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检索条件"任意字段=Conference on Real-Time Image Processing 2006"

共 25649 条记录，以下是41-50 订阅

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Design and Implementation of Adaptive Gaussian Denoising Algorithm Based on FPGA 7

Design and Implementation of Adaptive Gaussian Denoising Alg...

引用

7th International conference on Automation Electronics and Electrical Engineering

作者： Chen, Xinghao Beihang Univ Sch Elect Informat Engn Beijing Peoples R China

ISBN: (纸本)9798350377040;9798350377033

With the continuous progress of image processing and machine vision technology, the demand for efficient and real-time processing is becoming more and more prominent, especially in the field of high-noise image processing. In this study, an adaptive Gaussian filtering algorithm is proposed, which is implemented based on FPGA and aims to improve the computational efficiency and real-time performance of the image processing system. Compared with the traditional fixed-weight filter, this algorithm is able to dynamically adjust the filtering parameters according to different noise environments, effectively balancing noise suppression and image detail retention. We coded the algorithm using Verilog hardware description language and verified it on PYNQ-Z2 FPGA platform. The experimental results show that the adaptive algorithm outperforms the fixed-weight filtering method in terms of performance, especially in terms of noise suppression and detail preservation. Meanwhile, the FPGA hardware implements the reduction of filtering delay and optimization of resource consumption, making it well suited for real-time applications. This study demonstrates the promise of FPGA adaptive filtering for applications in medical imaging, remote sensing, and intelligent surveillance, which have stringent requirements for high-performance and high-efficiency processing. This research provides new hardware solutions for real-time, high-quality image processing in constrained environments.

关键词： FPGA image denoising gaussian filtering adaptive algorithm

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A Compact real-time Thermal Imaging System Based on Heterogeneous System-on-Chip 30

A Compact Real-Time Thermal Imaging System Based on Heteroge...

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30th IEEE International conference on Embedded and real-time Computing Systems and Applications (RTCSA)

作者： Oh, Hyun Woo Choi, Cheol-Ho Cha, Jeong Woo Choi, Hyunmin Shin, Jung-Ho Han, Joon Hwan Hanwha Syst Infra Technol R&D Ctr Seongnam South Korea

ISBN: (纸本)9798350387964;9798350387957

This paper presents a real-time embedded thermal imaging system architecture for compact, energy-efficient, high-quality imaging utilizing heterogeneous system-on-chip (SoC) and uncooled infrared focal plane arrays (IRFPAs). Unlike previous systems that organized separate devices for complex image processing, our system provides integrated image processing support for robust sensor-to-surveillance. The image processing organizes two algorithm stacks: a non-uniformity correction stack to mitigate the distinctive noise vulnerabilities of uncooled IRFPAs, and an image enhancement stack including contrast enhancement and temporal noise filters. We optimized these algorithms for domain-specific factors, including asymmetric multiprocessing (AMP), cache organization, single instruction multiple data (SIMD) instructions, and very long instruction word (VLIW) architectures. The implementation on the TI TDA3x SoC demonstrates that our system can process 640x480, 60 frames per second (FPS) videos at a peak core load of 57.5% while consuming power less than 2.2 W for the entire system, denoting the possibility of processing the 1280x1024, 30 FPS videos from the cutting-edge uncooled IRFPAs. Additionally, our system improves power efficiency by 9.42% and 9.96% at 30 and 60 FPS, respectively, compared to the state-of-the-art when executing similar image processing algorithms.

关键词： Thermal Imaging image processing Heterogeneous Computing SIMD real-time Embedded Systems

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GEEG-YOLOV8: GAUSSIAN ENHANCED EUCLIDEAN NORM GHOST ATTENTION FOR real-time POLYP DETECTION 31

GEEG-YOLOV8: GAUSSIAN ENHANCED EUCLIDEAN NORM GHOST ATTENTIO...

引用

2024 International conference on image processing

作者： Phuong Thao Nguyen Watanabe, Hiroshi Waseda Univ Grad Sch Fundamental Sci & Engn Tokyo Japan

ISBN: (纸本)9798350349405;9798350349399

Research on computer-aided polyp detection in gastrointestinal endoscopy has spanned the past few decades. Despite notable progress, the challenge of achieving automatic accurate and real-time polyp detection remains unresolved. This is because of the large differences in polyp characteristics such as shape, texture, size, and color, and the artifacts that are similar to polyp during endoscopy procedure. In this paper, we propose a novel Gaussian Enhanced Euclidean norm Ghost attention (GEEG) module for reliable real-time polyp detection on endoscopic images and videos. The new attention mechanism strengthens the features generated by Ghost convolution's cheap operations by increasing the ability to extract inter-channel and spatial information inside the convolution layer. This module is integrated into the backbone of YOLOv8, creating a new model named GEEG-YOLOv8, to overcome above obstacles in polyp detection. Experiment results on three public datasets show that our proposed method outperforms existing state-of-the-art methods in both accuracy and speed.

