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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531-540 订阅

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Predicting Autonomous Vehicle Navigation Parameters via image and image-and-Point Cloud Fusion-based End-to-End Methods

Predicting Autonomous Vehicle Navigation Parameters via Imag...

引用

IEEE International conference on Multisensor Fusion and Integration for Intelligent systems (MFI)

作者： Beycimen, Semih Ignatyev, Dmitry Zolotas, Argyrios Cranfield Univ Ctr Autonomous & Cyber Phys Syst SATM Bedford MK43 0AL England

ISBN: (数字)9781665460262

ISBN: (纸本)9781665460262

This paper presents a study of end-to-end methods for predicting autonomous vehicle navigation parameters. image-based and image & Lidar points-based end-to-end models have been trained under Nvidia learning architectures as well as Densenet-169, Resnet-152 and Inception-v4. Various learning parameters for autonomous vehicle navigation, input models and pre-processing data algorithms i.e. image cropping, noise removing, semantic segmentation for image data have been investigated and tested. The best ones, from the rigorous investigation, are selected for the main framework of the study. Results reveal that the Nvidia architecture trained image & Lidar points-based method offers the better results accuracy rate-wise for steering angle and speed.

关键词： Training Laser radar Recurrent neural networks Navigation Prediction algorithms Data models Land vehicles

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Enhancing Patient Privacy in IoT-Enabled Intelligent systems: A Deep and Broad Learning-Based Efficient Encryption Network 8

Enhancing Patient Privacy in IoT-Enabled Intelligent Systems...

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8th International conference on Smart Internet of Things

作者： Zhu, Xiaoqiang Liu, Jiqiang Zhang, Dalin Li, Lingkun Wang, Nan Beijing Jiaotong Univ Sch Cyberspace Sci & Technol Beijing 100044 Peoples R China Beijing Jiaotong Univ Beijing Key Lab Secur & Privacy Intelligent Trans Beijing 100044 Peoples R China

ISBN: (纸本)9798350366457;9798350366440

The rapid development of the Internet of Things (IoT) is enabling a wide range of applications in intelligent medical systems. Among others, medical imaging equipment produces sensitive user privacy information, however, current solutions from academia and industry often neglect the importance of secure communication mechanisms. There are open research challenges such as low real-time processing and poor security, even when using cryptography. This paper proposes EDBNet, an efficient encryption network based on deep and broad learning to improve patient privacy for medical images. To be specific, a four-layer convolutional neural network is employed to extract the horizontal and vertical factors and utilize broad learning to guide the training model to obtain two feature matrices. The training process includes pre-training and fine-tuning, with the open-source COVID-CT-Dataset enabling dual-stream encryption. To further enhance ciphertext image security in a privacy-protected environment, chaotic cryptography is utilized to consummate the encryption network, which includes scrambling and diffusion combing with the SHA3-256 algorithm. The proposed EDBNet is evaluated by extensive experiments, which show that it outperforms several state-of-the-art algorithms, such as the average cipher entropy of 7.9971, encryption quality of 248, and encrypted/decrypted time of around 1 second.

关键词： IoT image encryption Privacy protection Machine learning Chaos

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Medical image super-resolution algorithm based on multi-scale feature aggregation 2

Medical image super-resolution algorithm based on multi-scal...

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2024 International conference on Medical Artificial Intelligence

作者： Han, Xinyu Gong, Xun Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu 611756 Peoples R China Minist Educ Engn Res Ctr Sustainable Urban Intelligent Transp Chengdu 611756 Peoples R China Southwest Jiaotong Univ Natl Engn Lab Integrated Transportat Big Data App Chengdu 611756 Peoples R China Southwest Jiaotong Univ Mfg Ind Chains Collaborat & InformationSupport Te Chengdu 611756 Peoples R China

