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

共 12978 条记录，以下是951-960 订阅

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A Conversion Algorithm for ECG signals on a 2D array based on Digital Signal processing 11

A Conversion Algorithm for ECG signals on a 2D array based o...

引用

11th International conference on Communications, Circuits and systems (ICCCAS)

作者： Cesar Cabanillas, Julio Kemper, Guillermo del Carpio, Christian Univ Peruana Ciencias Aplicadas Fac Engn Sch Elect Engn Lima Peru

ISBN: (数字)9781665469890

ISBN: (纸本)9781665469890

This work proposes a computational algorithm to convert digital files containing electrocardiogram (ECG) information into 1D signals. Many medical databases have in storage files containing ECG information that is not easy to process for computational algorithms. Digitization by the proposed method makes it possible to modernize the databases of many health centers in order to perform post-processing of the signals obtained. This method is based on applying digital signal processing techniques to images obtained from a PDF file produced by an electrocardiograph. The proposed algorithm takes into consideration the thickness of the printed signal in the PDF image so that it does not introduce distortion in the final 1D signal. Due to the distribution of the ECG signals on the PDF files the algorithm identifies and segments the signals on 2 dimensions. The results show that the proposed method can correctly reproduce the information of the ECG waves captured in the PDF file regardless of the elements outside the ECG signal such as the background grid or the different information indicators, whether they are labels or references of the ECG signals. The algorithm has an accuracy of 95% based on the statistical analysis performed for all samples.

关键词： Digital Signal processing ECG signals Conversion Printed Signal Digitization 2D array

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Automatic Recognition of Non-standard Number Plates using YOLOv8 18

Automatic Recognition of Non-standard Number Plates using YO...

引用

18th INDIAcom;11th International conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Tupsounder, Asaavi Patwari, Renuka Ambati, Roja Chavarkar, Arya Shirsat, Kavita Vidyalankar Institute of Technology Dept. of Computer Engineering Mumbai India

ISBN: (纸本)9789380544519

A critical feature of vehicle movement applications is the detection and identification of a vehicle's License Plate (LP). Despite technological and algorithm developments, differences in LP properties by nation, such as number system, colors, character language, fonts, and size, necessitate more study to enhance detection and recognition. Despite substantial study, many systems function in well-defined contexts, necessitating the use of expensive equipment to capture photos from slow-moving vehicles or generate high-quality shots. This study describes a deep learning-based automated approach for LP detection and recognition, which is further separated into detection and character recognition. Yolov8 (You Only Look Once Version-8) was utilized for automatic pixel separation and gradient computation, making complicated model training easier. Using deep learning, Easy OCR (Optical Character Recognition) was utilized to retrieve text from number plates. Easy OCR (Optical Character Recognition) was utilized to extract text from license plates, with deep learning algorithms employed to effectively read text in difficult settings. For image and video processing, OpenCV (Open-Source Computer Vision Library) was employed. The project titled 'Automatic Number Plate Using YOLOv8 Model' leverages the advanced capabilities of the YOLOv8 model to develop a robust system for automated number plate recognition, serving as the core component for real-time object detection and number plate localization within images and video streams. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： ANPR (Automatic Number Plate Recognition) Deep learning image Recognition Non-Standard OpenCV YOLOv8

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Advances in Database Technology - Proceedings: 27th International conference on Extending Database Technology, EDBT 2024

Advances in Database Technology - Proceedings: 27th Internat...

引用

27th International conference on Extending Database Technology, EDBT 2024

ISBN: (纸本)9783893180943

The proceedings contain 87 papers. The topics discussed include: Auto-FP: an experimental study of automated feature preprocessing for tabular data;Data-CASE: grounding data regulations for compliant data processing systems;data coverage for detecting representation bias in image datasets: a crowdsourcing approach;balancing utility and fairness in submodular maximization;stateful entities: object-oriented cloud applications as distributed dataflows;learning over sets for databases;a new PET for data collection via forms with data minimization, full accuracy and informed consent;adaptive compression for databases;analysis of open government datasets from a data design and integration perspective;fine-grained geo-obfuscation to protect workers’ location privacy in time-sensitive spatial crowdsourcing;and a framework to evaluate early time-series classification algorithms.

