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检索条件"任意字段=Conference on Image Processing - Machine Vision Applications VIII"

共 3255 条记录，以下是121-130 订阅

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5th International conference on Data Science, machine Learning and applications, ICDSMLA 2023

5th International Conference on Data Science, Machine Learni...

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5th International conference on Data Science, machine Learning and applications, ICDSMLA 2023

ISBN: (纸本)9789819780303

The proceedings contain 128 papers. The special focus in this conference is on Data Science, machine Learning and applications. The topics include: Digitization of Monuments – An Impact on the Tourist Experience with Special Reference to Hampi;resume Parser Using machine Learning;IOT Based Smart Hydroponics System;comparative Study of machine Learning and Deep Learning Techniques for Cancer Disease Detection;High Thruput Modulation Approaches Used in Next Generation WiF’s Under Multi-impairments Environments with MATLAB Codes;skin Disease Detection;root Vegetable Crop Recommendation System Based on Soil Properties and Environmental Factors;deep Learning Model Development for an Automatic Healthcare Edge Computing Application;Empathetic Conversations in Mental Health: Fine-Tuning LLMs for Supportive AI Interactions;exploring Block Chain Technology with applications, and Future Prospects;a Comprehensive Review of Soft Computing Enabled Techniques for IoT Security: State-of-the-Art and Challenges Ahead;Performance Analysis of machine Learning Algorithms on Imbalanced Datasets Using SMOTE Technique;An AI Based Nutrient Tracking and Analysis System;power Saving Mechanism for Street Lights System Using IoT;Automatic Login System Using ATTINY85 IC;forecasting Stock Prices: A Comparative Analysis of machine Learning, Deep Learning, and Statistical Approaches;smart vision Bot;robots in Logistics: Apprehension of Current Status and Future Trends in Indian Warehouses;smart Healthcare: Enhancing Patient Well-Being with IoT;Detection of B-ALL Using CNN Model and Deep Learning;a Comprehensive Analysis for Advancements and Challenges in Deep Learning Models for image processing;a Comprehensive Survey on Enhancing Patient Care Through Deep Learning and IoT-Enabled Healthcare Innovations;attention-Based image Caption Generation.

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Video Denoising using Temporal Coherency of Video Frames and Sparse Representation 12

Video Denoising using Temporal Coherency of Video Frames and...

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12th Iranian/2nd International conference on machine vision and image processing, MVIP 2022

作者： Torkashvand, Azadeh Behrad, Alireza Shahed University Electrical Engineering Department Tehran Iran

ISBN: (纸本)9781665412162

Sparse representation based on dictionary learning has been widely used in many applications over the past decade. In this article, a new method is proposed for removing noise from video images using sparse representation and a trained dictionary. To enhance the noise removal capability, the proposed method is combined with a block matching algorithm to take the advantage of the temporal dependency of video images and increase the quality of the output images. The simulations performed on different test data show the appropriate response of the proposed algorithm in terms of video image output quality. © 2022 IEEE.

关键词： image reconstruction

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All-in-One image Coding for Joint Human-machine vision with Multi-Path Aggregation 38

All-in-One Image Coding for Joint Human-Machine Vision with ...

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38th conference on Neural Information processing Systems, NeurIPS 2024

作者： Zhang, Xu Guo, Peiyao Lu, Ming Ma, Zhan School of Electronic Science and Engineering Nanjing University China

image coding for multi-task applications, catering to both human perception and machine vision, has been extensively investigated. Existing methods often rely on multiple task-specific encoder-decoder pairs, leading to high overhead of parameter and bitrate usage, or face challenges in multi-objective optimization under a unified representation, failing to achieve both performance and efficiency. To this end, we propose Multi-Path Aggregation (MPA) integrated into existing coding models for joint human-machine vision, unifying the feature representation with an all-in-one architecture. MPA employs a predictor to allocate latent features among task-specific paths based on feature importance varied across tasks, maximizing the utility of shared features while preserving task-specific features for subsequent refinement. Leveraging feature correlations, we develop a two-stage optimization strategy to alleviate multi-task performance degradation. Upon the reuse of shared features, as low as 1.89% parameters are further augmented and fine-tuned for a specific task, which completely avoids extensive optimization of the entire model. Experimental results show that MPA achieves performance comparable to state-of-the-art methods in both task-specific and multi-objective optimization across human viewing and machine analysis tasks. Moreover, our all-in-one design supports seamless transitions between human- and machine-oriented reconstruction, enabling task-controllable interpretation without altering the unified model. Code is available at https://***/NJUvision/MPA. © 2024 Neural information processing systems foundation. All rights reserved.

