirements of the electron beam processing process. © 2023 IEEE.

关键词： computer vision

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4th international conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2023

4th International Conference on Recent Trends in Machine Lea...

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4th international conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2023

ISBN: (纸本)9789819994410

the proceedings contain 69 papers. the special focus in this conference is on Recent Trends in Machine Learning. the topics include: Implementation of Dual-Band Dielectric Resonator Antenna for 5G Applications;defect Detection in Metal Surfaces Using computer vision;liver Cirrhosis Prediction Using Machine Learning Classification Techniques;a Recent Survey on Risk Factors Affecting the Blood Pressure in India;Real-Time Monitoring System for Breakdown Analysis and OEE in the Wire Drawing Industry;tooth Sensitivity Device—Detection and Diagnosing of Sensitivity in the Dental Pulp;recognition of Skin Cancer;ioT-Based Smart Street Lighting Surveillance System;rock Segmentation of Real Martian Scenes Using Dual Attention Mechanism-Based U-Net;IAAS: IoT-Based Automatic Attendance System with Photo Face recognition in Smart Campus;hardware Implementation of Moving Object Detection Using Background Subtraction Algorithm;crime pattern Identification and Prediction Using Machine Learning;IMICE: An Improved Missing Data Imputation Using Machine Learning;analyzing Students’ Opinion on E-Learning—Indian Students’ Perspective;rash Driving Detection and Alerting System;Logistic-Based OVA-CNN Model for Alzheimer’s Disease Detection and Prediction Using MR images;Comparative Study of CNNs for Camouflaged Object Detection;3D Avatar Reconstruction Using Multi-level Pixel-Aligned Implicit Function;Helmet Detection Using YOLO-v5 and Paddle OCR for Embedded Systems;defogNet: A Residual Network for Removal of Fog Using Weighted Combination Loss;Text-to-image Generation Model with DNN Architecture and computer vision for Embedded Devices Using Quantization Technique;one-Shot Learning for Archaeological Site Data Using Deep Neural Network on Embedded Systems;enhanceNet: A Deep Neural Network for Low-Light image Enhancement with image Restoration;intelligent Prediction of Cardiac Abnormality.

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Research on image recognition based on neural network model learning algorithm 4

Research on image recognition based on neural network model ...

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4th international conference on computer vision, image and Deep Learning, CVIDL 2023

作者： Feng, Shuxuan Lu, Jun Heilongjiang University Harbin China

ISBN: (纸本)9798350326444

the model based on object detection algorithm shows great advantages in accuracy and precision rate. However, due to the different sizes of the targets to be detected in the images, coupled with the interference of factors such as occlusion and scene complexity, at the same time, the object detection has the problem of too small a percentage, which is prone to miss detection and false detection. therefore, the performance of the model needs to be further improved. In this paper, based on the shortcomings of existing models, we propose the Invo-YOLOv5s model, which can well improve the accuracy of detection and anti-interference ability. We conducted experiments on the model on the helmet dataset, and the experiments showed that the Invo-YOLOv5s model was selected for training, and the final obtained model detection accuracy reached 94.9%, which is 2.3% higher than the accuracy of the original YOLO model. © 2023 IEEE.

关键词： Object detection

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Low-quality image binarization method based on threshold array system 4

Low-quality image binarization method based on threshold arr...

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4th international conference on computer vision, image and Deep Learning, CVIDL 2023

作者： Mei, Xinguo Mu, Dazhong Cao, Peng Beijing Institution of Graphic Communication Beijing Key Laboratory of Signal and Information Processing for High-end Printing Equipment Beijing China

ISBN: (纸本)9798350326444

In order to improve the anti-noise performance of traditional image binarization methods, this paper proposes a novel binarization method for low-quality images based on threshold array system. the proposed method involves converting the image signal into a one-dimensional time sequence signal via Hilbert scanning, followed by normalization of the time sequence signal. the normalized signal is then input to the threshold array system for stochastic resonance processing. the output signal from the threshold array system undergoes demodulation and reverse scanning to generate the final binary image. Experimental results demonstrate that this approach outperforms traditional binarization methods such as Niblack, Otsu, and Sauvola under condition of strong noise interference. © 2023 IEEE.

关键词： Binary images

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