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

共 13664 条记录，以下是981-990 订阅

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

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

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

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

引用

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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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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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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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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4th International conference on Applied Technologies, ICAT 2022

4th International Conference on Applied Technologies, ICAT 2...

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4th International conference on Applied Technologies, ICAT 2022

ISBN: (纸本)9783031249709

The proceedings contain 113 papers. The special focus in this conference is on Applied Technologies. The topics include: Automatic Evaluation of Physiotherapy Activities Using Deep Learning Techniques;multivariable Control Approach Applied to the Embryo Incubation Process of Gallus Gallus Domesticus;discovering Visual Deficiencies in Pilots Candidates Using Data Mining;an Electronic Equipment for Measuring Color Difference Between Tissues Based on Digital image processing and Neural Networks;artificial Intelligence-Based Banana Ripeness Detection;U-Net vs. TransUNet: Performance Comparison in Medical image Segmentation;access Control Through Mask Detection and Estimation of People Capacity in Covered Premises;customer Segmentation in Food Retail Sector: An Approach from Customer Behavior and Product Association Rules;Bibliometric Analysis of Web of Science Database STEM Fields in Engineering and Mathematics. Ecuador’s Case Study;smart Antenna Array for Optimal Electromagnetic Energy Capture;image processing Method to Estimate Water Quality Parameter;use of the Student Engagement as a Strategy to Optimize Online Education, Applying a Supervised Machine Learning Model Using Facial Recognition;crime Data Analysis Using Machine Learning Models;Gene Therapy as a Solution to Genetic Diseases Through DNA Manipulation;Vulnerability of CAPTCHA systems Using Bots with Computer Vision Abilities;military Leadership in the Ecuadorian Army;analysis of Key Variables for Ecuadorian Defense Industry Development;the Prioritization of External Security as a Means of Guaranteeing Multidimensional Security and Economic Growth;Comparative Study of Deep Learning algorithms in the Detection of Phishing Attacks Based on HTML and Text Obtained from Web Pages;ground Robot for Search and Rescue Management;virtual Training Module for the Production of Rubber Adhesives Through the Production of Cyclopentenol.

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4th International conference on Applied Technologies, ICAT 2022

4th International Conference on Applied Technologies, ICAT 2...

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4th International conference on Applied Technologies, ICAT 2022

ISBN: (纸本)9783031249778

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Abnormal Behavior Detection in Electronic-Exam Videos Using BeatGAN 8

Abnormal Behavior Detection in Electronic-Exam Videos Using ...

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8th International Iranian conference on Signal processing and Intelligent systems, ICSPIS 2022

作者： Atabay, Habibollah Agh Hassanpour, Hamid Shahrood University of Technology Image Processing & Data Mining Lab Shahrood Iran

ISBN: (纸本)9781665476232

Abnormal Behavior Detection is a core ability to recognize cheating in electronic exams (e-exams), especially in the scenes where a fraudulent candidate hides unauthorized resources from the view of the proctor. This work utilizes the BeatGAN algorithm to detect behavioral abnormalities in the skeleton-based motion information of candidates during remote e-exams. We examine the algorithm on the previously published dataset of skeleton features extracted from e-exam videos. The results show that BeatGAN outperforms previously investigated algorithms in sequence anomaly detection using the raw joint-coordinate, implicit bone-length-and-angle and explicit bone-length feature types. But, in event anomaly detection, BeatGAN does not improve the state-of-the-art results on the dataset. © 2022 IEEE.

关键词： Anomaly detection

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A Comparative Analysis of Branch-Cut and Quality-Guided algorithms For inSAR Interferogram

A Comparative Analysis of Branch-Cut and Quality-Guided Algo...

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International conference on image and Signal processing and their Applications (ISPA)

作者： Saoussen Djeddi Tarek Bentahar Atef Bentahar Riad Saidi Hanane Djellab Laboratory of Mathematics Informatics and Systems (LAMIS) Echahid Cheikh Larbi Tebessi University Tebessa Algeria Laboratory of Electrical Engineering-Telecommunications (LABGET) Echahid Cheikh Larbi Tebessi University Tebessa Algeria Dept Commun Base Science and Technology Mustapha Ben Boulaid Batna2 University Batna Algeria LTI Laboratory of Guelma Echahid Cheikh Larbi Tebessi University Tebessa Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Interferometric synthetic aperture radar (inSAR) phase image is a key for the digital elevation model of earth mapping. To achieve this three-dimensional reconstruction, phase unwrapping process must be performed whitch is used to calculate the accurate elevations from the wrapped phase map. For the noise-free images, this process is just a simple integration of the wrapped gradient. But in reality, there is no phase image without noise, therefore the phase unwrapping have to be adaptive with a strong immunity to noise. Several adaptive algorithms have been proposed in such area where Goldstein’s branch-cut and Flynn’s quality-guided are the widely known in the path-following category, they are the most used methods and all other propositions are just enhancements or hybridizations. In this paper, we analyze the performance of each one and provide the substantial difference between them. Both algorithms are implemented using simulated and real inSAR data of different patterns and are analyzed under several relevant criteria.

关键词： image quality Earth Noise Signal processing algorithms Adaptive algorithms Noise measurement Synthetic aperture radar

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A Review on the Applications of Machine Learning and Deep Learning algorithms for image Recognition

A Review on the Applications of Machine Learning and Deep Le...

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Sentiment Analysis and Deep Learning (ICSADL), International conference on

作者： Ketan Wanjari Prateek Verma Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (Deemed to be University) Sawangi (Meghe) Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (Deemed to be University) Sawangi (Meghe) Wardha Maharashtra India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Modern image recognition has experienced dramatic improvements because of Machine Learning and Deep Learning algorithms together. This study investigates CNNs and SVMs for recognition enhancement while reviewing image recognition technologies extensively through literature. This paper demonstrates how the applications of healthcare and security systems and social media analysis influence society. New developments in technology have not resolved multiple unresolved obstacles such as data bias as well as computational complexity and privacy concerns together with real-time processing restrictions. The latest image processing techniques include ViTs alongside GANs and Few-Shot Learning but developers need to achieve better results in future improvements. The main goal of this research examines present-day advancements in ML and DL with a review of their capabilities as well as constraints before recommending future study paths to overcome problems encountered today. This paper evaluates both the future potential and benefits alongside drawbacks of ML and DL models applied to image recognition.

关键词： Deep learning Ethics Data privacy image recognition Machine learning algorithms Reviews Social networking (online) Vectors Security Stakeholders

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