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

共 13662 条记录，以下是971-980 订阅

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Removal of Impulse Noise from Colour image Using Different Variations of Non-Linear Filters

Removal of Impulse Noise from Colour Image Using Different V...

引用

Engineering and Technology (PICET), Parul International conference on

作者： Aakanksha Jain Harshal Arolkar GLS University Ahmedabad India

ISBN: (数字)9798350369748

ISBN: (纸本)9798350369755

Digital images are affected by a variety of noise and one well-known type is impulsive noise. In order to reduce or eliminate noise, many image-denoising algorithms have been created, with varying benefits and limitations. To deal with impulsive and spurious noise in colour images, this study does a thorough examination with an emphasis on the median filter and its various variants. By means of thorough experimentation, the researchers examine the relative performance of different denoising algorithms using Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR) as standards. The results of the study demonstrate the usefulness of the Vector Median filter, especially in situations with high-density impulsive noise. The Vector Median filter is particularly effective for real-time image processing applications since it performs better and requires less processing time. Furthermore, the Modified Median filter, with its high PSNR and low MSE values, shows potential as a low-density noise solution. This study offers insightful information about image eliminating techniques aimed at reducing impulsive noise in colour images. The study advances the field of image processing by utilising creative methodologies and performance measurements. This has significance for other sectors that depend on accurate image analysis. The study also sets the basis for next investigations focused on improving and expanding the range of denoising algorithms to handle a greater variety of noise kinds and intensities.

关键词： PSNR image color analysis Noise reduction Mean square error methods Filtering algorithms Information filters Vectors Real-time systems Colored noise Standards

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Accurate Segmentation and Classification of Brain Tumor using Deep Learning Approaches 2

Accurate Segmentation and Classification of Brain Tumor usin...

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2nd IEEE International conference on Advances in Information Technology, ICAIT 2024

作者： Benedict, J.N. Shanmugapriya, S. Senthil Pandi, S. Kumar, P. Department of CSE Rajalakshmi Engineering College Chennai India Department of CSE Kalasalingam Academy of Research and Education Srivilliputtur India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383867

Brain tumors are abnormal growths of tissue within the skull, posing serious health risks including brain cancer and related diseases. In healthcare, there's a growing focus on early detection methods, facilitated by surveillance decision support systems, to identify brain tumors promptly. Among the leading techniques for detection is the analysis of Magnetic Resonance images (MRIs), which provide crucial information about abnormal tissues. Researchers and doctors are actively exploring ways to improve the accuracy and efficiency of brain tumor detection through advanced imaging technologies and data analysis algorithms. By enhancing early detection capabilities, healthcare professionals aim to improve patient outcomes and facilitate timely interventions for individuals at risk of developing brain tumors. This study aims to identify and localize tumor areas in the brain through the utilization of advanced image processing techniques and a deep wavelet autoencoder method. Initially, a specialized clustering technique is applied to MRI images to segment the tumor areas. Subsequently, the detected tumor regions are refined using thresholding and level-set segmentation methods to achieve accurate delineation. Finally, a deep wavelet autoencoder is employed for precise classification of brain tumors based on the extracted features. Evaluation metrics are utilized to assess the performance of the proposed model, demonstrating its superiority over existing state-of-the-art approaches across all evaluation criteria. The combination of clustering, segmentation, and deep learning techniques allows for the efficient recognition and localization of brain tumors, providing valuable insights for medical diagnosis and treatment planning. The robustness and accuracy of the proposed model make it a promising tool for enhancing the efficiency and efficacy of brain tumor detection in clinical practice. © 2024 IEEE.

关键词： image segmentation

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Construction Site Fence Recognition Method Based on Multi-Scale Attention Fusion ENet Segmentation Network 35

Construction Site Fence Recognition Method Based on Multi-Sc...

