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

共 12901 条记录，以下是1071-1080 订阅

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Small-Capacity Liquid Density Detection Device Based on Computer Vision

Small-Capacity Liquid Density Detection Device Based on Comp...

引用

Advanced algorithms and Control Engineering (ICAACE), International conference on

作者： Renda Xing Bin Li Jie Chen Rui Liu School of Mechatronic Engineering and Automation Shanghai University Shanghai China Metrology and Testing Department Shanghai Institute of Quality Inspection and Technical Research Shanghai China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

To address the challenge of density detection for small-volume unit-labeled quantitative packaged products, a computer vision-based density detection device suitable for small-volume liquids was designed. The device integrates image processing technology with an automatic injection system and micro-volume constant-volume control technology to achieve automatic weighing and volume acquisition of volumetric flasks, thereby enabling automated density measurement of samples. Additionally, a data acquisition and processing software system was developed to minimize human reading and operational errors, improving detection efficiency and measurement accuracy. Experimental results demonstrate that the device achieves a relative expanded uncertainty of $0.00069 \mathrm{g} / \text{cm}^3$ and a measurement repeatability of $0.0005 \text{g/cm}^{3}$ , ensuring high detection precision.

关键词： Uncertainty Accuracy Liquids Software design image processing Measurement uncertainty Software systems Hardware Software measurement Thermal stability

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Exploration on Facial image Recognition and processing algorithms under Hadoop

Exploration on Facial Image Recognition and Processing Algor...

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Integrated Intelligence and Communication systems (ICIICS), International conference on

作者： Meifang Gu Hailin Tang Lili Li Institutional Research Center Guangdong Baiyun University Guangzhou Guangdong China Faculty of Intelligent Manufacturing Engineering Guangdong Baiyun University Guangzhou Guangdong China Faculty of Megadata and Computing Guangdong Baiyun University Guangzhou Guangdong China

Face recognition is an important technology in the field of digital image processing, which has a crucial impact on subsequent work. This article introduced an example of facial recognition based on the combination of Hadoop algorithm and facial feature extraction. The experimental results were validated in the MATLAB environment and trained using neural networks to improve system performance and adaptability. The test results indicated that the processing speed was between 0.84 and 0.97; the highest resource utilization rate was 0.06, and the lowest was 0.02; the throughput range of facial data was between 13598 and 15479, and the throughput range of user data was between 5037 and 5879. This indicated that the system had fast recognition speed and high-precision image output, and exhibited good fault tolerance and robustness compared to other methods. Exploring facial image recognition and processing algorithms in the Hadoop environment can utilize Hadoop’s distributed computing capabilities to accelerate the processing of large-scale facial image data, achieve fast and efficient processing of large-scale facial image data, accelerate algorithm operation, and possess distributed computing capabilities. Multiple computing nodes can be used for parallel processing to improve algorithm performance.

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Design of AXI Bus-Based IP Core for image processing 12th

Design of AXI Bus-Based IP Core for Image Processing

引用

12th CCF conference on BigData, BigData 2024

作者： Chen, Yutong Yi, Zhongchao Li, Xuqiang Chen, Xingyan Chen, Yanjiang School of Software Engineering University of Science and Technology Hefei China School of Computer Science and Technology University of Science and Technology Hefei China

ISBN: (纸本)9789819610235

This study addresses the significant issue of image blur through image restoration techniques that reduce or remove blur and detect blurry regions, improving image quality and analysis potential. Utilizing FPGA-based digital hardware systems, the research leverages FPGA-supported systems like ZYNQ and ARM, using the Advanced eXtensible Interface (AXI) and Advanced Microcontroller Bus Architecture (AMBA) for streamlined device communication. The design incorporates specialized Intellectual Property (IP) cores such as Square Root (Sqrt) and Shift RAM, tailored to overcome the limitations of development cycles and processing algorithms, focusing on portability across various platforms including domestic FPGAs. The practical implementation features modules for axis-stream transmission, RGB to YUV conversion, image smoothing using Gaussian, mean, and median filters, advanced image processing with Sobel edge detection, and erosion-dilation modules. A ZYNQ verification system with an OV5640 camera and HDMI output substantiates the functionality and efficiency of the designed IP core, which is further analyzed for resource and power consumption using a High-Level Synthesis (HLS) tool. The study successfully demonstrates a robust IP core for blurry image processing, significantly enhancing the field of image processing and embedded systems, laying a strong groundwork for future advancements and porting endeavors, and highlighting the efficacy of FPGA-based image processing solutions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Median filters

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Vehicle Number Plate Recognition and Information Access System

Vehicle Number Plate Recognition and Information Access Syst...

