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

共 13718 条记录，以下是1531-1540 订阅

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Local Adaptive LMS Filtration of Multidimensional images 56

Local Adaptive LMS Filtration of Multidimensional Images

引用

56th International Scientific conference on Information, Communication and Energy systems and Technologies (ICEST)

作者： Mironov, Rumen P. Draganov, ivo R. Tech Univ Sofia Fac Telecommun Bulv Kl Ohridsky 8 Sofia 1000 Bulgaria

ISBN: (纸本)9781665428873

In this paper a new method for local adaptive three dimensional filtration of multidimensional halftone images is presented. The adaptation is based on the local information from the neighbourhood pixels of processed 3D image using generalized 3D LMS filter. An analysis of the quality of the processed multidimensional images is made on the basis of the calculated PSNR, SNR, MSE and the subjective observation. The given experimental results from the simulation in MATLAB environment for operating systems Windows of the developed filtration algorithms, based on a set of real test images are presented. By using the adaptive 3D LMS algorithm the average signal to noise ratio improvement is about 3 dB.

关键词： digital signal processing 3D image processing adaptive filtration LMS adaptation multidimensional image processing

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Analysis of High Performance Low Power Full Adder Circuit

Analysis of High Performance Low Power Full Adder Circuit

引用

Integrated Circuits and Communication systems (ICICACS), IEEE International conference on

作者： P A Shraavya Chaithali R Shetty Nidhi Dhanyashri P Suvarna Roopashree Anush Bekal Dept. of Electronics and Communication Engineering Sahyadri College of Engineering & Management (Affiliated to Visvesvaraya Technological University Belagavi) Mangalore India

Over the past few decades, the electronics sector has experienced significant growth. This is all a result of the development of nanotechnology. The advancement of nanotechnology has allowed for the creation of numerous high-speed microelectronic devices that rely on sophisticated computer techniques to maintain their accuracy and effectiveness. With the advent of very large-scale integration (VLSI) designs, the application of integrated circuits (ICs) in high-performance computing systems, telecommunication devices, video and image processing algorithms, control systems and consumer electronics has substantially risen. The most basic element needed for all this processing and computation is the complete adder. A full adder circuit is a crucial part of arithmetic and logic units (ALUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), and a huge variety of different digital systems and circuits. Greater speed, longer battery life, and other qualities are in higher demand as technology advances. Today, creating complete adder circuits that satisfy the expanding demands is one of the biggest challenges facing VLSI architects. Thus, this research survey has covered the prospective technologies that are currently accessible. With the help of this study, the paper intends to provide a comparative analysis of the available architectures and facilitates to come up with a decision on the one that produces the most beneficial outcomes.

关键词： image processing High performance computing Digital signal processors Signal processing algorithms Computer architecture Very large scale integration Hybrid power systems

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Efficient FPGA Implementation of Syndrome-Assisted Layered Belief Propagation Decoder for CV-QKD systems

Efficient FPGA Implementation of Syndrome-Assisted Layered B...

引用

Communication, image and Signal processing (CCISP), International conference on

作者： Yong Chen Xue-Qin Jiang Genlong Chen Peng Huang Enjian Bai Han Hai School of Information Science and Technology Donghua University Shanghai China State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350356656

ISBN: (纸本)9798350356663

In this paper, we propose a QC-LDPC decoder architecture with low complexity as well as excellent decoding performance in CV-QKD systems, which employs the syndrome-assisted layered belief propagation decoding algorithm. To minimize the complexity of hardware implementation and resource consumption in iterative decoding, we use fixed-point numbers instead of floating-point numbers to update node messages. Additionally, two-segment nonuniform lookup tables are employed to implement $\psi(x)$ efficiently. Besides, the layered syndrome-assisted early termination (LS-ET) scheme is introduced, resulting in the reduction of the number of decoding iterations while maintaining superior frame error rate (FER) performance. The experimental results indicate that the QC-LDPC decoder implemented on an FPGA is capable of achieving throughput of 50.8 Mbps when the code length is 26112 and code rate is 22/68.

关键词： Performance evaluation Codes Signal processing algorithms Throughput Decoding Complexity theory Table lookup Iterative decoding Field programmable gate arrays Belief propagation

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Guided View Approach for Lane Detection and Caution Analysis using Computer Vision and Deep Learning Techniques

Guided View Approach for Lane Detection and Caution Analysis...