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AI Based Automated Gym Trainer Using machine Learning 10

AI Based Automated Gym Trainer Using Machine Learning

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10th International conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Cholaraja, K. Gururaj, M. Swasthika, V. Gnanasambandam, S.R. Sri Eshwar College of Engineering Department of Computer Science and Business Systems Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

The 'Smart Exercise Counter using Computer vision' is a groundbreaking system that blends cutting-edge computer vision technology with exercise monitoring. In an age where fitness and health are paramount, this innovative solution addresses the limitations of conventional fitness tracking methods by offering real-time, accurate feedback and comprehensive data analysis. By utilizing computer vision algorithms, this system tracks and analyzes the movements of individuals during their exercise routines, enhancing exercise counting, form assessment, and overall workout efficacy. Key components of the system include strategically positioned cameras, real-time image processing, machine learning models for exercise recognition, and biomechanically-informed form analysis. The system not only counts repetitions but also evaluates posture and technique, providing immediate feedback and corrections. Users can access their exercise data and progress through an intuitive mobile app or web dashboard, making it accessible and user-friendly. The applications of this technology extend beyond personal fitness, encompassing healthcare, sports training, and gym facilities. Healthcare professionals can employ it for rehabilitation and therapy, while athletes and coaches can refine training regimens. Gyms can enhance member experiences by offering advanced exercise monitoring. The 'Smart Exercise Counter using Computer vision' embodies the future of exercise monitoring, where technology aligns with health and fitness goals, offering a promising path towards more accurate, effective, and enjoyable workouts. This abstract encapsulates the transformative potential of computer vision in shaping the way we approach physical exercise and well-being. © 2024 IEEE.

关键词： Training Computer vision Accuracy Tracking Medical treatment machine learning Real-time systems Mobile applications Monitoring Sports

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Generalizing Functional Error Correction for Language and vision-Language Models 23

Generalizing Functional Error Correction for Language and Vi...

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23rd IEEE International conference on machine Learning and applications, ICMLA 2024

作者： Peng, Wenyu Zheng, Simeng Baluja, Michael Xie, Tao Jiang, Anxiao Siegel, Paul H. University of California San Diego Electrical and Computer Engineering Department La Jolla CA92093 United States San Diego State University San DiegoCA92182 United States Texas A&m University Department of Computer Science and Engineering College StationTX77843 United States

ISBN: (纸本)9798350374889

The goal of functional error correction is to preserve neural network performance when stored network weights are corrupted by noise. To achieve this goal, a selective protection (SP) scheme was proposed to optimally protect the functionally important bits in binary weight representations in a layer-dependent manner. Although it showed its effectiveness in image classification tasks on some relatively simple networks such as ResNet-18 and VGG-16, it becomes inadequate for emerging complex machine learning tasks generated from natural language processing and vision-language association domains. To solve this problem, we extend the SP scheme in three directions: task complexity, model complexity, and storage complexity. Extensions to complex natural language and vision-language tasks include text categorization and 'zero-shot' textual classification of images. Extensions to more complex models with deeper block structures and attention mechanisms consist of Very Deep Convolutional Neural Network (VDCNN) and Contrastive Language-image Pre-Training (CLIP) networks. Extensions to more complex storage configurations focus on distributed storage architectures to support model parallelism. Experimental results show that the optimized SP scheme preserves network performance in all of these settings. The results also provide insights into redundancy-performance tradeoffs, generalizability of SP across datasets and tasks, and robustness of partitioned network architectures. © 2024 IEEE.

关键词： Visual languages

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Novel View Synthesis from a Single RGBD image for Indoor Scenes 2

Novel View Synthesis from a Single RGBD Image for Indoor Sce...