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Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI), International conference on

作者： Pradeep B. Mane Varsha Kiran Patil Mayur R. Paithankar Mallikarjun M. Kotgire Samveg S. Shah Dept. Electronics and Telecommunication AISSMS Institute of Information Technology Pune India

ISBN: (数字)9798350355338

ISBN: (纸本)9798350355345

Addressing the growing challenges posed by increasing vehicles and stringent traffic violations, many vehicle number plates go undetected while passing through toll booths and traffic lights. Thus it creates the need for a vehicle number plate recognition system. Existing systems employing ANPR algorithms utilize MATLAB and K-Nearest neighbour algorithms for image processing. Drawbacks include limitations in adapting to dynamic environments, Real time recognition, handling multiple vehicles, slow processing speeds, incapability of multiple object detection in single frame. The paper focuses on Automatic Number Plate Recognition (ANPR), employing specialized cameras and advanced image processing and using YOLOv8 for object detection and extracting license plate information from video feeds using EasyOCR. The noisy data generated during character recognition is filtered out using Regex based algorithm. The system integrates with centralized databases for real-time vehicle information access for users. The same database is used to automate e-challan updation, contributing to traffic management by swiftly issuing fines for rule violations. The proposed video-based processing system achieves number plate detection using YOLOv8 and storage of vehicle number plates in a centralized MongoDB database with an accuracy achieved of 96% in license plate detection and reduces noisy data using algorithm. It also enables real-time data retrieval through a user interface using Reactjs as frontend and is also connected to the MongoDB database for precise and user-friendly access in modern traffic systems.

关键词： image recognition Databases Heuristic algorithms Object detection Filtering algorithms Information filters Real-time systems Character recognition Noise measurement License plate recognition

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SEMANTIC SEGMENTATION IN DEPTH DATA : A COMPARATIVE EVALUATION OF image AND POINT CLOUD BASED METHODS

SEMANTIC SEGMENTATION IN DEPTH DATA : A COMPARATIVE EVALUATI...

引用

IEEE International conference on image processing (ICIP)

作者： Katrolia, Jigyasa Singh Kraemer, Lars Rambach, Jason Mirbach, Bruno Stricker, Didier DFKI Kaiserslautern Germany Tech Univ Kaiserslautern Kaiserslautern Germany

ISBN: (纸本)9781665441155

The problem of semantic segmentation from depth images can be addressed by segmenting directly in the image domain or at 3D point cloud level. In this paper, we attempt for the first time to provide a study and experimental comparison of the two approaches. Through experiments on three datasets, namely SUN RGB-D, NYUdV2 and TICaM, we extensively compare various semantic segmentation algorithms, the input to which includes images and point clouds derived from them. Based on this, we offer analysis of the performance and computational cost of these algorithms that can provide guidelines on when each method should be preferred.

关键词： scene segmentation depth image point cloud

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Machine Learning Based Skin Disease Detection Using Semi-Automatic image Segmentation

Machine Learning Based Skin Disease Detection Using Semi-Aut...