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Anisotropic Diffusion Method on Multiple Domain Noisy images: A Recommendation

Anisotropic Diffusion Method on Multiple Domain Noisy Images...

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Advances in Data Engineering and Intelligent Computing systems (ADICS), International conference on

作者： Palanisamy V Muthukumaran Malarvel Thangakumar J Department of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

Noise poses a maj or challenge to image processing, making accurate analysis and interpretation more difficult. Anisotropic diffusion algorithms specifically tailored for noisy images across several domains are examined in this paper. The efficiency of anisotropic diffusion in lowering noise in various image datasets is evaluated in-depth in this study. Based on comprehensive study and experimentation, this work presents real proof of the efficacy of this method in decreasing noise while preserving significant image properties in multiple domains. In comparison to the MP and MPM models, the experimental findings show that the suggested model performs quite well.

关键词： Anisotropic magnetoresistance image processing image edge detection Noise Noise reduction Anisotropic Data engineering

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Beyond the Pixels: Detecting Originality and Assessing Product Condition using Digital image processing Techniques

Beyond the Pixels: Detecting Originality and Assessing Produ...

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International conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Ishit Arhatia Bryan Abraham Department of Computing Technologies SRM Institute of Science and Technology India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

This paper introduces a novel approach using digital image processing for product management, focusing on authenticity verification and quality assessment. Advanced algorithms distinguish genuine from fraudulent products and evaluate their condition. The study highlights the potential of digital image processing to enhance supply chain security and consumer trust, promising significant business impacts by reducing counterfeit circulation, improving brand reputation, and optimizing inventory management

关键词： Computers Digital images Supply chains Focusing Data integration Inventory management Real-time systems

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Advances in Database Technology - Proceedings: 27th International conference on Extending Database Technology, EDBT 2024

Advances in Database Technology - Proceedings: 27th Internat...

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27th International conference on Extending Database Technology, EDBT 2024

ISBN: (纸本)9783893180943

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Applications of Vedic multiplier - A Review

Applications of Vedic multiplier - A Review

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2021 Virtual National conference on Materials for Electronics Applications, ViNCMEA 2021

作者： Khubnani, Rashi Sharma, Tarunika Subramanyam, Chitirala New Horizon College of Engineering Bangalore India

Vedic Multiplier is a key tool in rapidly growing technology especially in the immense domain of image processing, Digital Signal processing, real-time signal. Multipliers are important block in digital systems and play a critical role in digital designs. Along with accuracy demand for minimizing time area, power, and delay of the processor by enhancing speed is the focus point. Vedic mathematics rules and algorithms generate partial products concurrently and save time. This paper is a review of the application and modification of Vedic multiplier in different fields and a comparison of Vedic multiplier with other multipliers for enhancing performance parameters. © Published under licence by IOP Publishing Ltd.

关键词： Digital signal processing

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Synergizing Support Vector Machines and image processing for Superior Skin Cancer Detection : A Crammer-Singer Approach

Synergizing Support Vector Machines and Image Processing for...

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International conference on Communication and Electronics systems (ICCES)

作者： S. Divya S. Thilagavathi Department of Computer Sciene Sri Krishna Adithya College of Arts and Science Coimbatore India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

Skin cancer, which primarily impacts skin exposed to ultraviolet (UV) rays against the sun, represents dangerous to the most significant organs in the human body, the skin. Usually, a spot, lump, or mole that appears on the skin is the primary suspicion of skin cancer. However, each of these can undergo changes in coloring or shape as time passes. Recovery for skin cancer is mostly possible if the disease is discovered early. Numerous medical diagnostic methods, such as Dermoscopy, biopsy, and ocular examination of the affected area, are useful in helping anticipate the development of skin cancer. However, these approaches have the disadvantage of delivering erroneous results because it is extremely difficult to distinguish between normal and malignant skin. Therefore, the drawback of these diagnostic procedures is that machine learning algorithms are currently used together with image processing techniques to examine the images for the purpose of precisely identifying skin cancer. The current research employs the ISIC dataset to develop a novel model for skin cancer classification that combines image processing techniques with advanced machine learning methods, including Crammer-Singer Support vector machine learning algorithms. The categorization of skin cancer begins with preprocessing the input image, which includes hair removal using a morphological filter and image enhancement using a median filter to minimize noise and increase image clarity. The ABCD approach is used to segment lesion images by evaluating them for asymmetry, border irregularity, color variability, and diameter. The crammer-Singer SVM algorithm is then used with these images to classify skin lesions into various types such as melanoma (MEL), melanocytic nevus (NV), basal cell carcinoma (BCC), actinic keratosis (AK), benign keratosis (BKL), dermatofibroma (DF), vascular lesion (VASC), and squamous cell carcinoma (SCC), leveraging its robust multi-class handling capabilities. The system achieve

