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检索条件"任意字段=Conference on Real-Time Image Processing 2007"

共 25886 条记录，以下是431-440 订阅

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MS-PRNet: A Novel Hierarchical Framework for Optimized and Precise Medical image Registration 10

MS-PRNet: A Novel Hierarchical Framework for Optimized and P...

引用

10th International conference on Big Data and Information Analytics

作者： Chen, Zexian Zhou, Xiren Li, Huaijun Zhao, Danyang Chen, Huanhuan Univ Sci & Technol China Sch Comp Sci & Technol Hefei Peoples R China China Merchants Bank Co Ltd Dept Informat Technol Shenzhen Peoples R China

ISBN: (纸本)9798350354638;9798350354621

Medical image registration faces challenges due to limited effective receptive fields and inadequate understanding of global spatial correspondences in traditional Convolutional Neural Networks (CNN). The existing methods, while addressing these issues, also introduced problems, such as increased model complexity, which results in higher computational costs and reduced real-time performance. In response, we propose the Multi Scale Pyramid Registration Network (MS-PRNet), which not only effectively handles large-scale displacements but also avoids the increased computational complexity associated with existing methods. MS-PRNet utilizes a hierarchical scaling method to process image pairs, calculating displacement deformation fields at multiple levels and integrating them to produce accurate registration outcomes. Tested on the LPBA40 and Mindboggle101 datasets, MS-PRNet outperformed existing methods, particularly excelling in regions with complex anatomical structures. This approach improves diagnostic precision and enhances clinical applications without compromising computational efficiency.

关键词： Medical image Registration Feature Pyramid Network Multi-scale processing Deformable image Registration Deep Learning

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Performance Optimization of U-Net Network in Segmentation of Nerves image Dataset

Performance Optimization of U-Net Network in Segmentation of...

引用

2025 International conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Sreeja, G Veera Nagaraju, M. Theja, C.P. Sai Agarwal, Vikas Telangana Hyderabad India

ISBN: (纸本)9798331509828

This research explores the performance and optimization of U-Net architectures for segmenting nerve images. U-Nets are prevalent in medical image analysis due to their effectiveness in capturing detailed features and spatial relationships. Our focus is on enhancing U-Net models by fine-tuning hyperparameters, applying data augmentation techniques, and utilizing advanced loss functions to boost segmentation accuracy and computational efficiency. The study compares various U-Net variants, evaluating their effectiveness in preserving the delicate structures of nerve tissues. The results indicate that optimized U-Nets deliver high accuracy with reduced processing times, making them ideal for real-time medical applications. This research provides valuable insights into the practical use of U-Nets for nerve image segmentation and suggests avenues for future improvements. © 2025 IEEE.

关键词： Computational Modelling Data Augmentation Hyperparameter Tuning Medical image Analysis Nerve Disease Segmentation

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Hardware-Aware Depth image Classification Model for In-Sensor AI Integration

Hardware-Aware Depth Image Classification Model for In-Sens...

引用

International conference on Applications in Electronics Pervading Industry, Environment and Society, APPLEPIES 2024

作者： Fasolino, Andrea Licciardo, Gian Domenico Vitolo, Paola Liguori, Rosalba Di Benedetto, Luigi Rubino, Alfredo Pau, Danilo Department of Industrial Engineering University of Salerno Fisciano Salerno84084 Italy System Research and Applications STMicroelectronics Agrate Brianza20041 Italy

ISBN: (纸本)9783031840999

The shift of computing capabilities towards edge sensing elements in image classification tasks is leading to the substitution of cameras in some industrial and consumer tasks with ultra-low-resolution (ULR) time-of-Flight (ToF) sensors, thanks to their compactness and ultra-low power consumption. To effectively integrate classification capabilities into edge devices, a hardware-aware design of the classification algorithm and a careful custom Hardware (HW) post-processing core are needed. This work proposes a new compact and ultra-low power Neural Network (NN) that enables the implementation of a HW post-processing core for real-time classification tasks inside the sensor packaging. The resulting HW architecture has been prototyped on an AMD Xilinx Artix-7 FPGA, reaching an Energy per Inference consumption of 65.6 nJ and a power consumption of 1.095 μW at the maximum Output Data Rate of the sensor. Moreover, the implementation in Skywater 130 nm technology reveals an area occupation below 1 mm2, with a total power compared to the sensor consumption of about 11%. These results are significantly lower than the typical energy and power consumption of the sensor, enabling real-time post-processing of depth images and encouraging research in integrating classification capabilities next to sensing elements. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Digital storage

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Applying Reservoir Computing and Machine Learning Techniques for image Enhancement in Biomedical Imaging

Applying Reservoir Computing and Machine Learning Techniques...