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35th International conference on Software Engineering and Knowledge Engineering, SEKE 2023

作者： Zhang, Xing Tang, Tinglong Wu, Yirong Quan, Tingwei Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges University Yichang China Wuhan National Laboratory for Optelectronics Huazhong University of Science and Technology Wuhan China

In this paper, we propose a fence recognition method based on the ENet (Efficient neural Network) segmentation network to address the problems of traditional segmentation networks, which have poor performance in recognizing fences with a large range of scale variations and hollow structures. Firstly, a multi-scale attention fusion ENet segmentation network is designed, which is trained using the fence with obvious color features. Then, a morphological algorithm is used to process the predicted image to restore the fence segmentation results. The designed multi-scale attention fusion segmentation network performs better on fence datasets than traditional methods. In addition, the activation function Leaky_Relu6 further enhances the stability and generalization ability of the network. The experiments are conducted on 540 fence images from different construction sites, and the computed IoU is 90%. The processing speed is about 28 frames per second. The experimental results show that our proposed network outperforms traditional segmentation algorithms in fence recognition performance, and achieves robustness in different construction scenarios while meeting the requirements of both accuracy and speed. © 2023 Knowledge systems Institute Graduate School. All rights reserved.

关键词： image segmentation

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Automatic Recognition of Non-standard Number Plates using YOLOv8 18

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

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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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A Two-Stage Multispectral Demosaicking Method Using Neville Filters and Directional Interpolation

A Two-Stage Multispectral Demosaicking Method Using Neville ...

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Signal, Information and Data processing (ICSIDP), IEEE International conference on

作者： Wenzhuo Yang Yakun Liu Yan Shi Wei Li Ran Tao School of Information and Electronics Beijing Institute of Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Snapshot multispectral imaging systems typically capture multispectral images in a single shot by covering the sensor with a multispectral filter array (MSFA). A demosaicking algorithm is generally required for such systems to reconstruct the full-resolution multispectral images. A two-stage demosaicking method is proposed in this paper. In the first stage, the image is progressively interpolated by Neville filters. In the second stage, the directional interpolation with a novel weighting function is performed to enhance the quality of the pre-interpolated image. Experimental results demonstrate that the proposed approach achieves superior performance in both subjective and objective assessments.

关键词： Interpolation Multispectral imaging Array signal processing Filtering algorithms Information filters Data processing image reconstruction Sensor arrays

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Research on image processing algorithms Based on Variational and Deep Learning

Research on Image Processing Algorithms Based on Variational...

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Mechatronics, IoT and Industrial Informatics (ICMIII), International conference on

作者： Xiaoxu Zhang Heilongjiang Polytechnic Harbin China

ISBN: (数字)9798350386639

ISBN: (纸本)9798350386646

China has seen an unheard-of surge in interest in deep-learning methods for image restoration in recent years. Most of these strategies draw inspiration from the established variational technique and related optimization methods for the picture reconstruction inverse issue. While using learnable components to create organized deep neural networks and using copious amounts of observation data to train the networks for the particular reconstruction objectives, these techniques resemble the iterative strategies of ordinary optimization algorithms. In many cases, they have proven to have far better empirical performance than the conventional approaches, and they also demand a lot lower computing cost. For various common networks in this subject, this research offers the specifics of the derivations, the network topologies, and the training protocol. The research therefore focuses on image processing algorithms based on variational and deep learning models.

关键词： Training Deep learning Sensitivity Protocols Optimization methods Visual systems Retina

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Comparing the Effectiveness and Performance of image processing algorithms in Face Recognition

Comparing the Effectiveness and Performance of Image Process...

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International Carpathian Control conference (ICCC)

作者： Hussam M. Rostum József Vásárhelyi Institute of Automation and Info-Communication University of Miskolc Miskolc Hungary

ISBN: (数字)9798350350708

ISBN: (纸本)9798350350715

Many institutions have recently embraced biometric security solutions, utilizing biological measurements to safeguard against fraudulent activities, theft, and various security threats. Face recognition technology holds a pivotal role within the realm of bio-metric security systems, serving purposes such as authentication, monitoring, individual identification, and identity verification. This article aims to delve into the examination of facial recognition systems grounded in deep learning. This focus arises due to the intricate nature of the process and the existence of numerous hurdles and variables that impact algorithm performance. The objective here is to illuminate the foremost challenges that real-world systems encounter, often overlooked in previous research. Additionally,under these challenges, the article will conduct a comparative analysis of the performance of prominent facial recognition algorithms, namely VGGFace, FaceNet, and ArcFace. This academic approach will allow to make informed choices when selecting the most suitable algorithms for specific applications.

关键词： Deep learning Employee welfare Databases Face recognition image processing Lighting Biology

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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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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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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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