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Smart systems for Electrical, Electronics, Communication and Computer Engineering (ICSSEECC), International conference on

作者： J. Sathya A. Kalaivani Department of MCA New Horizon College of Engineering Bengaluru India

ISBN: (数字)9798350378177

ISBN: (纸本)9798350378184

The human skin is a remarkable structure, susceptible to numerous known and unknown diseases. Thus, diagnosing skin conditions is one of the most uncertain and complex areas in medical science, making clinical image analysis for skin diseases a formidable task. Moreover, diagnosing skin conditions manually by medical professionals is not only time-consuming but also subjective. This paper presents an approach for diagnosing and categorizing skin diseases utilizing machine learning classification techniques. This approach is based on a popular Semi-automatic image segmentation algorithm known as Interactive Superpixel Segmentation. It combines manual user input with automated algorithms to partition images into meaningful regions. Morphological operations are used in this system to remove hair in image processing. Median filtering technique is employed for noise removal. Computer-based methods will be employed to analyze, process, and classify image data, taking into account different attributes of the photos under examination. To start with, skin photographs go through filtering to eliminate unwanted noise, followed by processing to enhance the overall quality of the image. Gray-Level Co-occurrence Matrix (GLCM) is implemented to extract the features from the skin disease images. Following feature extraction, the image can be classified using the K-Nearest Neighbors (KNN) algorithm, ultimately generating an evaluative report as the output. This paper summarizes the basic workflow for interactive superpixel segmentation in machine learning. Medical image dataset is taken from different websites to predict the skin diseases. This application is more efficient and producing more accuracy results comparing to others.

关键词： image segmentation Machine learning algorithms Machine learning Nearest neighbor methods Feature extraction Skin Classification algorithms

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Design of an Improved Dish Recognition Algorithm Based on image Retrieval

Design of an Improved Dish Recognition Algorithm Based on Im...

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Machine Vision, image processing and Imaging Technology (MVIPIT), International conference on

作者： Yi Yu Jie Liu Archival Sebial Angie Ceniza Canillo Jiangxi Vocational College of Industry and Engineering China Department of Computer Information Sciences and Mathematics School of Arts and Sciences University of San Carlos Cebu Philippines

ISBN: (数字)9798331543037

ISBN: (纸本)9798331543044

With the booming of the new retail industry, unmanned billing systems have become the key to improve the operational efficiency of restaurants and reduce labour costs. In the field of payment and billing, traditional recognition systems based on deep neural networks often rely on a single network to achieve the detection and classification of Chinese dishes, but in the face of the increase or decrease in the number of dishes, it is necessary to frequently adjust the type of the network output and re-collect a large amount of image data for training, which leads to high update costs. In this study, a deep learning EfficientNet convolutional neural network combined with image retrieval for dish recognition is used to solve this problem. Firstly, EfficientNet, an advanced dish detection network, is used to accurately locate the positions of multiple dishes in an image, determine their orientations, and effectively separate the dishes from the background. Subsequently, the EfficientNet based feature extraction network calculates the image features of the separated dishes and uses cosine similarity as a metric to efficiently retrieve the dishes in the reference dish library, thus obtaining the classification results of the dishes. Finally, the system outputs the precise location and category information of the dishes. Experimental results show that the EfficientNet and cosine similarity approach achieves an average accuracy of 98.3% on the dish image dataset of China Food Network, which not only significantly shortens the updating time compared with traditional algorithms, but also requires only 1/24 of the amount of image data of the traditional deep learning classification methods. This breakthrough effectively solves the problem of the difficulty and high cost of updating the current dish recognition algorithms. This breakthrough effectively solves the current problem of difficult and costly updating of dish recognition algorithms, and is expected to significantly red

关键词： Industries Deep learning Training image recognition Costs Accuracy Face recognition image retrieval Feature extraction Classification algorithms

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Enhancing Structural Health Monitoring: AI-Driven image processing for Automated Crack Identification in Concrete Surfaces

Enhancing Structural Health Monitoring: AI-Driven Image Proc...

引用

Electronics and Sustainable Communication systems (ICESC), 2020 International conference on

作者： S. Jency G. Ramkumar Department of ECE Saveetha School of Engineering SIMATS Chennai Technology Chennai