引用

International conference on Smart Applications, Communications and Networking (SmartNets)

作者： Slonopas, Andre Beatty, Adam Djajalaksana, Yenni Amer Publ Univ Syst APUS Sch Secur & Global Studies SSGS Dept Cybersecur Charles Town WV 25414 USA Int Council E Commerce Consultants EC Council Acad Div Tampa FL USA

ISBN: (纸本)9798350385328;9798350385335

The paper presents a comprehensive study on the application of Machine Learning (ML) in enhancing the contrast of biomedical images. This research is pivotal in addressing the challenges of low visibility and detail in medical imaging, which are crucial for accurate diagnosis and treatment. The paper introduces innovative ML-based Histogram Equalization (ML - HE), leveraging deep learning algorithms and advanced image processing methods, to significantly improve the quality of biomedical images. These techniques enhance image clarity, detail, and overall contrast without compromising the integrity of the original data. This paper seeks to explore the integration of ML, specifically Reservoir Computing, with traditional image enhancement methods, creating a synergistic approach that leverages the strengths of both ML and conventional techniques and expedites image enhancements in near real-time. This hybrid approach is shown to be more effective in handling diverse and complex imaging scenarios encountered in biomedical applications. The study also discusses the implications of these advancements for medical professionals, highlighting how ML-enhanced images can lead to more accurate diagnoses, better patient outcomes, and advancements in medical research. Overall, this paper sheds light on the transformative potential of ML in revolutionizing biomedical imaging, setting a new standard for image quality and diagnostic precision in healthcare.

关键词： Machine Learning image Enhancement Biomedical Imaging AI in Healthcare AI/ML Integration

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Research on Fire Recognition Algorithm Based on Automatic image Recognition Technology 6

Research on Fire Recognition Algorithm Based on Automatic Im...

引用

6th IEEE International conference on Power, Intelligent Computing and Systems, ICPICS 2024

作者： Zhao, Zhijian Jiangsu Union Technical Institute Nanjing Engineering Branch Nanjing China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374315

Aiming at the research of automatic fire recognition technology, this paper discusses a fire recognition algorithm based on automatic image recognition technology. The algorithm makes full use of advanced technologies such as image processing, pattern recognition and machine learning, and effectively extracts fire signal information such as flame color and dynamic characteristics through real-time analysis of the acquired image sequence. In this paper, a deep learning model integrating multiple fire characteristics is proposed. By training large-scale fire sample data, the algorithm can improve its adaptability and recognition accuracy to different environments, lighting conditions and fire development stages. In addition, aiming at the problem of flame recognition in complex scenes, a method based on 3D convolutional neural network was designed to effectively distinguish real fire from interference factors by integrating spatiotemporal information between continuous frames. © 2024 IEEE.

关键词： Premixed flames

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Hardware Implementation of Low-latency image Denoising with Wavelet Shrinkage and Noise2Noise-based Learning for Ultra-high Definition Cameras

Hardware Implementation of Low-latency Image Denoising with ...

引用

2024 IEEE International conference on Consumer Electronics, ICCE 2024

作者： Kikuchi, Kodai Tomioka, Kohei Honji, Akira Usui, Takenobu Hirakawa, Keigo Kitamura, Kazuya Science and Technology Research Laboratories Japan Broadcasting Corporation Tokyo Japan University of Dayton Department of Electrical and Computer Engineering OH United States

ISBN: (纸本)9798350324136

There is a growing demand for real-time image denoising in low-light shooting with ultra-high definition cameras. This paper presents a denoising method that incorporates Haar-wavelet shrinkage denoising and a minimum mean square error estimation based on an extension of the Noise2Noise learning technique, which can perform low-latency processing and can be adapted to non-linear processed signals without prior knowledge of noise and signal distributions. By introducing a two-dimensional kernel to reduce memory usage in the coring function estimator, we demonstrate real-time hardware denoising for luminance signals of 4K 60 fps video with a delay of less than one millisecond. © 2024 IEEE.

关键词： FPGA Haar-wavelet image denoising MMSE estimation Noise2Noise UHD

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SLIDEX: A Novel Architecture for Sliding Window processing 24

SLIDEX: A Novel Architecture for Sliding Window Processing

引用

38th ACM International conference on Supercomputing (ACM ICS)

作者： Taranco, Raul Arnau, Jose-Maria Gonzalez, Antonio Univ Politecn Cataluna Barcelona Spain

