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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems VI"

共 15613 条记录，以下是771-780 订阅

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Development of an Intelligent Robotic System for Recognizing and Transporting Postal Objects

Development of an Intelligent Robotic System for Recognizing...

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2023 International conference "Quality Management, Transport and Information Security, Information Technologies", IT and QM and IS 2023

作者： Khokhlova, Mariya V. Barsuk, Igor V. Voronova, Liliya I. Belov, Nikita V. Technical University of Communications and Informatics Department of Intelligent Control Systems and Automation Moscow Moscow Russia

ISBN: (纸本)9798350340532

Now computer science methods are actively used in different areas of Industry 4.0., including in the postal service to automate the transportation of postal items. The problem of transportation is particularly acute for postal objects of large volumes and weights, as it is performed manually and is one of the most time-consuming operations. The article describes the development of a prototype intelligent robotic system with computer vision to recognize and transport such postal objects (parcels). An analysis of modern scientific and practical developments, methods and means of image processing, including methods of contour analysis, and video stream processing algorithms is carried out. The stages of design and integration of the computer vision subsystem into the robotic complex are described. © 2023 IEEE.

关键词： Computer vision

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DeTrack: In-model Latent Denoising Learning for visual Object Tracking 38

DeTrack: In-model Latent Denoising Learning for Visual Objec...

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38th conference on Neural Information processing systems, NeurIPS 2024

作者： Zhou, Xinyu Li, Jinglun Hong, Lingyi Jiang, Kaixun Guo, Pinxue Ge, Weifeng Zhang, Wenqiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Shanghai Engineering Research Center of AI & Robotics Academy for Engineering and Technology Fudan University Shanghai China

Previous visual object tracking methods employ image-feature regression models or coordinate autoregression models for bounding box prediction. image-feature regression methods heavily depend on matching results and do not utilize positional prior, while the autoregressive approach can only be trained using bounding boxes available in the training set, potentially resulting in suboptimal performance during testing with unseen data. Inspired by the diffusion model, denoising learning enhances the model's robustness to unseen data. Therefore, We introduce noise to bounding boxes, generating noisy boxes for training, thus enhancing model robustness on testing data. We propose a new paradigm to formulate the visual object tracking problem as a denoising learning process. However, tracking algorithms are usually asked to run in real-time, directly applying the diffusion model to object tracking would severely impair tracking speed. Therefore, we decompose the denoising learning process into every denoising block within a model, not by running the model multiple times, and thus we summarize the proposed paradigm as an in-model latent denoising learning process. Specifically, we propose a denoising vision Transformer (viT), which is composed of multiple denoising blocks. In the denoising block, template and search embeddings are projected into every denoising block as conditions. A denoising block is responsible for removing the noise in a predicted bounding box, and multiple stacked denoising blocks cooperate to accomplish the whole denoising process. Subsequently, we utilize image features and trajectory information to refine the denoised bounding box. Besides, we also utilize trajectory memory and visual memory to improve tracking stability. Experimental results validate the effectiveness of our approach, achieving competitive performance on several challenging datasets. The proposed in-model latent denoising tracker achieve real-time speed, rendering denoising learning

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Application of Color Filtering and image Filtering algorithms in Circuit Diagnosis of Smart Meter Box 22

Application of Color Filtering and Image Filtering Algorithm...

引用

7th International conference on Multimedia systems and Signal processing, ICMSSP 2022

作者： Chen, Ming Ji, Chengyue Mao, Dilin Wang, Yu-Nan Ma, Yuan Cao, Xiu State Grid Shanghai ShiNan Electric Power Supply Company China Fudan University China

ISBN: (纸本)9781450396424

In order to identify the right or wrong wiring of the smart meter box, a hybrid model composed of the object detection model and the image classification model was built to detect the key parts of the smart meter box and identify the right or wrong wiring. The main work of this paper is to introduce color filtering, corrosion, expansion and Gaussian filtering and other image processing algorithms before the photos enters the image classification algorithm to identify the right or wrong wiring to filter out irrelevant components such as air switches and wiring terminals in the meter box photos, and keep the key features of the line. Experiments show that after the image is processed by color filtering and image filtering, the subsequent model training process converges faster, and the recognition accuracy is increased from 93.9% to 98.78%, which effectively improves the efficiency of model training and the accuracy of image recognition. © 2022 ACM.