ISBN: (纸本)9798350377620;9798350377613

Ultrasound images are vital for medical diagnostics but often suffer from information loss and blurred details due to limitations in imaging systems and sensor technologies. Many researchers have proposed super-resolution algorithms to enhance medical images. But existing super-resolution methods struggle with the uneven distribution of noise and clarity. To address this, we propose a super-resolution algorithm for medical images based on multi-scale feature aggregation Leveraging the architecture of Unet as our primary framework, our method enhances output details through multi-scale hole convolutions. Taking into consideration the characteristics of ultrasonic images, we propose a frequency domain-based module to enhances edge information while effectively denoising the image. Furthermore, to improve the quality of the output images, we introduce an image processing nodule grounded in global information. The module ensures clarity while considering the overarching context, thereby preserving global consistency and enhancing output quality. We experiment on three datasets to demonstrate the effectiveness of our model. Additionally, significant improvements in medical image segmentation are observed, proving the practicability of our proposed approach.

关键词： Ultrasound images super-resolution multi-scale feature aggregation frequency-domain global information

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image Frequency Separation Residual Network for End-to-end RAW to RGB Mapping 5

Image Frequency Separation Residual Network for End-to-end R...

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5th IEEE International conference on Artificial Intelligence Circuits and systems, AICAS 2023

作者： Dong, Mengchuan Zhou, Wei Pang, Cong Zhang, Xiangyu Lou, Xin ShanghaiTech University Shanghai China

ISBN: (纸本)9798350332674

Due to the limitations of hardware specification of smartphones' camera system, there is still a visible gap in imaging quality between smartphones and digital singlelens reflex (DSLR) cameras. Sophisticated learning-based image processing becomes a promising solution to close this gap. In this paper, we propose an image Frequency Separation Residual Network (IFS Net) to perform the end-to-end RAW to RGB image mapping. Different from existing methods that directly train the input image and the ground truth image one-to-one as a whole, our proposed method first divides the input image and the ground truth into high-frequency and low-frequency parts by discrete wavelet transform (DWT). These two parts are then trained separately using different networks for details and global information, and finally synthesized into the output image using inverse DWT. Experimental results show that the proposed IFS Net outperforms other existing algorithms in both PSNR and SSIM. Visual comparison shows that the images produces by IFS Net preserves more details and look close to that captured by DSLR cameras. © 2023 IEEE.

关键词： Discrete wavelet transforms

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AN EXPERIMENTAL INVESTIGATION INTO NON-CONTACT MEASUREMENTS FOR LEAK DETECTION USING A COMMERCIAL CAMERA TO MEASURE VIBRATION 12

AN EXPERIMENTAL INVESTIGATION INTO NON-CONTACT MEASUREMENTS ...

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12th International conference on Structural Dynamics (EURODYN)

作者： Campos, B. C. Brennan, M. J. Goncalves, P. J. P. Iwanaga, M. K. Rustighi, E. Matos, P. H. M. C. de Almeida, F. C. L. Salo Paulo State Univ UNESP Dept Mech Engn UNESP FEB Bauru SP Brazil Univ Trento Dept Ind Engn Trento Italy

Leaks in water distribution systems are a major problem as a large amount of water is wasted between treatment plants and consumers. Water supply companies use different strategies to identify and locate leaks in their pipe networks, among which, vibro-acoustic methods and devices (i.e. listening sticks, geophones, and leak noise correlators) are the most popular. Although single-point listening devices are cheap and simple to use, they rely on the operator's hearing training. Moreover, this may lead to a search based on trial and error as only vibration intensity is used, so no information regarding the leak direction is available to aid in its location. Leak noise correlators, however, use two sensors and a leak can be pinpointed by estimating the time of flight between the two measured signals together with knowledge of the speed at which the leak noise propagates. These sensors need to be directly placed on the pipe or a pipe fitting. This paper concerns an investigation of a new technique, where leak noise signals measured using cameras (non-contact measurements), are pre-processed using computer vision techniques to extract meaningful information regarding vibration due to a leak. Here, this is conducted in controlled conditions by using a test bench that simulates the vibration response of a buried pipe at two measurement positions. Tests are carried out to evaluate the camera sensitivity regarding the distance from the signal source, lighting, contrast intensity, and the relation between image resolution and detection of a simulated leak are performed. The results are compared to classic contact measurements made using accelerometers, showing that the proposed technique is promising for leak detection. Moreover, this comparison is conducted using classical signal processing tools such as Power Spectral Density (PSD), modulus and phase of the Cross-Power Spectral Density (CPSD), coherence and Cross-Correlation Coefficient (CCC).

