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检索条件"任意字段=International Symposium on Image and Signal Processing and Analysis"

共 54101 条记录，以下是81-90 订阅

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Enhancing Road Navigation for the Visually Impaired: A Laser-Based Night Vision Approach 4

Enhancing Road Navigation for the Visually Impaired: A Laser...

引用

4th international Conference on Robotics, Electrical and signal processing Techniques, ICREST 2025

作者： Sakib, Fahim Faysal Akash, Nasin Ayenul Rayied, Shahzad Hussain Juel, Md. Sajjadul Islam Hasan, Tabin American International University-Bangladesh Department of Computer Science Dhaka Bangladesh

ISBN: (纸本)9798331530938

This study proposes a Convolutional Neural Network (CNN) based system to aid visually impaired individuals by classifying road surfaces. Leveraging image analysis and processing techniques, the model uses laser beams to identify road types under different conditions, including low-light scenarios. The challenge faced by visually impaired people in navigating roads with traditional white canes limited in scope and effectiveness motivated this research. The system incorporates a custom dataset of over 3000 high-quality images, employing contour detection to recognize obstacles and road types from various distances. The model achieves an accuracy exceeding 99% indicating reliability. This advancement offers a significant improvement in navigation and safety providing a practical solution for enhancing mobility and independence for visually impaired users. Future work will focus on expanding the model to detect additional objects further enhancing user safety and environmental awareness. © 2025 IEEE.

关键词： Laser beams

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Efficient Algorithm analysis Of Kidney images On FPGA

Efficient Algorithm Analysis Of Kidney Images On FPGA

引用

2025 IEEE international Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Thanuja, G.V. Ramya, S. Manipal Institute of Technology Manipal Academy of Higher Education Dept. Electronics and Communication Engineering Karnataka Manipal576104 India

ISBN: (纸本)9798331529833

The field-programmable gate array (FPGA) offers an effective solution to meet the high-performance requirements of real-time digital signal processors. IP cores developed on FPGAs benefit from the programmable logic's flexibility, efficient timing, and adaptability in algorithm modification, coupled with the processing power provided by the embedded processor. Integrating image processing into this sector is an ideal addition, especially in the growing field of edge detection, which is crucial in areas like image pattern recognition, machine learning, and data processing. This paper presents a case study focused on kidney CT scan images for edge detection, utilizing the Sobel and Canny edge detection techniques on the target FPGA device, xc7z020clg484-1. An IP core was designed and generated using VIVADO software, converting image pixels into binary form for pre-processing through these edge detection algorithms. The algorithms were simulated and synthesized on the xc7z020clg484-1 FPGA, with the entire design implemented in Verilog code to create the IP, which connects to the DMA controller throughout the system. This IP core can process multiple CT scan images simultaneously, making it valuable for various biomedical applications. The primary aim of this research is to compare the Sobel and Canny edge detection algorithms to identify the best approach for developing an IP core, evaluated by metrics such as total power consumption, CPU time, execution time, LUT count, and FF usage. The resulting IP core is efficient and conserves resources, making it suitable for other embedded applications. © 2025 IEEE.

关键词： image analysis

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Heart signal processing Using Wavelet analysis and Deep Learning Algorithms Based on Artificial Intelligence

Heart Signal Processing Using Wavelet Analysis and Deep Lear...

引用

international Conference on Applications in Electronics Pervading Industry, Environment and Society, APPLEPIES 2024

作者： Zilaie, Morteza Mohammadkhani, Zohreh Asrari, Keyvan Azimi Talebiyan, Seyed Reza Islamic Azad University Science and Research Branch Khorasan Razavi Mashhad Iran The University of Pavia Pavia Italy Imam Reza International University Khorasan Razavi Mashhad Iran

ISBN: (纸本)9783031840999

The electrocardiogram signal of the heart is used to monitor the health status and function of the human heart and to a doctor in diagnosing the type of disease. For this purpose, first, the scalogram of the different heart failures produces different types of this signal. In this research, the cardiac signals of the patients are analyzed by an artificial intelligence neural network, and the type of the person’s disease is diagnosed. The artificial intelligence in question can be learned and can be used as an assistant ECG signal is calculated using continuous wavelet transform and then it is subjected to learning and evaluation using a deep convolutional neural network. In the architecture of artificial intelligence, two well-known neural networks GoogLeNet and SqueezeNet are used, which have been sufficiently trained in similar applications such as image processing and machine vision. In the end, the type of heart failure is diagnosed and classified. The cardiac signals of the patients used in the simulation were extracted from the PhysioNet medical engineering standard dataset of MIT University. In the end, the simulation results of the research are given. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Patch based image processing for complex environment characterization 6

Patch based image processing for complex environment charact...