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2nd International conference on image processing, Computer vision and machine Learning, ICICML 2023

作者： Hetang, Congrui Wang, Yuping Carnegie Mellon University PittsburghPA15213 United States University of Michigan Ann ArborMI48109 United States

ISBN: (纸本)9798350331417

In this paper, we propose an approach for synthesizing novel view images from a single RGBD (Red Green Blue-Depth) input. Novel view synthesis (NVS) is an interesting computer vision task with extensive applications. Methods using multiple images has been well-studied, exemplary ones include training scene-specific Neural Radiance Fields (NeRF), or leveraging multi-view stereo (MVS) and 3D rendering pipelines. However, both are either computationally intensive or non-generalizable across different scenes, limiting their practical value. Conversely, the depth information embedded in RGBD images unlocks 3D potential from a singular view, simplifying NVS. The widespread availability of compact, affordable stereo cameras, and even LiDARs in contemporary devices like smartphones, makes capturing RGBD images more accessible than ever. In our method, we convert an RGBD image into a point cloud and render it from a different viewpoint, then formulate the NVS task into an image translation problem. We leveraged generative adversarial networks to style-transfer the rendered image, achieving a result similar to a photograph taken from the new perspective. We explore both unsupervised learning using CycleGAN and supervised learning with Pix2Pix, and demonstrate the qualitative results. Our method circumvents the limitations of traditional multi-image techniques, holding significant promise for practical, real-time applications in NVS. © 2023 IEEE.

关键词： 3D reconstruction Generative Adversarial Network image Style Transfer Novel View Synthesis

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Optimization of Aquaponics System Efficiency Based on Artificial Intelligence Approach 3

Optimization of Aquaponics System Efficiency Based on Artifi...

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3rd International conference on Computing and machine Intelligence (ICMI)

作者： Liu, Jayden Jiang, William Harker Sch San Jose CA 95124 USA

ISBN: (纸本)9798350372977;9798350372984

This paper aims to design and implement an MLbased approach to learn from NeuroAqua - the AI and IoT-based aquaponics system set up in our previous research at both a lab setting and larger-scale Ouroboros Aquaponics Farm (Half Moon Bay, CA) to enhance system stability and efficiency. Utilizing the data gathered from the wireless sensors, a structured database was formed to store the aquaponics environmental conditions, water quality, nutrient components, and plant images. We used the ML model to find the important factors having the largest impact on plant growth and their optimal amount levels. First, computer vision with image processing was applied to develop auto plant growth monitoring and to measure plant growth rate as the target variable more accurately and automatically for ML. Then feature engineering on the input variables was performed to enhance model performance and accuracy for a smaller dataset. ML algorithms including Linear Regression, Bagging Regressor, Decision Tree, Random Forest, XGBoost and Artificial Neural Network were applied and evaluated based on key performance metrics. The findings show that XGBoost outperformed the other models with 91.6% accuracy and also had the lowest MAE. Random Forest came in second with 90.9% accuracy and then Bagging Regressor in third with 88.5% accuracy. Lastly, according to the feature importance analysis conducted on the best model of XGBoost, Nitrogen had the largest impact on plant growth, followed by Nitrate, Nitrite, Light, and Phosphorus. Hence the initial results would recommend to closely monitor these top important factors together with plant growth in NeuroAqua's monitoring applications.

关键词： Artificial Intelligence machine Learning Aquaponics IoT image processing

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A Weed Control Approach in Christmas Tree Production Based on Tree Crown Detection Using Remote Sensing and Deep Learning 8

A Weed Control Approach in Christmas Tree Production Based o...

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conference on Optics, Photonics, and Digital Technologies for Imaging applications viii

作者： Hardersa, Leif O. Ufer, Thorsten Wrede, Andreas Hussmanna, Stephan Hartung, Eberhard West Coast Univ Appl Sci Fritz Thiedemann Ring 20 D-25746 Heide Germany Chamber Agr Dept Hort Thiensen 16 D-25373 Ellerhoop Germany Univ Kiel Inst Agr Engn Max Eyth Str 6 D-24118 Kiel Germany

