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

共 16440 条记录，以下是1891-1900 订阅

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Application and Research of Dark Channel Defogging Algorithm in Video Logging image Enhancement 6

Application and Research of Dark Channel Defogging Algorithm...

引用

6th IEEE International conference on Intelligent Computing and Signal processing, ICSP 2021

作者： Dong, Haifeng Ma, Ruini Xi'an Shiyou University School of Computer Science Xi'an710065 China

ISBN: (纸本)9780738143705

The video logging technology is applied to detect the general condition of the well. Due to the influence of water fog, the contrast of the video logging image is decreased and the color contrast is distorted. The application and research of the dark channel dehazing algorithm in the video logging image enhancement system is proposed. In this paper, the dark channel defogging algorithm and Retinex defogging algorithm are compared and analyzed respectively, the realization process and application of the two algorithms are described briefly, and the advantages and disadvantages of the two algorithms applied in video logging image enhancement are compared. © 2021 IEEE.

关键词： image enhancement

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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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Auxiliary Particle Filtering with Variational Inference for Jump Markov systems with Unknown Measurement Noise Covariance

Auxiliary Particle Filtering with Variational Inference for ...

引用

European Signal processing conference (EUSIPCO)

作者： Cheng Cheng Sinan Yildirim Jean–Y Ves Tourneret School of Astronautics Northwestern Polytechnical University Xi’ an China Faculty of Engineering and Natural Sciences Sabanci University Istanbul Turkey ENSEEIHT- IRIT- TéSA University of Toulouse Toulouse France

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

This paper studies an auxiliary particle filter with variational inference for jointly estimating the system mode, the state and the measurement noise covariance matrix of jump Markov systems. The joint posterior distribution of the system mode, the state and the noise covariance matrix is marginalized out with respect to the system mode. The marginalized posterior distribution of the mode is then approximated by using an auxiliary particle filter, and the state and noise covariance matrix conditionally on each particle of the mode variable are updated using variational Bayesian inference. A simulation study is conducted to compare the proposed method with state-of-the-art approaches for a target tracking scenario.

关键词： Linear systems Target tracking Atmospheric measurements Noise Signal processing algorithms Particle measurements Particle filters Noise measurement Covariance matrices Nonlinear systems

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Learning to Follow Infrared Prior Repersentation for image Dehazing

Learning to Follow Infrared Prior Repersentation for Image D...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yiquan Du Ming Yang Haiyang Huo Jiaqi Shi Jiangang Ding Lili Pei School of Information Engineering Chang’an University Xi’an China School of Data Science and Artificial Intelligence Chang’an University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The infrared image can distinguish the targets from the background based on radiation differences, providing more significant target visibility under dense haze. Fusion of visible haze images with infrared prior representations can generate high-quality fused images for high-level tasks. Consequently, we propose a novel dual-modal fusion network structure that makes full use of infrared prior representations for dehazing. Specifically, we emphasize a Multi-modal Feature Extraction Network (MMFE) to extract deep multi-scale features. Meanwhile, we introduce a Multi-scale Feature Extraction module (MSFE), integrating an Efficient Dual Attenion block (EDAB) to efficiently explore more spatial and marginal information. Additionally, we propose a new feature fusion strategy, which calculates feature fusion weights based on an adaptive multi-head self-attention. Therefore, IPRDehazeNet achieves better dehazing results through dual-modal fusion. Experimental results indicate that IPRDehazeNet outperforms various advanced methods.

关键词： Adaptive systems Fuses Signal processing Feature extraction Acoustics Speech processing

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Design of an Arm Posture Recognition System and Adaptive TENS Therapeutic Device Based on sEMG Signals

Design of an Arm Posture Recognition System and Adaptive TEN...

引用

2024 8th International conference on Electrical, Mechanical and Computer Engineering (ICEMCE)

作者： Yinchao Li Linyang Ma Weiwei Yue Fei Chen School of Physical and Electronic Sciences Shandong Normal University Jinan Shandong China

ISBN: (数字)9798331506230

ISBN: (纸本)9798331506247

This study presents the design of a novel arm posture recognition and adaptive transcutaneous electrical nerve stimulation (TENS) physiotherapeutic device utilizing surface electromyographic (EMG) signals. The system employs high-precision EMG sensors for real-time monitoring of users' arm EMG signals, alongside sophisticated signal processing algorithms to facilitate posture recognition. Unlike conventional physiotherapy methods, this innovative design incorporates an adaptive adjustment feature that dynamically modulates the electrical stimulation intensity and frequency based on the user's myoelectric feedback, thereby optimizing therapeutic efficacy. Furthermore, it integrates lightweight algorithmic deployment of embedded artificial intelligence, enhancing its usability. This system holds significant potential for application in rehabilitation medicine, sports medicine, and geriatric care, with the aim of improving treatment precision and comfort while aiding in the restoration and maintenance of arm functionality.

关键词： Adaptive systems Accuracy Signal processing algorithms Electrical stimulation Electromyography Real-time systems Sensor systems Sensors Surface treatment Sports

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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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