引用

6th IEEE international Conference on image processing, Applications and Systems, IPAS 2025

作者： Menier, Corentin Meurie, Cyril Guillemaille, Timothee Ruichek, Yassine Marais, Juliette Univ Gustave Eiffel COSYS-LEOST Villeneuve d'AscqF-59650 France UTBM BelfortF-90010 France

ISBN: (纸本)9798331506520

Environment analysis is a critical part of autonomous vehicle for transport applications and for passenger safety. The solutions demonstrating the greatest robustness have been integrating multiple sensors used for redundancy and refinement purposes. Vision applications have proven to offer a high degree of flexibility and performance. One particular instance of this is higlighted in vehicle localisation, which predominantly relies on GNSS-based systems for positioning calculation using propagation time measurements. However, this signal may be degraded through the environment around the vehicle, worst case being urban canyons leading to Non Line Of Sight(NLOS) scenarios or multipaths issues due to reflecting obstacles. Previous work have shown vision-based algorithms can be used to mitigate these effects. One widely studied approach relies on the segmentation of an acquired wide-angle image installed on the roof of the vehicle and oriented toward the *** the sky processing module is binary, the pipeline lack any way to express its uncertainty when applying weighting policies to the detected satellite state, which can be detrimental to the resulting positioning. In this paper, we propose a novel way of analysing wide-angle camera images, also known as fisheye images, dividing the image into patches to output the corresponding situation of each region of interest. Additionally we propose a new class to the previous sky versus non-sky segmentation, designated as mixed class and designed to serve as a fuzzy answer by the deep learning model to improve confidence to other scenarios as well as allow for new analysis policies of satellites signals. The data-driven algorithm is designed and tested on a publicly available dataset, composed of a large number of finely labelled images provided by ISAE-SUPAERO reaching a 94% accuracy. © 2025 IEEE.

关键词： image segmentation

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analysis Automation for High Explosive Breakout Symmetry 19th

Analysis Automation for High Explosive Breakout Symmetry

引用

19th international symposium on Visual Computing

作者： Tronsen, Sean Francois, Elizabeth Scovel, Christina DeBardeleben, Nathan Los Alamos Natl Lab Los Alamos NM 87545 USA

ISBN: (纸本)9783031773914;9783031773921

In this work, we present an automated approach for analyzing hemispherical breakout symmetry in high explosive detonation experiments. Traditionally, the analysis of streak camera records from these tests required extensive human intervention to ensure accurate measurements, thereby slowing the rate at which additional tests could be fired. Our developed procedures leverage a combination of classic vision algorithms, probabilistic algorithms like RANSAC, and machine learning techniques, reducing the analysis time from several hours to several minutes. The software was designed with the intent to run on non-exotic consumer hardware, thus enabling researchers to obtain results in the field using standard laptops within minutes.

关键词： signal processing Machine learning High-speed imaging Optical measurement Explosions Shockwave symmetry

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CAF-YOLO: A Robust Framework for Multi-Scale Lesion Detection in Biomedical imagery

CAF-YOLO: A Robust Framework for Multi-Scale Lesion Detectio...

引用

2025 IEEE international Conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Chen, Zilin Lu, Shengnan School of Computer Science Xi'an Shiyou University Xian China

ISBN: (纸本)9798350368741

Object detection is essential in biomedical image analysis, particularly for identifying small yet critical entities such as abnormal cells or lung nodules under 3 mm, which are often missed by existing methods. To address this, we propose CAF-YOLO, built on the YOLOv8 architecture. Our model combines CNNs for robust local feature extraction with transformers to capture long-range dependencies. To mitigate the limited ability of convolutional kernels to handle distant spatial interactions, we developed an attention and convolution fusion module (ACFM), which enhances both global and local feature modeling. Additionally, we designed a multi-scale neural network (MSNN) to aggregate features across multiple scales, overcoming the restricted feature aggregation seen in standard feed-forward networks (FFN) within transformer architectures. This comprehensive approach improves detection accuracy in identifying complex micro-lesions. Experimental results on benchmark datasets like BCCD and LUNA16 demonstrate the efficacy of CAF-YOLO in capturing detailed biomedical entities. Our codes are available at https://***/xiaochen925/CAF-YOLO. © 2025 IEEE.

关键词： convolutional Medical image object detection Transformer

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SelfDeblur with Sparsity Enforced Bregman Learning

SelfDeblur with Sparsity Enforced Bregman Learning

引用

2025 IEEE international Conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Zeng, Yaoyun Chen, Beier Wang, Hongxia Department of Mathematics National University of Defense Technology Changsha China

ISBN: (纸本)9798350368741

Learning-driven methods have revolutionized the field of blind deconvolution, with SelfDeblur as a pioneering method. It uses deep image priors to jointly estimate blur kernels and latent clear images, employing deep neural networks to automatically learn these priors and effectively capture their statistical properties. Building on the success of SelfDeblur, this paper proposes a novel sparse optimization method, named B-SelfDeblur, which employs linearized Bregman iterations to train neural networks. Our approach addresses the challenges of processing large-scale blurry images by starting with minimal parameters and incrementally adding necessary ones in an inverse scale space manner. Additionally, we incorporate a smoothing technique for the total variation (TV) regularization term, which ensures our loss function is continuously differentiable. Experimental results demonstrate that B-SelfDeblur can significantly optimize network weight storage while maintaining deblurring effectiveness. Furthermore, we provide a partial theoretical analysis and ablation studies, which confirm that our loss function is monotonically decreasing and that our method is well-suited for alternating iterative optimization. © 2025 IEEE.