ISBN: (纸本)9798400706103

Efficient image processing is increasingly crucial in constrained embedded and real-time platforms, especially in emerging applications such as Autonomous Driving (AD) or Augmented/Virtual reality (AR/VR). A commonality among most image processing operations is their reliance on primitives like convolutions and stencil operations, which typically utilize a sliding window dataflow. Many existing implementations are domain-specific, lacking generality, or are programmable at the cost of sacrificing performance and energy efficiency. Among the latter, the CPU-based platforms that typically rely on Vector processing Units (VPUs) often miss critical optimization opportunities, particularly those arising from the overlapping nature of the mentioned windowed image processing operations. In response, we propose SLIDEX, a novel high-performance and energy-efficient vector ISA extension to exploit Sliding Window-processing (SWP) in conventional CPUs. SWP extends the conventional vector SIMD execution model, treating vector registers like variable-sized groups of overlapping pixel windows. SLIDEX-enabled VPU processes multiple windows simultaneously, maximizing the Data Level Parallelism (DLP) achievable per instruction while maintaining the same vector length. Furthermore, it significantly reduces the need for data access, movement, and alignment, decreasing memory and register file accesses compared to traditional SIMD designs. To support SLIDEX, we introduce a cost-effective micro-architecture designed for easy integration into existing VPUs with minimal modifications. We demonstrate the efficacy of SLIDEX by testing it on a state-of-the-art visual localization task critical in AD and AR/VR. The results are compelling: SLIDEX achieves significant speedups in vital tasks such as 2D convolutions for image filtering and stencil operations for feature extraction, leading to an overall speedup of similar to 1.2x and up to 19% energy reduction compared to traditional vector exten

关键词： ISA SIMD image processing sliding-window convolution stencil

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Dynamic-Feature-Based Object Tracking Using real-time image processing 5th

Dynamic-Feature-Based Object Tracking Using Real-Time Image ...

引用

5th International conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Mishra, Awakash Rengarajan Bhatt, Rahul Prince, P. Blessed Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science & Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

This research explores the utility of today's real-time picture processing for dynamic-characteristic-primarily based object monitoring. Notably, this painting proposes a novel tracking method that combines an actual-time, place-primarily based convolutional neural community (R-CNN) with monitoring with the detection (TbD) technique. The proposed approach is for contemporary R-CNNs to constantly come across transferring objects in video frames and then modern-day the TbD technique to generate accurate item tracking information. The proposed system is evaluated on two benchmark datasets and compared to numerous tracking methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Integrating AI and UAV Technologies for Enhanced and Workplace Safety 27th

Integrating AI and UAV Technologies for Enhanced and Workpla...

引用

27th International conference on Interactive Collaborative Learning-ICL

作者： Badea, Cosmin Costea, Antonia Halmaghi, Alexandra Hancu, Alexandra Bogdan, Ioana Corina Modran, Horia Alexandru Transilvania Univ Brasov Dept Elect & Comp Brasov Romania

ISBN: (纸本)9783031835193;9783031835209

The integration of artificial intelligence (AI) and unmanned aerial vehicle (UAV) technologies presents a significant advancement in enhancing safety in traffic, workplace, and healthcare environments. This study explores the application of AI-driven computer vision algorithms in UAVs to detect and mitigate risks associated with substance abuse, fatigue, and health impairments. Utilizing sophisticated image processing techniques, such as edge detection and support vector machine (SVM) algorithms, drones are equipped to autonomously monitor and analyze ocular characteristics and facial expressions of individuals. The research employs a mobile phone camera and Python-based libraries to conduct real-time assessments, providing critical data to medical and industrial professionals. The study demonstrates the potential of drones to enhance safety by checking sobriety and monitoring worker health. The experimental setup includes a detailed workflow for real-time video detection and facial analysis, leveraging pre-trained models and convolutional neural networks. The results confirm the effectiveness of this approach, highlighting significant progress in AI and UAV technology. Future work aims to transition these innovations from laboratory conditions to practical, real-world applications, continuously enhancing the algorithms and expanding their applicability across various safety-critical scenarios.

关键词： Drones Surveillance Computer Vision Artificial Intelligence image processing Healthcare

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image Monitoring System Based on Deep Neural Network 2

Image Monitoring System Based on Deep Neural Network

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2nd International conference on image processing, Computer Vision and Machine Learning, ICICML 2023

作者： Weng, Junhong Zeng, Lingfeng Song, Yingjie Shenzhen Power Supply Bureau CO. LTD China Southern Power Grid Guangdong Shenzhen China

ISBN: (纸本)9798350331417

For safety and security reasons, the indoor/outdoor working environments of various industries require the use of many cameras for automated surveillance. In such context, a major challenge for automated monitoring system is achieving high-precision real-time performance for image classification and object detection. In this paper, we present a novel image surveillance system based on a combined approach derived from YOLO V5. The system first detects moving targets using background subtraction. Then, we propose a modified YOLO V5 algorithm for accurately detecting and categorizing different objects in images captured in a video stream. The system runs in real time and could analyze multiple video streams simultaneously. The results of the experiments show that this system has good performance and could be widely applied on several areas, such as security, surveillance, and traffic management. © 2023 IEEE.

关键词： deep nueral network monitoring system YOLOv5

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