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Artificial Intelligence and Data Engineering (AIDE), International conference on

作者： Manjula Gururaj Rao Vinyas S Gururaj Rao H Priyanka Vishal Aisiri NITTE (Deemed to be University) NMAM Institute of Technology Nitte Karkala Sasken Technologies Limited Bangaluru Computer Science & Engineering PESU Bangaluru

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

In recent years, advancements in computer vision technology have enabled innovative applications in various fields, including transportation safety. One such application is a Lane Detection and Caution Measurement Analyzer system aimed at enhancing road safety. This system uses computer vision techniques to provide real-time analysis of road conditions, helping drivers navigate more safely. The Lane Detection module employs image processing algorithms such as edge detection, Hough transform, and perspective transformation to identify lane boundaries accurately. Additionally, the Caution Measurement Analyzer module utilizes object detection and tracking algorithms to identify potential hazards, such as potholes, and measures the caution required for safe driving. The proposed model integrates CNN, OpenCV, and YOLO, achieving 87% accuracy in object detection and caution measurement, and 94% accuracy in lane boundary detection.

关键词： YOLO Computer vision Accuracy Lane detection image edge detection Transforms Road safety Real-time systems Hazards Vehicles

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Deep Learning-Driven Black Box Approach For image Segmentation

Deep Learning-Driven Black Box Approach For Image Segmentati...

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Advances in Computing, Communication and Networking (ICAC2N), International conference on

作者： Nuthalapati Sudha Sai Bhargav Kasetty K. Sai Madhuri Jajala Nikitha Issac Neha Margret K. Rajakumar School of Engineering Mahindra University Hyderabad Telangana India School of Computer Science and Engineering V.I.T – University Vellore Tamilnadu India Dept. of Computer Science and Data Science BVRIT Narsapur Hyderabad Telangana India Dept. of CSE – AIML & IoT VNR VIET Hyderabad Telangana India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

The diagnosis of a range of eye disorders needs to categorize the retinal vessels. Computerized implementation of this process is becoming increasingly essential for automated screening systems for retinal diseases. To achieve a more accurate extraction of the retinal vessels, a new pre-processing step is proposed. These proposed pre-processes are also compared to other algorithms to assess their impact. The proposed pre-processing process consists of two phases. The first phase is the implementation and validation of the pre-processing modules, and the second phase is the implementation of these pre-processes onto the retinal vessels that were to be extracted. To achieve a significantly improved segmented vessel image, the proposed pre-process phase employs a common image-processing technique. In recent years, there has been a great deal of focus on retinal vessel identification studies, and the importance of assessing and confirming the findings of retinal vessel segmentation.

关键词： image segmentation Analytical models Accuracy Computational modeling Retinal vessels Diseases

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Video Smoke Detection Algorithm Based on a Spatial-Temporal Neural Network Model

Video Smoke Detection Algorithm Based on a Spatial-Temporal ...

引用

6th International conference on Intelligent Computing and Signal processing (ICSP)

作者： Zhen Cao Xi Zhang Ministry of Emergency Management Shenyang Fire Science and Technology Research Institute Shengyang China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

a kind of spatial-temporal neural network video smoke detection algorithm is proposed in order to solve the problems associated with the incorrect classification of the static approximate smoke background in the face of the detection of smoke in video detection networks, and the problem of false alarms and of the original test model algorithms being different in different detection environments. Based on the original YOLO v4 neural network algorithm, this paper introduces a k-means + + algorithm and genetic algorithm, while using the algorithm's clustering function to classify the sample points of the real boxes of the image data set, which make it a more suitable anchor. At the same time, the genetic algorithm is used to adjust its anchor in order to allow the generated anchor to adapt to the needs related to smoke detection. In the original neural network model, the dual-stream network model algorithm is used to extract information from the first step of the YOLO algorithm in order to further filter the smoke's characteristics as well as filter out error information, all to improve the detection capabilities of the overall neural network for video smoke fog images. Compared with traditional YOLOv4 networks, the algorithm obtained by the model algorithm has been improved by 8.51°/0. In actual tests, the alarm time requirements of the smoke alarm test program for early fire monitoring and the alarm systems for visual images were improved, and the detection accuracy of the network was also improved based on the assurance of the detection speed, while the performance of the model algorithm was also improved for different scenes.

关键词： YOLO image recognition Neural networks Clustering algorithms Lighting Filtering algorithms Approximation algorithms Information filters Classification algorithms Genetic algorithms

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Design and implementation of interactive image measurement software based on Tkinter and PIL library 25

Design and implementation of interactive image measurement s...

引用

Proceedings of the 4th International conference on Computer, Artificial Intelligence and Control Engineering

作者： Xiao He Zhulin Wang Shaozheng Zhan National Key Laboratory of Strength and Structural Integrity Xi' an Shaanxi China Aircraft Strength Research Institute of China Xi' an Shaanxi China

ISBN: (纸本)9798400712647

In this paper, we design and implement a high-precision image processing and measurement software based on Python. The software utilizes the Tkinter and PIL libraries to build a graphical user interface and integrates a variety of commonly used image processing algorithms, including grayscale conversion, edge extraction, Gaussian blur, image sharpening, binarization, and median filtering, enabling effective image enhancement and segmentation. In addition, the software provides interactive measurement functionality, allowing users to draw rectangular regions on the image using the mouse and automatically calculate the dimensions of these rectangles. Measurement results can be stored, displayed, and managed, facilitating users in analyzing and processing the data. To evaluate the performance and reliability of the software, we conducted experiments on multiple datasets. The experimental results demonstrate that the software can accurately process images of different types and qualities and provide stable and reliable measurement results. Compared with the existing C-scan measurement software of current equipment, the software developed in this study has certain advantages in terms of functional integration, interactivity, and measurement accuracy. The design and implementation of this software can meet the need to replace the existing C-scan system software, while also providing valuable references and insights for the further development of intelligent and automated image analysis systems.