ISBN: (纸本)9781510673151;9781510673144

Advanced information technologies, such as the fifth-generation mobile networks (5G), unmanned aerial vehicles (UAV), and artificial intelligence (AI), build the foundation for integrated autonomous systems in smart agriculture that contribute to the sustainable transformation and optimization of agricultural processes, such as weed control. Weed control is a particular challenge for specialty crops, as it is labor-intensive, and the widely used chemical weed control is increasingly subject to legal restrictions. A new Regulation on the Sustainable Use of Plant Protection Products was adopted by the European Commission in 2022 that targets to reduce the use of chemical pesticides by 50% by 2030. This paper proposes a sustainable weed control approach based on tree crown detection using remote sensing data and evaluates the state-of-the-art deep learning architectures YOLOv4-tiny, YOLOv4-tiny-3l, and YOLOv7-tiny using a five-fold cross-validation with image inputs sizes of 832 x 832 and 1152 x 1152 pixels. The deep learning architectures are trained and evaluated with a custom dataset of 63 individual UAV-based images with 1,380 transplanted three-year-old Christmas trees under normal production conditions. The selected deep learning models achieve an average precision (AP) at an intersection over union (IOU) threshold of 0.5 above 97%, showing that all selected architectures are suited for this specific application. Neither a significant interaction effect nor significant main effects of the deep learning architecture or the image input size could be observed on the AP.

关键词： deep learning remote sensing weed control machine vision unmanned aerial vehicles (UAV) horticulture Christmas tree

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3rd International conference on machine vision and Augmented Intelligence, MAI 2023

3rd International Conference on Machine Vision and Augmented...

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3rd International conference on machine vision and Augmented Intelligence, MAI 2023

ISBN: (纸本)9789819743582

The proceedings contain 76 papers. The special focus in this conference is on machine vision and Augmented Intelligence. The topics include: Survey on Robustness of Deep Learning Techniques on Adversarial Attacks in WBAN;synergizing Collaborative and Content-Based Filtering for Enhanced Movie Recommendations;exploring Transformer-Based Approaches for Hyperspectral image Classification: A Comparative Analysis;deep Learning for Cognitive Task and Seizure Classification with Hilbert–Huang Transform and Variational Mode Decomposition;tracking of Ship and Plane in Satellite Videos Using a Convolutional Regression Network with Deep Features;Tumor Detection and Analysis from Brain MRI images Using Deep Learning;software Maintenance Prediction Using Stack Ensemble Deep Learning Algorithms;resource Allocation in 6G Network for High-Speed Train Using D2D Outband Communication;controlling the Band-to-Band Tunneling Effect in Charge Plasma Based Dopingless Transistor;Comparison of Different CIC Filter Architectures on the Basis of a Novel Parameter Called Noise Factor for Sigma-Delta Based ADCs;the Scientific Analysis on Effective Yoga Posture Recognition Techniques;impact of Gamma Rays on Emerging Devices for Photonic applications;shaft Rotation Monitoring Using Radar Signal processing and Wavelet Transform;gysel Power Divider Miniaturization Using an Inter-Digital Capacitor-Based Slow-Wave Structure;noise Estimation and Removal in Fundus images Using Pyramid Real image Denoising Network;evaluation of Hybrid Encryption Method to Secure Healthcare Data;multimodal Face Recognition System Using Hybrid Deep Learning Feature;Classification of Copy and Move image by Using HELM-FSK Method: An Efficient Approach;analysis of Energy Efficient Smart Home Based on IoT System;role of Explainable Artificial Intelligence Approaches in Cybersecurity.

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Transmission Line Inspection image Intelligent Diagnosis System 2

Transmission Line Inspection Image Intelligent Diagnosis Sys...

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2nd IEEE International conference on image processing and Computer applications, ICIPCA 2024

作者： Zheng, Bangzhu Liu, Haitao Ma, Haiteng Liu, Yin Shen, Xingyi Liang, Hua Wen, Jie Guangdong Power Grid Co. Ltd. Maoming Power Supply Bureau Guangdong Province Maoming City525000 China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350360240

This paper introduces the image diagnosis technology of power inspection based on computer vision. image import, database access, text output and other functional modules are designed using VisualStudio2010. ADO technology is used to access and change the database. The framework and method of diagnosis result fusion based on multidimensional fusion are proposed. The fault diagnosis criterion of the system is established by using genetic algorithm. Under the premise of ensuring the optimal solution, the key information is retained and the minimum expression of knowledge is obtained, so as to realize the rapid and accurate fault diagnosis. The experimental results show that the method is effective, fast and suitable for large-scale power grid, especially for transmission lines. Low error rate and friendly man-machine interface. It can meet the needs of power line inspection. The automatic inspection of the transmission line image is realized, which standardizes the working process and greatly improves the working efficiency. © 2024 IEEE.

关键词： Power distribution lines

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