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

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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...

引用

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...

引用

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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Auto-encoder Convolutıonal Neural Network (AECNN) for Apple Fruit Flower Detection 1

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Congress on Smart Computing Technologies, CSCT 2023

作者： Rajagopal, Manikandan Sathesh Kumar, K. Nagaraja, P. Sivasakthivel, Ramkumar Sivaraman, G. Bangalore India Computer Science and Engineering Alliance College of Engineering and Design Alliance University Central Campus Anekal Main Road Karnataka Bangalore India Bangalore India Bangalore India Department of Computer Science M.G.R. College Tamil Nadu Hosur India

ISBN: (数字)9789819750818

ISBN: (纸本)9789819750801

The yield estimation task altogether relies upon the way toward identifying and checking the quantity of fruits on trees. In production of fruit, basic yield the board choices are guided through the bloom frequency, i.e., the quantity of the flowers that are present in a plantation. The intensity of bloom technique is still commonly assessed by methods for human visual investigation. Mechanized PC vision frameworks for flower recognizable proof depend closely on designed procedures which function just under explicit conditions and with restricted execution. This work comprises four significant advances, (I) system preparing for Fully Convolutional Network (FCN), (ii) preprocessing, (iii) component extraction, (iv) division. Initially, a strategy for assessing high-resolution pictures with deep FCN on Graphics processing Unit (GPU). Then, non-linear and linear algorithms are presented for lessening the image noise, so the exact flower identification can be ensured. The next phase of the work handles the highlight extraction for diminishing the quality of the prime assets which are needed for handling without compromising on data applicable. By applying Local Binary Pattern (LBP), surface example likelihood can be summed up into a histogram. At last, isolate an image with high resolution into sub patches, assess all patches with the help of AECNN, at that point apply the refinement calculation on acquired score maps to figure out the final version of the mask segmentation. Trial results are led utilizing two datasets on flower pictures of AppleA and AppleB. Results are estimated regarding the measurements like Precision (P) and Recall (R). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Graphics processing unit

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

Bearing Fault Classification using Phase Space Reconstructio...

引用

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...

引用

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...

引用

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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Underwater Imaging Using Low Rank Matrix and Sparse Representation for Oil Pipeline Inspection 7

Underwater Imaging Using Low Rank Matrix and Sparse Represen...

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7th International conference on Electrical, Mechanical and Computer Engineering, ICEMCE 2023

作者： Zhou, Mingshuo Zhang, Qizhi School of Electronic Engineering Xi'an Shiyou University Xi'an China

ISBN: (纸本)9798350382877

Due to the critical importance of underwater pipeline integrity, particularly in the oil and gas transportation sector. This paper addresses the significance of applying low-rank matrix and sparse representation theories in the context of underwater imaging, with a primary focus on oil pipeline inspection. By comparing the effects of three algorithms on image processing, the superiority of the Structured Low-Dimensional Representation (SLDR) algorithm is demonstrated. The research aims to enhance the accuracy and quality of underwater images, ensuring the safety and integrity of subaqueous pipeline systems. © 2023 IEEE.

关键词： image enhancement

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Performance Analysis of RTX Architecture in Virtual Production and Graphics processing 42

Performance Analysis of RTX Architecture in Virtual Producti...

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42nd IEEE International conference on Distributed Computing systems (ICDCS)

作者： Oakden, Tony Kavakli, Manolya AIE Inst Canberra Australia AIE Inst Sydney Australia

ISBN: (数字)9781665488792

ISBN: (纸本)9781665488792

Real-time rendering techniques developed for computer games combined with the improved algorithms and advanced hardware such as the Nvidia Geforce RTX 3000 series of graphic cards improve the quality of the rendered images in CGI. In this paper, our goal is to test the performance of RTX architecture in Virtual Production and graphics processing. We conducted a series of tests for rendering of a scene in Unreal game engine in a Virtual Production studio. images are rendered in 4k and output to a network distribution system where the image is broken down into a series of smaller images each rendered onto LED screens. The comparison of render times between two graphics workstations using Nvidia RTX A6000 GPU and Nvidia RTX A3090 GPI show that whilst RTX architecture produces better image quality, the gains might not be worth the additional hardware cost required by the high-end graphic cards. It might also he optimal to split the rendering of the scene across multiple computers.

关键词： Reid-time rendering Graphics processing Virtual production Game engineering

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