关键词： image resolution

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Bearing Fault Classification using Phase Space Reconstruction Integrated Fine-tuned VGG19 Model 15

Bearing Fault Classification using Phase Space Reconstructio...

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15th International conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Swarup, Asim Rath, Soham Rout, Madhusmita Dash, Dipti Ranjan Wagh, Mamata P. C.V. Raman Global University Computer Science And Engineering Bhubaneswar India

ISBN: (纸本)9798350370249

Effective fault detection in rotating machinery is essential for ensuring industrial systems' reliability and operational efficiency. In this work, we proposed a method for fault detection using image matching technique. The continuous images generated from rotating machinery part are considered for experimentation. Traditional signal processing algorithms may fail to extract meaningful information from signals, particularly early in the fault growth process, they might miss the subtle changes that occur during the early phases of fault development. To address this problem an image-matching technique using Visual Geometry Group 19 (VGG19) is adopted. The fundamental benefit of this approach is its ability to deal with the complexities of non-stationary chaotic line images, which frequently display temporal changes and noise interference of the expected fault. In the proposed technique, vibration signals are transformed into chaotic line images and VGG-19 architecture is employed to detect the fault from these images. The proposed technique enhances the sensitivity to faults while accommodating the complexities of non-stationary signals and noise by comparing generated images with the pre-stored images of normal and faulty occurrences. The approach showcases its capability to offer advanced warnings of potential malfunctions, enabling maintenance personnel to take proactive measures and prevent costly downtime. The proposed technique exhibits the advantage of improving the reliability and efficiency of industrial systems by enabling timely intervention and preventive maintenance. © 2024 IEEE.

关键词： chaotic line images CNN Fault detection rotating machinery template image-matching VGG-19

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3rd World conference on Intelligent and 3D Technologies, WCI3DT 2024

3rd World Conference on Intelligent and 3D Technologies, WCI...

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3rd World conference on Intelligent and 3D Technologies, WCI3DT 2024

ISBN: (纸本)9789819791279

The proceedings contain 45 papers. The special focus in this conference is on Intelligent and 3D Technologies. The topics include: Multi-scale Point Cloud Shape Completion Network Based on Deep Learning;research on Application and Effect Evaluation of Product Innovation Management Based on Deep Learning;study on Deep Learning-Based Personalised Product Recommendation Model for Autonomous Question-and-Answer Robot;application of image Watermarking Technology Based on Deep Learning in Copyright Protection;research on the Combination of Building Structural Health Monitoring and Deep Learning image processing;electrical Equipment Prediction in a Variable Electromagnetic Field Using Deep Learning;state Monitoring and Fault Prediction of Wind Farm Transmission and Transformation Equipment Based on Deep Learning;application of Deep Learning algorithms in the Innovation Ecosystem of Electric Power;optimization Strategy for Inventory Management Based on Machine Learning;intelligent Design and Evaluation of Aging Adaptable Public Spaces Based on Deep Learning;performance Optimization and Acceleration of Machine Learning algorithms in Task Allocation of Mine Maintenance Robots;Application of Deep Learning to Improve the Performance of Automotive Electronic Control Unit (ECU);deep Learning-Based Scene Classification for Remote Sensing images;Improved Swarm Intelligence Optimization Algorithm Based on SL-Relu Activation Function Improvement Strategy and Its Application in Price Forecasting;Automatic Segmentation of Traumatic Penumbra in Rat Brain Based on Improved UNet++;A Brain Tumor Classification Method Based on ResNeXt-SESA Network;A LSTM Algorithm for Coastal City Cultural Scene Value Sustainable Development Forecast Improvement;pattern Recognition in Archive Analysis Using Data Mining;building Crack Detection Method Based on Convolutional Neural Network.