关键词： image classification

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Comprehensive Weather Recognition Using ResNet-50 and a Novel Weather image Dataset 4

Comprehensive Weather Recognition Using ResNet-50 and a Nove...

引用

4th International Signal processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Wang, Jun Zhou, Yunhan Fu, Min School of Electrical and Electronics Engineering UESTC Chengdu China Cyrinn Science & Technology Ltd. Guangzhou China

ISBN: (纸本)9798331528676

Weather conditions play a crucial role in both daily human activities and industrial operations. For example, recognizing different weather patterns is critical for outdoor automation systems. With the development of AI technology, scientific researchers have trained many models which receive weather images as inputs and output the weather in these images fast and accurately. However, many existing weather recognition models are limited in scope, typically identifying only four types of weather with varying degrees of accuracy. In this study, the two key contributions of this research are: first, the creation of a custom dataset designed specifically for recognizing seven distinct weather conditions, and second, the exploration of multiple machine learning techniques to identify the most effective model for this task. By developing our own dataset, we ensure a broader representation of weather types, allowing the models to train and be tested on more diverse and realistic scenarios. We evaluate five different algorithms: Support Vector Machine (SVM), Random Forest, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and ResNet50. To enhance model generalization and reduce overfitting, we employ data augmentation techniques and add dropout layers in the CNN and ResNet50 architectures. Through extensive experimentation, ResNet50 emerges as the best-performing model, achieving an accuracy of approximately 87% on the custom dataset. This work demonstrates the importance of creating domain-specific datasets and using a range of machine learning methods to develop accurate and reliable weather recognition models. Our approach offers a robust solution for outdoor vision systems, with potential applications in autonomous vehicles, surveillance systems, and other environments where accurate weather detection is critical for safety and performance. ©2024 IEEE.

关键词： Convolutional neural networks

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Real-time Low vision Simulation in Mixed Reality 16

Real-time Low Vision Simulation in Mixed Reality

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16th International conference on Signal-image Technology and Internet-Based systems (SITIS)

作者： Acevedo, Valeria Colantoni, Philippe Dinet, Eric Tremeau, Alain Univ Jean Monnet Univ Lyon Lab Hubert Curien UMR 5516 St Etienne France

ISBN: (纸本)9781665464956

visual impairments are a global health issue with profound socioeconomic ramifications in both the developing and the developed world. There exist ongoing research projects, that aim to investigate the influence of light in the perception of low vision individuals. But as of today, there is neither clear knowledge nor extensive data regarding the influence of light in low vision situations. This research will address these issues by introducing a methodology and a system to simulate visual impairments. A pipeline based on eye anatomy coupled with real-time image processing algorithms allows to dynamically simulate low vision specific characteristics of selected impairments in mixed reality. An original new approach based on massively parallelized processing combined with an efficient modeling of eye refractive errors aims to improve the accuracy of the low vision simulation.

关键词： mixed reality low-vision simulation parallel programming image processing

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Bacterial colony detection method for microbiological photographic images

Bacterial colony detection method for microbiological photog...