关键词： PIL

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Low Complexity Continuous Rain Density Estimation Using RISTA Technique for Single image Rain Streak Removal

Low Complexity Continuous Rain Density Estimation Using RIST...

引用

IEEE International conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Ko-Yang Chao Yu-Hsuan Lee Dept. of Electrical Engineering Yuan-Ze University Taoyuan City R.O.C.

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

image deraining is an image processing technique to restore a rainy image back to rain-free one. Iterative Soft Thresholding Algorithm (ISTA) is an iterative procedure to estimate sparsity. ISTA demands considerable computation burdens of Floating Point Operations (FLOPs), limiting its applications for real-time requirement. The proposed Restored-Feature Iterative Soft Thresholding Algorithm (RISTA) provides a low complexity approach to reduce FLOPs. Experiment results demonstrate this work has a computation saving ratio up to 66%, while maintaining a Peak Signal-to-Noise Ratio (PSNR) error as low as 6%.

关键词： Rain Thresholding (Imaging) PSNR Limiting Estimation Iterative algorithms Real-time systems

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From Linear to Nonlinear Unfolded Condat-Vú Algorithm for Spectro-Polarimetric Hight-Constrast image Recovery

From Linear to Nonlinear Unfolded Condat-Vú Algorithm for S...

引用

European Signal processing conference (EUSIPCO)

作者： E. Chappon N. Pustelnik J. Tachella L. Denneulin A. Ferrari M. Langlois Laboratoire de Physique ENSL CNRS UMR 5672 Lyon France Laboratoire de Recherche de l'EPITA EPITA Le Kremlin-Bicêtre France Universite Côte d'Azur Observatoire de la Côte d'Azur CNRS Lab. J.-L. Lagrange France Univ Lyon Univ. Lyon 1 ENS de Lyon CNRS Centre de Recherche Astrophysique de Lyon UMR5574 Saint-Genis-Laval France

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Studying circumstellar environments is crucial for understanding exoplanets and stellar systems. Instruments like SPHERE can extract information about these environments by leveraging advanced image reconstruction methods, possibly based on deep learning. This work focuses on unfolded proximal neural networks based on Condat- Vii iterations and proposes a new nonlinear formulation. To evaluate and compare the performance of the proposed reconstruction strategies, two datasets dedicated to circumstellar environments analysis in the context of high-contrast imagery have been created offering different level of complexity in the evaluation of the performance.

关键词： Deep learning Training image color analysis Neural networks Signal processing algorithms Signal processing image reconstruction Standards Synthetic data Finite difference methods

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Quantum Computing for image processing and Artificial Vision: Innovations in Advanced Quantum Physics

Quantum Computing for Image Processing and Artificial Vision...

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International conference on Pattern Analysis and Intelligent systems (PAIS)

作者： Tarek Nouioua Nour El-Houda Yahiaoui Department of Mathematics and Computer Sciences Faculty of Exact Sciences and Sciences of Nature and Life University of Tebessa Tebessa Algeria Residential Formation Photonics Optics Professional Formation Center Dris Mohamed El-Hadi Tebessa Algeria

This paper delves into the groundbreaking potential of quantum computing, with a primary focus on qubits and their wide-ranging applications. It explores the fundamental principles of quantum physics, such as entanglement and superposition, demonstrating how these principles underpin innovative approaches to data representation and processing. Real-world demonstrations utilizing quantum hardware underscore the exponential advantages that quantum computing offers in terms of both speed and storage capacity. Within the realm of quantum computing, one major objective is to address the issue of decoherence, and the article proposes the utilization of photons as qubits due to their virtually infinite decoherence times. This introduction of Photonic quantum computing paves the way for a robust and scalable quantum computing approach, with specific objectives centered around enhancing the stability and scalability of quantum systems. Furthermore, the article explores the fascinating domain of quantum image processing, aiming to revolutionize the fields of image analysis and compression. Quantum image representations are envisioned to significantly enhance storage efficiency, and the article sets objectives to develop practical quantum algorithms for image processing tasks. To illustrate the practicality of these advancements, the article showcases a real-world implementation of quantum edge detection, emphasizing its promising applications not only in medical imaging but also in advancing artificial vision systems.

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