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Machine Learning Approaches for Early Detection and Classification of Cervical Cancer Using image processing 5

Machine Learning Approaches for Early Detection and Classifi...

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5th International conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Kumaran, S. Roshini, M. Rohini, G. Saveetha Engineering College Department of ECE Tamilnadu Chennai India

ISBN: (纸本)9798331530013

Traditional machine learning (ML) techniques have limitations that make it difficult for existing algorithms to diagnose cervical cancer. These limitations include lower accuracy and an inability to handle complicated variations in histopathological images. These systems usually rely on manual evaluations, decision trees, logistic regression, and basic neural networks;these methods are not well suited to handling the nuances of precancerous lesions and picture quality fluctuations. By combining innovative techniques, the proposed hybrid system solves these problems. Specifically, it integrates Convolutional Neural Networks (CNNs) for hierarchical feature extraction, Vision Transformers (ViTs) for contextual information and ensemble methods that combine Gradient Boosting Machines (GBMs) and Support Vector Machines (SVMs) for robust classification. Further improving model robustness is self-supervised learning. The methodology outperforms the existing systems in terms of accuracy (97.0%), precision (94.5%), recall (95.2%), and F1-score (94.8%), indicating improved performance and fewer false positives and negatives. © 2024 IEEE.

关键词： Cervical cancer Classification Diagnostic Automation Early detection Histopathology Transfer learning

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Optimization Methods for image Edge Detection Using Ant and Bee Colony Techniques 4th

Optimization Methods for Image Edge Detection Using Ant and ...

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4th International conference on Advances in Information Communication Technology and Computing, AICTC 2022

作者： Kumar, Sachin Kumar, Sandeep Kumar, Brajendra Sharma, Sandeep Chaudhary, Harshita CSE Department Tula’s Institute Dehradun Dehradun India CSE Department UIT Uttaranchal University Dehradun Dehradun India

ISBN: (纸本)9789811998874

In a computer image analysis, the main aim is to produce the image with specified appearance that provides more convenience for society and machines to detect, identify, and understand the situation. image processing is the technique from which we can get any digital information that is stored in the form of images. "Appearance Edges" are very important feature of image inquiry and clarification. "Edge Detection" technique can be applied for extraction of edges from the image. By applying these techniques, we can get every small details of an image and unwanted details of the image can be discarded resulting in fewer amounts of data to be processed. ACO and BCO are well known meta-heuristic search algorithms used in explaining various combinative optimization problems. In this paper we are hybridizing the ACO and BCO algorithms to optimize the feature selection. The simulation results show that our proposed hybrid algorithm provides promising and optimal selection of features. We used Python’s simply framework for virtual simulations. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Ant colony optimization

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Real Time Performance Evaluation of Deep Learning algorithms in image Recognition under the Pytorch Framework

Real Time Performance Evaluation of Deep Learning Algorithms...

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Intelligent algorithms for Computational Intelligence systems (IACIS), International conference on

作者： Di Lu Shengzhi Liu Xinjiang College of Science & Technology Korla China

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

With the rapid development of artificial intelligence technology, deep learning has become one of the key technologies in the field of image recognition. PyTorch has become the preferred framework for researchers due to its flexibility, efficiency, and ease of use. This study focuses on the real-time performance of deep learning algorithms in image recognition under the PyTorch framework, and its effectiveness is verified through system experiments. The experiment revealed that the algorithm under this framework exhibits excellent real-time performance in image recognition tasks, with an average frame rate of up to 59.46 FPS and an image processing delay as low as 59.44 milliseconds, fully meeting the demand for efficient processing in a wide range of application scenarios. This discovery demonstrates the powerful potential and practicality of PyTorch in the field of image recognition.

关键词： Deep learning Performance evaluation Training image recognition Transcoding Data processing Real-time systems Delays Standards Testing

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