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检索条件"任意字段=18th Image Processing: Algorithms and Systems Conference, IPAS 2020"

共 258 条记录，以下是141-150 订阅

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A ringing metric to evaluate the quality of images restored using iterative deconvolution algorithms

A ringing metric to evaluate the quality of images restored ...

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IEEE International conference on systems Engineering

作者： M. Balasubramanian S.S. Iyengar J. Reynaud R.W. Beuerman Computer Science LSU USA Eye Centre LSUHSC USA Ophthalmology LSUHSC USA

Iterative deconvolution algorithms exhibit increased ringing artifacts at higher number of iterations. Quantitative image analysis techniques such as optical flow algorithms are sensitive to these artifacts. We propose to use an image quality metric to identify the terminal step of iterative restoration algorithms. Frequency based metrics have difficulties in distinguishing the ringing artifacts from the image features. Spatial analysis techniques require extensive processing making them unsuitable for real-time image quality measurement as needed in iterative image restoration. We propose a novel ringing metric using binary morphological operators and demonstrate the method on the images of random cotton fibers acquired using white light confocal microscope.

关键词： image restoration Deconvolution Iterative algorithms image analysis image quality Optical sensors image motion analysis Frequency Cotton Microscopy

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Full 4π emission data collection and reconstruction for small animal PET imaging

Full 4π emission data collection and reconstruction for sma...

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IEEE Nuclear Science Symposium/Medical Imaging conference (NSS/MIC)/18th International Workshop on Room-Temperature Semiconductor X-Ray and Gamma-Ray Detectors

作者： Zhang, Yuxuan Baghaei, Hossain Li, Hongdi Liu, Shitao Ramirez, Rocio An, Shaohui Wang, Chao Wong, Wai-Hoi Univ Texas MD Anderson Canc Ctr Houston TX 77030 USA

ISBN: (纸本)9781467301206

Most of the current animal PET detector systems are cylindrical or similar multi-sided polygonal geometries with limited axial field of view. the object is placed near the center of the detector ring during the emission data collection. the signal that can be detected has a limited angular range;the paraxial signal cannot be detected. Moreover, the sensitivity for the objects positioned at different locations inside the FOV is different. the central part of the FOV has higher sensitivity than that near the end along the axis. the lack of paraxial detectability which means non-uniform sampling, along with the non-uniform sensitivity of a PET system, will affect the uniformity of the overall image quality. Also, currently widely used PET data processing and reconstruction algorithms are sinogram based, which usually uses different sizes for the voxel in axial and transaxial directions. Because of these non-uniformities, the resolution and quality of current PET image are anisotropic. In order to achieve isotropic results, the emissions from the object must be collected in full 4 pi space and reconstructed accordingly. Here we propose a full 4 pi emission data collection method, which involving the rotation of the object during the emission collection in a plane parallel to the detector ring axis. the full 4 pi emission data are then reconstructed and processed to generate the 3D image set with cubic voxel, uniform resolution and signal-to-noise ratio in all directions and locations. Both Monte Carlo simulations and experiments are carried out with simulated and real mouse phantoms. Emission data in both 4 pi and conventional modes are collected and then processed. Point source is used in the experiment as the fiducial mark. Our results show that with full 4 pi collection method, the image qualities are substantially improved in several aspects such as the axial distortions and the uniformity of the SNR. Moreover, the axial strip-like artifacts in the conventional mode

关键词： image quality

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Proceedings of the 18th International conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2023

Proceedings of the 18th International Conference on Soft Com...

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Proceedings of the 18th International conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2023