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IEEE International conference on Fuzzy systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Gorokhov, Oleg Fazylov, Ramazan Kazachuk, Maria Lazukhin, Ivan Mashechkin, Igor Pankratyeva, Liudmila Popov, Ivan Lomonosov Moscow State Univ MSU Comp Sci Dept Moscow Russia Dmitry Rogachev Natl Med Res Ctr Pediat Hematol Moscow Russia

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

This paper is dedicated to the research and development of automated methods for detecting bacterial colonies on photographic images of Petri dishes. We propose a segmentation algorithm for microbiological photographic images which is based on the convolutional neural network architecture of the encoder-decoder class, trained on artificially generated image data. Our method consists of two stages: sliding window image processing by the proposed neural network to find areas containing isolated objects potentially suitable for further research, and further segmentation of the resulting binary area map into regions containing colonies by classical computer vision algorithms. According to the experimental research, the developed algorithm shows the best results in comparison with the existing neural network algorithms we adapt in this work as well as watershed segmentation algorithms. The work also proposes algorithms for microbiological photographic images artifact searching based on heuristic and neural network approaches (including flare, magnetic balls, barcodes and condensate), the use of which allows to increase the quality of the image segmentation algorithm, as well as to find errors caused by the human factor. The results of this work might be further used for classification algorithms of the bacterial colonies in the segments found, which will allow in some cases fully automated process of visual bacteria recognition on Petri dishes photographic images.

关键词： microbiological analysis bacterial colony detection artifact detection object detection computer vision machine learning data mining image segmentation heuristic algorithms neural networks Petri dish

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Musa Base - Mobile Application for Banana Farmers to Minimize the Challenges in Banana Production during the Pre-Harvest and Post-Harvest Period

Musa Base - Mobile Application for Banana Farmers to Minimiz...

引用

2023 International conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2023

作者： Kavinda, G.H.M. Dissanayake, D.D. Nanayakkara, K.A.K.K. Swrnakantha, N.H.P. Ravi Supunya Liyanagolla, Mudiyanselage. N. V. K. Kumari, P.K.S. Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350319200

This research paper presents an innovative solution to address the multifaceted challenges encountered by banana farmers in Sri Lanka, encompassing the entire spectrum of banana production from pre-harvest to post-harvest stages. The proposed system, titled "Musa-Base,"is a user-friendly mobile application that seamlessly integrates cutting-edge technologies, including machine learning, IoT-based approaches, natural language processing, and image processing methodologies. This holistic approach aims to elevate the quality and productivity of banana cultivation. The Musa-Base system comprises several interlinked modules: crop yield prediction, disease identification through advanced image processing and interactive chatbot interactions, personalized recommendations for optimal watering and fertilizer strategies, and comprehensive guidance on best agronomic practices. The research rigorously employs a diverse range of algorithms, including deep learning models, for accurate crop yield prediction, rapid disease diagnosis, and finely tuned recommendation systems. The system empowers farmers with informed decision-making capabilities, enabling them to mitigate losses, efficiently allocate resources, and ultimately enhance banana production and product quality. The integration of images of banana plants, sensor data, and user input data forms the backbone of the data-driven research methodology that underpins the development of the Banana Mobile Application. The pivotal outcomes of this research include precise disease detection, reliable harvest forecasting, and the dynamic integration of a chatbot for timely assistance, all of which collectively contribute to superior banana crop management, heightened agricultural efficiency, and enhanced sustainability. This comprehensive solution caters to the needs of both smallholder farmers and commercial producers, representing a significant advancement in modernizing banana farming practices and ensuring food security. © 2023 IE

关键词： Banana Farming Banana quality Banana symptoms Disease detection Fertilization technique Harvest prediction image processing IoT-based technologies Machine learning Mobile application post-harvest practices Pre-harvest practices Watering plans

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An algorithm for parallel processing of traffic signs video on a graphics processor

An algorithm for parallel processing of traffic signs video ...