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

One of the first signs of structural deterioration is cracks in the concrete surface, which is important for maintenance because prolonged exposure will seriously harm the environment. The highly regarded method for inspecting cracks is manual inspection. During the handwritten inspection, the irregularities' conditions are noted and a hand-drawn illustrate of the crack is created. One of the main problems on large construction sites is the degradation of structures as a result of cracks, especially in cases where inspection by hand is not feasible. Thus, an alternative called automatic image-based crack detection is suggested. The primary goal of this research work is to use artificial intelligence-based image processing techniques to identify and categorize cracks. Here, this study proposes five modules. The first module collects the dataset of crack images; the second module applies a pre-processing method to filter the data; the third module divides the data into training and testing portions; and the fourth module implements a model that uses both deep learning, such as the Mobile-net algorithm, and machine learning, such as the Random Forest algorithm, for testing and training the data. The last part is the prediction module, which forecasts whether a crack image will be positive or negative. Lastly, this study compares the accuracy score, recall, precision, loss graph, F1 score value, and algorithms for machine learning along with deep learning. The system performed better, as demonstrated by the experimental results.

关键词： Deep learning Training Machine learning algorithms Accuracy image processing Roads Inspection

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image Recognition and Analysis of Computer Interlock Interface Based on OpenCV Technology

Image Recognition and Analysis of Computer Interlock Interfa...

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IEEE Advanced Information Management,Communicates,Electronic and Automation Control conference (IMCEC)

作者： Xuanmo Chen Qiang Li College of Computer Science and Technology Lanzhoujiaotong University Lanzhou China Key Laboratory of Optoelectronic Technology and Intelligent Control Ministry of Education Province Lanzhou China

ISBN: (数字)9798350316537

ISBN: (纸本)9798350316544

An integrated computerized locking mechanism is vital for upholding the safety of train movements within railway networks. As computer technology progresses swiftly, conventional locking systems are encountering a multitude of difficulties, with the identification and processing of visual interface data being a key challenge. Furthermore, the pace of technological innovation has led to an array of other obstacles for these systems to surmount. The deployment of cutting-edge technologies, including AI and machine learning, has markedly enhanced the functionality and security of these mechanisms. Anticipatedly, the future will witness the development of even more sophisticated and secure railway traffic management systems. These evolving technologies serve as formidable instruments in tackling the prevailing issues and challenges. Specifically, the computerized interface locking system is indispensable in guaranteeing the safe running of trains within railway *** paper applies the image processing capability of OpenCV to preprocess and extract characteristics from the interlocking interface image. Subsequently, the enhanced clustering algorithm of RV-DBSCAN is utilized to identify the text of signaling equipment diagrams, followed by the implementation of the Paddle OCR (Optical Character Recognition) machine learning algorithm to classify and identify the images. The recognition accuracy rate for information related to railway signaling equipment stands at 92.6%. This process offers a dependable approach for extracting and recognizing station information from computerized interlocking interfaces.

关键词： Visualization Technological innovation image recognition Machine learning algorithms Optical character recognition Software algorithms Clustering algorithms

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Single image Super-Resolution Based on Enhanced Residual Neural Network 14

Single Image Super-Resolution Based on Enhanced Residual Neu...

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14th International conference on Signal processing systems, ICSPS 2022

作者： Zhao, Jie Chen, Zhenxue Liu, Chengyun Yang, Yue Ye, Mengting Zhang, Yujiao The School of Control Science and Engineering Shandong University Jinan China Information Tech Co Ltd China Mobile Comm Corp Hangzhou China Shenzhen Research Institute Shandong University Shenzhen China

ISBN: (纸本)9798350336313

Vision is an important way for humans to perceive the world around them. It is an urgent need to improve image quality by using image super resolution. Residual network plays an important role in image super resolution because of its powerful feature extraction ability. Only stacking residual network is not enough to extract a large amount of effective information, and at the same time, more training memory is needed. Therefore, this paper designs a single feature extraction module with better performance to enhance the feature extraction ability of the network. In this paper, the Residual Network is mainly improved, and a Single image Super-Resolution Based on Enhanced Residual Neural Network (ERNN) is proposed based on improved residual module. Compared with other classical lightweight algorithms, the lightweight network with the new residual module as the basic unit has higher reconstruction ability, and can achieve better image super-resolution reconstruction with a small number of parameters. Moreover, a large number of experiments have verified that the proposed ERNN can achieve the trade-off between the number of parameters and performance. © 2022 IEEE.

关键词： Feature extraction

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