关键词： Blind deconvolution Bregman learning deep image priors SelfDeblur

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Integrating Deep Learning Frameworks for Automated Medical image Diagnosis 8th

Integrating Deep Learning Frameworks for Automated Medical I...

引用

8th IFIP TC 12 international Conference on Computer, Communication and signal processing, ICCCSP 2024

作者： Gayatri, K. Kumar, K. M. Anand Padmavathi, B. Shankar Department of Computer Science and Engineering Easwari Engineering College Ramapuram Chennai600089 India

ISBN: (纸本)9783031736162

In today’s healthcare system, medical image analysis is essential for diagnosis, treatment planning, and condition monitoring. In this study, the four well-known tools in the field are 3D Slicer, MONAI, SAMM, and YOLOv8 are examined and contrasted. The study’s scope includes its ability to process a variety of 2D and 4D medical imaging datasets, which contain images, segmentation, annotations, and transformations. The process entails a comprehensive prehensive examination of every tool’s feature, with particular emphasis on their capacity to read and write DICOM images, accommodate multiple file formats, offer interactive 3D Slicer. Key findings show that 3D Slicer performs exceptionally well when using deep learning techniques for segmentation and interactive visualization. While SAM Model exhibits adaptability in managing diverse segmentation prompts, MONAI provides a comprehensive end-to-end medical data processing solution. YOLOv8 shows potential for effective object detection. The study’s implications include the possibility of using these instruments in clinical practices and medical research contexts. The research results provide insight into the current discussion about how to strategically integrate state-of-the-art methods to improve overall healthcare outcomes, diagnostic accuracy, and treatment planning efficiency as medical image analysis continues to advance. © IFIP international Federation for Information processing 2025.

关键词： Clinical research

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SX-Stitch: An Efficient VMS-UNet Based Framework for Intraoperative Scoliosis X-Ray image Stitching

SX-Stitch: An Efficient VMS-UNet Based Framework for Intraop...

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2025 IEEE international Conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Li, Yi Gao, Heting He, Mingde Liang, Jinqian Gu, Jason Liu, Wei Department of Mechanical and Energy Engineering Southern University of Science and Technology China Peking Union Medical College Hospital China Dalhousie University Canada

ISBN: (纸本)9798350368741

In scoliosis surgery, the limited field of view of the C-arm X-ray machine restricts the surgeons' holistic analysis of spinal structures. This paper presents an end-to-end efficient and robust intraoperative X-ray image stitching method for scoliosis surgery, named SX-Stitch. The method is divided into two stages: segmentation and stitching. In the segmentation stage, we propose a medical image segmentation model named Vision Mamba of Spine-UNet (VMS-UNet), which utilizes the state space Mamba to capture long-distance contextual information while maintaining linear computational complexity. Meanwhile, the proposed model incorporates the SimAM attention mechanism, significantly improving the segmentation performance. In the stitching stage, we simplify the alignment process between images to minimize a registration energy function. The total energy function is then optimized to order unordered images and a hybrid energy function is introduced to optimize the best seam, effectively eliminating parallax artifacts. On the clinical dataset, Sx-Stitch demonstrates superiority over SOTA schemes both qualitatively and quantitatively. © 2025 IEEE.

关键词： image Stitching Mamba Scoliosis UNet X-ray image

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DRSFANet: Dual-Path CNN with Residual and Frequency Attention for image Denoising

DRSFANet: Dual-Path CNN with Residual and Frequency Attentio...

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2025 IEEE international Conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Kumar, Manish Maji, Suman Kumar Saha, Anirban CSIR Fourth Paradigm Institute Bangalore Karnataka 560037 India C.S.E. Department IIT Patna Bihta Bihar Patna801103 India C.S.E. Department Indian Institute of Technology Patna Bihta Bihar Patna801103 India

ISBN: (纸本)9798350368741

image noise, often resulting from disturbances during image acquisition or imperfections in the imaging device, notably degrades the quality of digital images. The challenge of removing this noise has been addressed through various techniques, from traditional filtering and prior-based methods to more recent deep learning approaches. In this paper, we introduce DRSFANet, an advanced dual-path convolutional neural network (CNN) specifically designed to tackle both synthetic Additive White Gaussian Noise (AWGN) and real-world noise. DRSFANet incorporates several innovative components: a residual feature extraction module (FEB) equipped with dilated convolutional layers to enhance the receptive field and mitigate gradient vanishing issues, and novel attention modules-Frequency-Plane Attention Block (FPAB) and Residual Attention Block (RAB)-which improve feature extraction in both frequency and spatial domains. Furthermore, the model features a downsampling (DS) block that effectively consolidates essential features prior to their integration into subsequent network stages. Comprehensive experimental evaluations reveal that DRSFANet outperforms several state-of-the-art denoising methods, demonstrating superior performance in both synthetic and real datasets through rigorous quantitative and qualitative analysis. © 2025 IEEE.

关键词： Additive White Gaussian Noise (AWGN) Convolutional Neural Networks (CNN) image Denoising Real Noise Synthetic

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