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2022 International conference on Information Science and Communications Technologies, ICISCT 2022

作者： Umarov, Mukhriddin Elov, Jamshid Khalilov, Sirojiddin Narzullayev, Inomjon Karimov, Marat Samarkand Br. of Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Dept. Department of Software Engineering Samarkand Uzbekistan Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Dept. Department of Hardware and Software of Management Systems in Telecommunication Tashkent Uzbekistan Nurafshan Branch of Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Dept. Department of Information Technologies Tashkent Uzbekistan

ISBN: (纸本)9781665472296

In the lighting conditions such as snowing, hazing, raining, and weak lighting condition, the accuracy of traffic sign recognition is not very high. It is important to develop an algorithms for real-time fast detection of road signs from video images and use them in driver assistance systems. Firstly, we image preprocessing by using dark channel prior based image dehazing for remove noises from the input image. In this article, we develop a transposed image filtering method combined with other fast parallel filtering algorithms. We propose a high-speed and high-accuracy method by improving image dehazing algorithms and analyzing GPU architecture. In this, we quickly removing fog from HD video images using DCP with image quality and visual effects. © 2022 IEEE.

关键词： image enhancement

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Development of the Decentralized Biometric Identity Verification System Using Blockchain Technology and Computer vision

Development of the Decentralized Biometric Identity Verifica...

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2024 International Russian Smart Industry conference, SmartIndustryCon 2024

作者： Uteyev, G. Gibadullin, R.F. Kazan National Research Technical University Named after A. N. Tupolev - Kai Department of Computer Systems Kazan Russia

ISBN: (纸本)9798350395044

This article investigates the creation of a decentralized system for biometric-based identity verification, integrating blockchain technology and computer vision. The goal is to provide a secure verification method, minimizing identity fraud and transactional deceptions. The system combines facial recognition, fingerprint scanning, and iris scanning, ensuring precise individual identification. Blockchain guarantees data immutability and decentralization, ensuring biometric data integrity. Computer vision enhances biometric data processing, improving system accuracy and efficiency. Key components include blockchain for secure biometric data storage via Solidity-based smart contracts, computer vision algorithms like convolutional neural networks for facial image analysis, and the OpenPGP library for asymmetric encryption. The system's backend is developed in Python using FastAPI, with a React-based frontend for user interaction. This web application is compatible with modern browsers and integrates seamlessly with existing systems. Results show the development of a novel blockchain-based decentralized identity system, employing a Solidity smart contract for encrypted biometric data storage and a React and FastAPI web application for facial image analysis. The system ensures data privacy through asymmetric encryption and has undergone extensive testing, proving significant advantages over conventional solutions. The discussion emphasizes the system's benefits in secure, efficient, and accurate identity verification, suitable for various sectors like finance, healthcare, and government services. The system's future could involve expanding biometric modalities, algorithmic optimization, and incorporating emerging technologies like the Internet of Things. © 2024 IEEE.

关键词： Cryptography

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Boosting Semi-Supervised Federated Learning with Model Personalization and Client-Variance-Reduction 48

Boosting Semi-Supervised Federated Learning with Model Perso...

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48th IEEE International conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Wang, Shuai Xu, Yanqing Yuan, Yanli Wang, Xiuhua Quek, Tony Q. S. Singapore University of Technology and Design 487372 Singapore The Chinese University of HongKong Shenzhen518172 China Beijing Institute of Technology Beijing100081 China Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781728163277

Recently, federated learning (FL) has been increasingly appealing in distributed signal processing and machine learning. Nevertheless, the practical challenges of label deficiency and client heterogeneity form a bottleneck to its wide adoption. Although numerous efforts have been devoted to semi- supervised FL, most of the adopted algorithms follow the same spirit as FedAvg, thus heavily suffering from the adverse effects caused by client heterogeneity. In this paper, we boost the semi-supervised FL by addressing the issue using model personalization and client-variance-reduction. In particular, we propose a novel and unified problem formulation based on pseudo-labeling and model interpolation. We then propose an effective algorithm, named FedCPSL, which judiciously adopts the schemes of a novel momentum-based client- variance-reduction and normalized averaging. Convergence property of FedCPSL is analyzed and shows that FedCPSL is resilient to client heterogeneity and obtains a sublinear convergence rate. Experimental results on image classification tasks are also presented to demonstrate the efficacy of FedCPSL over the benchmark algorithms. © 2023 IEEE.

关键词： Learning systems

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