关键词： Attention mechanism yolov8 deep learning polyp detection medical image analysis

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Optimization techniques for real-time optical image processing using edge computing 2

Optimization techniques for real-time optical image processi...

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2nd International conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： Yuan, Hongwei Niu, Zhenzhen Liu, Jingtian Jiuquan Satellite Launch Center China Korla Xinjiang 841000 China

ISBN: (纸本)9781510689053

real-time optical image processing has become a critical technology in domains such as autonomous systems, medical diagnostics, and surveillance. However, traditional centralized processing approaches face challenges in latency, bandwidth consumption, and scalability, particularly with high-resolution image data. This paper proposes an optimization framework that integrates edge computing with advanced image processing techniques to address these challenges. The framework utilizes a distributed architecture where computationally intensive tasks, such as image enhancement, object detection, and noise reduction, are offloaded to edge devices. Key optimizations include dynamic task allocation, low-latency communication protocols, and lightweight machine learning models for edge deployment. Experimental results demonstrate a significant reduction in processing latency, enhanced scalability, and improved accuracy in real-time image processing applications. The findings highlight the potential of edge computing to revolutionize real-time optical image processing, paving the way for robust and efficient solutions in diverse application areas. © 2025 SPIE.

关键词： Optical data processing

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Charting the rise of ONNX in swift image processing: a publications exploration

Charting the rise of ONNX in swift image processing: a publi...

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conference on real-time image processing and Deep Learning

作者： Obaideen, Khaled Bonny, Talal AlShabi, Mohammad Univ Sharjah Biosensing & Biosensors Grp Smart Automat & Commun Technol RISE Sharjah U Arab Emirates Univ Sharjah Dept Comp Engn Sharjah U Arab Emirates Univ Sharjah Dept Mech & Nucl Engn Sharjah U Arab Emirates

ISBN: (纸本)9781510673878;9781510673861

In the rapidly evolving realm of machine learning, the integration of the Open Neural Network Exchange (ONNX) has become increasingly significant, particularly in image processing applications. This study conducts a comprehensive examination of the role of ONNX in enhancing image processing efficiency. Utilizing a diverse range of peer-reviewed articles, conference papers, and technical reports, we quantitatively evaluate ONNX's adoption, impact, and innovation trajectory within the field. Our findings reveal a consistent rise in ONNX's use for various image processing tasks, attributable to its versatility in integrating with multiple machine learning frameworks and harnessing hardware-specific optimizations. A notable observation from our study is the positive relationship between ONNX implementation and reduced image processing times, evident in applications like real-time object detection and high-resolution image synthesis. Our analysis also highlights the growing collaborations between academic and industrial sectors in advancing ONNX capabilities, underlining its pivotal role in future imaging solutions. In summary, this paper emphasizes ONNX's transformative influence in the field of image processing. The ongoing developments and active community engagement point towards a promising future for more rapid and efficient image processing methods.

关键词： ONNX image processing Biblioshiny Bibliometric

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Practical real-time image compression for resource-challenged devices

Practical real-time image compression for resource-challenge...

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conference on Multimodal image Exploitation and Learning

作者： Pham, Kevin Depoian, Arthur C., II Bailey, Colleen P. Univ North Texas Dept Elect Engn Denton TX 76207 USA

ISBN: (纸本)9781510673854;9781510673847

In the era of rapidly expanding image data, the demand for improved image compression algorithms has grown significantly, particularly with the integration of deep learning approaches into traditional image processing tasks. However, many of the existing solutions in this domain are burdened by computational complexity, rendering them unsuitable for real-time deployment on standard devices as they often necessitate complex systems and substantial energy consumption. This work addresses the growing paradigm of edge computing for real-time applications by introducing a novel, on-edge device solution. This innovative approach aims to strike a balance between efficiency and accuracy, adhering to the practical constraints of real-world deployment. By presenting demonstrations of the proposed solution's performance on readily available devices, we provide tangible evidence of its applicability and viability in real-world scenarios. This advance contributes to the ongoing dialogue about the need for accessible and efficient image compression algorithms that can be deployed real-time applications on edge devices, bridging the gap between the demanding computational requirements of deep learning and the practical limitations of everyday hardware. As data continues to surge, solutions like this become ever more critical in ensuring effective image compression, aligning with on-edge computing within AI. This research paves the way for improved image processing in real-time applications while conserving computational resources and energy consumption.

关键词： image compression efficient AI real time processing

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Self-Adapting Reconfigurable Multiply-Accumulator for real-time image processing in Embedded Systems

Self-Adapting Reconfigurable Multiply-Accumulator for Real-T...

