With the continuous progress of imageprocessing and machine vision technology, the demand for efficient and real-timeprocessing is becoming more and more prominent, especially in the field of high-noise image proces...
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ISBN:
(纸本)9798350377040;9798350377033
With the continuous progress of imageprocessing and machine vision technology, the demand for efficient and real-timeprocessing is becoming more and more prominent, especially in the field of high-noise imageprocessing. 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 imageprocessing 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 imageprocessing in constrained environments.
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 (I...
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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 imageprocessing, our system provides integrated imageprocessing support for robust sensor-to-surveillance. The imageprocessing 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 imageprocessing algorithms.
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 ...
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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.
real-time optical imageprocessing has become a critical technology in domains such as autonomous systems, medical diagnostics, and surveillance. However, traditional centralized processing approaches face challenges ...
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In the rapidly evolving realm of machine learning, the integration of the Open Neural Network Exchange (ONNX) has become increasingly significant, particularly in imageprocessing applications. This study conducts a c...
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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 imageprocessing applications. This study conducts a comprehensive examination of the role of ONNX in enhancing imageprocessing 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 imageprocessing 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 imageprocessingtimes, 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 imageprocessing. The ongoing developments and active community engagement point towards a promising future for more rapid and efficient imageprocessing methods.
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 imageprocessing...
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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 imageprocessing 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 imageprocessing in real-time applications while conserving computational resources and energy consumption.
Multiply-Accumulate (MAC) operation is widely used in various real-timeimageprocessing tasks, ranging from Convolutional Neural Networks to digital filtering, significantly impacting overall system performance. In t...
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ISBN:
(纸本)9781510673199;9781510673182
Multiply-Accumulate (MAC) operation is widely used in various real-timeimageprocessing 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-timeimageprocessing 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-timeimageprocessing 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-timeimageprocessing applications.
The proceedings contain 78 papers. The special focus in this conference is on Recent Trends in imageprocessing and Pattern Recognition. The topics include: Ensemble of Nested Dichotomies for Author Identification Sys...
ISBN:
(纸本)9789811604928
The proceedings contain 78 papers. The special focus in this conference is on Recent Trends in imageprocessing 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 imageprocessing: Feature Extraction and Matching;Development of Multi Faces Recognition System Using HOG Features and Neural Network Classifier in realtime 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.
The proceedings contain 78 papers. The special focus in this conference is on Recent Trends in imageprocessing and Pattern Recognition. The topics include: Ensemble of Nested Dichotomies for Author Identification Sys...
ISBN:
(纸本)9789811605062
The proceedings contain 78 papers. The special focus in this conference is on Recent Trends in imageprocessing 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 imageprocessing: Feature Extraction and Matching;Development of Multi Faces Recognition System Using HOG Features and Neural Network Classifier in realtime 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.
The ever-growing volume and demand for real- timeimageprocessing pose significant challenges for traditional centralized architectures. This paper investigates the development and performance of a distributed enviro...
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