ISBN: (纸本)9783031425356

the proceedings contain 61 papers. the special focus in this conference is on Soft Computing Models in Industrial and Environmental Applications. the topics include: Missing Values Imputation for Visualizing the Air Quality Evolution During the COVID-19 Pandemic in Madrid;Comparative Study of Wastewater Treatment Plant Feature Selection for COD Prediction;effectiveness of Quantum Computing in image processing for Burr Detection;hyperspectral Technology for Oil Spills Detection by Using Artificial Neural Network Classifier;Model-Based Design of the IMO-NMPC Strategy: Real-Time Implementation;first Approach of an Intelligent Automatic System for Aircraft Flap/Slat Positioning;multi-scale Neural Model for Tool-Narayanaswamy-Moynihan Model Parameter Extraction;comparative Study of Open Source Database Management systems to Enable Predictive Maintenance of Autonomous Guided Vehicles;deep Learning and Metaheuristic for Multivariate Time-Series Forecasting;forecasting Greenhouse Temperature Using Machine Learning Models: Optimizing Crop Production in Andalucia;extended Rank-Based Ant Colony Optimization Algorithm for Traveling Salesman Problem;Managing Pandemics through Agent-Based Simulation: A Case Study Based on COVID-19;leveraging Smart Meter Data for Adaptive Consumer Profiling;Categorization of CoAP DoS Attack Based on One-Class Boundary Methods;text Classification for Automatic Distribution of Review Notes in Movie Production;fault Detection in Biological Methanation Process Using Machine Learning: A Comparative Study of Different algorithms;machine Learning Approaches for Predicting Tree Growth Trends Based on Basal Area Increment;integrated Forecast and Optimization for Retailer Allocation in a Two-Echelon Inventory System;application of Fuzzy Logic to the Risk Assessment of Production Machines Failures;Neuron Characterization in Complex Cultures Using a Combined YOLO and U-Net Segmentation Approach;neuroevolutionary Transfer Learning for Time Series Forecasting;f

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Computer Aided systems theory – EUROCAST 2022 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Roberto Moreno-Díaz Franz Pichler Alexis Quesada-Arencibia

ISBN: (数字)9783031253126

ISBN: (纸本)9783031253119

this book constitutes the refereed proceedings of the 18th International conference on Computer-Aided systems theory, EUROCAST 2022, held in Las Palmas de Gran Canaria, Spain, during February 20–25, 2022. the 77 full papers included in this book were carefully reviewed and selected from 110 submissions.;they were organized in topical sections as follows: systems theory and Applications, theory and Applications of Metaheuristic algorithms, Model-Based System Design, Verification and Simulation, Applications of Signal processing Technology, Artificial Intelligence and Data Mining for Intelligent Transportation systems and Smart Mobility, Computer Vision, Machine Learning for image Analysis and Applications, Computer and systems Based Methods and Electronic Technologies in Medicine, systems in Industrial Robotics, Automation and IoT, systems thinking. Relevance for Technology, Science and Management Professionals.

关键词： Simulation and Modeling Artificial Intelligence Software Engineering/Programming and Operating systems Computer Communication Networks

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Proceedings of the 18th International conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2023

Proceedings of the 18th International Conference on Soft Com...

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Proceedings of the 18th International conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2023

ISBN: (纸本)9783031425288

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Combined block sparse PNLMS/NLMS algorithms

Combined block sparse PNLMS/NLMS algorithms

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International conference on systems, Signals and image processing, IWSSIP

作者： Kirill Sakhnov Boris Simak Department of telecommunications engineering Czech Technical University Prague Czech Republic

In some applications of adaptive filtering, such as echo cancellation and active noise reduction, an adaptive filter may be required to have a large number of coefficients in order to model unknown systems with a sufficient accuracy. the computational complexity of adaptive filtering algorithms is proportional to the number of filter coefficients being adapted. the main purpose of partial coefficient updating is to reduce high computational complexity associated with long echo paths. So as to reach computational savings, only a subset of the filter coefficients rather than the entire filter is updated. the filter weights to be updated are usually chosen in accordance with the amplitudes of input signal. However, this approach does not achieve similar performance comparing to the fully updated counterpart. there are opinions that for sparse impulse responses the convergence speed can be improved, if the coefficients amplitudes are used in selection criterion. this paper presents a study on proportionate, partially and sparse partial updated algorithms, and provides their generalization to the Proportionate Normalized Least Mean Squares algorithm. Computer simulations show a reasonable performance of the algorithm with applications, which require an identification of sparse regions within long echo paths.