关键词： Support vector machines image segmentation Machine learning algorithms Accuracy Filtering algorithms Skin Classification algorithms Lesions image enhancement Skin cancer

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AINoC: New Interconnect for Future Deep Neural Network Accelerators 16th

AINoC: New Interconnect for Future Deep Neural Network Accel...

引用

16th International Workshop on Design and Architecture for Signal and image processing (DASIP) held jointly with the 18th HiPEAC conference

作者： Krichene, Hana Prasad, Rohit Mouhagir, Ayoub Univ Paris Saclay List CEA F-91120 Palaiseau France

ISBN: (纸本)9783031299698;9783031299704

Data exchanges can be significant in the Deep Neural Network (DNN) algorithms. The interconnection between computing resources can quickly have a substantial impact on the overall performance of the architecture and its energy efficiency. Similarly, access to the different memories of the system, with potentially high data sharing, is a critical point. To overcome these problems, in this paper, we propose a new interconnect network, called AINoC, for future DNN accelerators, which require more flexibility and less power consumption to facilitate their integration into artificial intelligence (AI) edge systems. AINoC is based on (1) parallel routing that ensures communication/computation overlap to improve performance and (2) data reuse (filters, image inputs, and partial sums) to reduce multiple memory accesses. In the experiment section, AINoC can speedup LeNet5 convolution layers by up to 71.74x in latency performance w.r.t. a RISC-V-based CPU and also speedup MobileNetV2 convolution layers by up to 2.35x in latency performance w.r.t. a dataflow architecture featuring row-stationary execution style. AINoC provides any-to-any data exchange with wide interfaces (up to 51.2 GB/s) to support long bursts (up to 384 flits/cycle with packed data, i.e., 3 * 8-bit data in a 32-bit wide datapath) while executing LeNet5 and MobileNetV2. AINoC supports flexible communication with many multicast/broadcast requests with non-blocking transfers. Parallel communication in AINoC can provide up to 128x more throughput (flits/cycle) and bandwidth (GB/s), using parallel routing with respect to single-path routing while executing convolution layers of LeNet5 and MobiletNetV2.

关键词： On-Chip Interconnect Network-on-Chip DNN CNN dataflow execution parallel processing

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Efficiency of deep neural networks for reinforced concrete shear walls failure mode detection 27

Efficiency of deep neural networks for reinforced concrete s...

引用

27th International Computer conference, Computer Society of Iran, CSICC 2022

作者： Barkhordari, Mohammad Sadegh Massone, Leonardo M. Department Of Civil And Environmental Engineering Iran University Of Chile Department Of Civil Engineering Santiago Chile

ISBN: (纸本)9781665480277

After a severe earthquake, it is critical to check the health of hospitals, buildings, fire stations, to name but a few. New technologies such as deep learning algorithms, image processing, and signal processing methods have become more appealing in such quick applications because of their higher reliabilities and efficiency than traditional visual methods. On the other hand, reinforced concrete shear walls are the key structural elements of the most buildings as lateral force resisting systems in seismic areas. Determining the failure mode of reinforced concrete shear walls is a crucial task that can help engineers and designers choose the best retrofitting options. The goal of this research is to assess the ability of deep neural networks for predicting the failure mode of the reinforced concrete shear walls. Furthermore, the SHapley Additive exPlanation approach is used to demonstrate the relevance and contribution of the factors that influence the reinforced concrete shear walls failure mode determination. © 2022 IEEE.

关键词： Deep learning Visualization Buildings Neurons Signal processing algorithms Signal processing Concrete

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