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conference on real-time processing of image, Depth, and Video Information

作者： Fasolino, Andrea Vitolo, Paola Di Benedetto, Luigi Liguori, Rosalba Rubino, Alfredo Licciardo, Gian Domenico Univ Salerno Dept Ind Engn Via Giovanni Paolo II 132 I-84084 Fisciano SA Italy

ISBN: (纸本)9781510673199;9781510673182

Multiply-Accumulate (MAC) operation is widely used in various real-time image processing tasks, ranging from Convolutional Neural Networks to digital filtering, significantly impacting overall system performance. In this work the Self-Adapting Reconfigurable Multiply-Accumulate (SR-MAC) is proposed as a new instrument to find the optimal trade-off between operation throughput, power consumption and physical resources utilization in real-time image processing applications. Operations of the proposed system rely on the dynamic reconfiguration of the hardware resources on the basis of the current computational requirements. This is achieved by monitoring overflow and over-representation occurrences at each accumulation cycle, and properly considering the relevant portion of the accumulation result. A custom architecture of the proposed algorithm has been designed and implemented on an AMD Xilinx Artix-7 FPGA through a Verilog description and compared to the AMD Xilinx fixed-point macro (floating-point fused multiply-accumulate). The SR-MAC achieves reductions of 83% (82%), 79% (93%) and 87.2% (94%) in the number of LUTs, FFs, and the power dissipation, P-dynN, respectively. The SR-MAC has also been used to replace arithmetic units in typical real-time image processing applications. In these cases, its employment has allowed the reduction up to 6% and 14% of FFs and P-dynN, respectively, while increasing up to 14% the f(Max). These results highlight the significant performance enhancement achieved with respect to both single operators and entire systems, making SR-MAC an excellent design choice in real-time image processing applications.

关键词： Multiply-Accumulate (MAC) dynamically reconfigurable circuits power efficient circuits

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3rd International conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2020

3rd International Conference on Recent Trends in Image Proce...

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3rd International conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2020

ISBN: (纸本)9789811604928

The proceedings contain 78 papers. The special focus in this conference is on Recent Trends in image processing and Pattern Recognition. The topics include: Ensemble of Nested Dichotomies for Author Identification System Using Similarity-Based Textual Features;Feature Combination of Pauli and H/A/Alpha Decomposition for Improved Oil Spill Detection Using SAR;a Fast and Efficient Convolutional Neural Network for Fruit Recognition and Classification;copy-Move image Forgery Detection Using Discrete Wavelet Transform;a Comprehensive Survey of Different Phases for Involuntary System for Face Emotion Recognition;classification of Vehicle Type on Indian Road Scene Based on Deep Learning;indian Road Lanes Detection Based on Regression and clustering using Video processing Techniques;detection of Emotion Intensity Using Face Recognition;double Authentication System Based on Face Identification and Lipreading;fuzzy Approach to Evaluate Performance of Teaching Staff in Technical Institutions;safety Gear Check at Industries and Laboratories Using Convolutional Neural Network Based on Deep Learning;analysis of Changing Trends in Textual Data Representation;detection of Falsary Happening on Social Media Using image processing: Feature Extraction and Matching;Development of Multi Faces Recognition System Using HOG Features and Neural Network Classifier in real time Environment;extraction of Key Frame from Random Videos Based On Discrete Cosine Transformation;Prediction of SO2 Air Pollution Quality Parameter of Kolhapur City Using time Series Analysis;A Big Data Prediction for Weather Forecast Using Hybrid ARIMA-ANN time Series Model;automatic Detection of Riots Using Deep Learning;protecting Big Data Sets from Unauthorized Users on Cloud;text Categorization: A Lazy Learning-Based Approach.

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3rd International conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2020

3rd International Conference on Recent Trends in Image Proce...

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3rd International conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2020

ISBN: (纸本)9789811605062

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Near real-time image processing system utilizing Apache Kafka 7

Near real-time image processing system utilizing Apache Kafk...

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7th IEEE International conference on Smart Technologies in Power Engineering and Electronics, STEE 2024

作者： Karpiuk, Nazar Klym, Halyna Lviv Polytechnic National University Department of Specialized Computer Systems Lviv Ukraine

ISBN: (纸本)9798331540999

The ever-growing volume and demand for real- time image processing pose significant challenges for traditional centralized architectures. This paper investigates the development and performance of a distributed environment utilizing Apache Kafka for near real-time image processing. We present a novel approach that decouples image ingestion, processing, and output, enabling parallel processing across a cluster of processing nodes. Apache Kafka serves as the backbone for communication and data flow, providing scalability, fault tolerance, and high throughput. Our evaluation demonstrates significant performance improvements compared to a centralized approach, showcasing the feasibility, benefits and drawbacks of using Kafka for distributed image processing. We analyzed factors impacting performance, including topic partitioning, consumer group configuration, and processing workload. The proposed environment offers a compelling solution for tackling near real-time image processing tasks, paving the way for efficient and scalable image analysis applications. © 2024 IEEE.

关键词： Scalability

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