关键词： Adaptive filters Convergence Filtering algorithms Algorithm design and analysis Measurement Echo cancellers Signal processing algorithms

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image Noise Recognition Algorithm Based on Improved DenseNet 1

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18th Web Information systems and Applications conference (WISA)

作者： Huang, Mengxing Zeng, Lirong Zhang, Yu Li, Yuchun Ni, Zehao Wu, Di Feng, Siling Hainan Univ State Key Lab Marine Resource Utilizat South Chin Coll Informat Sci & Technol Haikou Hainan Peoples R China Hainan Univ Sch Comp Sci & Technol Haikou 570288 Hainan Peoples R China

ISBN: (数字)9783030875718

ISBN: (纸本)9783030875718;9783030875701

In the process of image generation or transmission, the image quality is often degraded due to the interference and influence of speckle noise, which will adversely affect the subsequent image processing. However, most of the existing Optical Coherence Tomography (OCT) image denoising methods usually only use part of the prior information of the OCT image, and ignore the changes in the texture and structure of the OCT image. there is often a problem that the network is too deep and the calculation complexity is too large. Aiming at this shortcoming, this paper proposes an improved deep convolutional neural network image denoising algorithm based on the original DenseNet algorithm idea. First, by constructing a speckle noise image data set, a series of preprocessing is performed on the input image data set, then the visual statistical feature map of the image is extracted, and finally the DenseNet network structure is improved for network training. the experimental results show that the improved DenseNet has better performance no matter compared with BM3D, which is recognized as the best denoising algorithm in the field of image denoising, or compared with DnCNN, an advanced image denoising algorithm in the field of deep learning.

关键词： OCT image Speckle noise image noise recognition DenseNet network

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Scheduling of image processing Using Anytime Algorithm for Real-time System

Scheduling of Image Processing Using Anytime Algorithm for R...

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International conference on Pattern Recognition

作者： W.W. Kywe D. Fujiwara K. Murakami Graduate School of Information Science and Technology Aichi Prefectural University Nagakute Aichi Japan

this paper proposes an adaptive scheduling method using anytime algorithm under such a condition that the processing time is restricted or is not enough. By using typical image processing procedure as an example, this paper discusses how to modify conventional image processing algorithms to anytime algorithmic image processing, modeling of time-precision function and an adaptive scheduling method to realize the maximum performance in the restricted time

关键词： image processing Scheduling algorithm Real time systems Adaptive scheduling Information science Error analysis Process planning Artificial intelligence image analysis Algorithm design and analysis

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EczemaNet: A Deep CNN-based Eczema Diseases Classification

EczemaNet: A Deep CNN-based Eczema Diseases Classification

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International image processing, Applications and systems conference (ipas)

作者： Masum Shah Junayed Abu Noman Md Sakib Nipa Anjum Md Baharul Islam Afsana Ahsan Jeny Daffodil International University Dhaka Bangladesh Khulna University of Engineering and Technology Khulna Bangladesh American University of Malta Bormla BML Malta

ISBN: (数字)9781728175744

ISBN: (纸本)9781728175751

Eczema is the most common among all types of skin diseases. A solution for this disease is very crucial for patients to have better treatment. Eczema is usually detected manually by doctors or dermatologists. It is tough to distinguish between different types of Eczema because of the similarities in symptoms. In recent years, several attempts have been taken to automate the detection of skin diseases with much accuracy. Many methods such as image processing Techniques, Machine Learning algorithms are getting used to execute segmentation and classification of skin diseases. It is found that among all those skin disease detection systems, particularly detection work on eczema disease is rare. there is also insufficiency in eczema disease dataset. In this paper, we propose a novel deep CNN-based approach for classifying five different classes of Eczema with our collected dataset. Data augmentation is used to transform images for better performance. Regularization techniques such as batch normalization and dropout helped to reduce overfitting. Our proposed model achieved an accuracy of 96.2%, which exceeded the performance of the state of the arts.

关键词： image segmentation Machine learning algorithms conferences Transforms Medical services Skin Diseases

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CRIST900: A fully-labeled natural image dataset for multi-operator content aware image retargeting 18th

CRIST900: A fully-labeled natural image dataset for multi-op...

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Joint conferences on 18th International conference on Intelligent systems Design and Applications, ISDA 2018 and 10th World Congress on Nature and Biologically Inspired Computing , NaBIC 2018

作者： Abhayadev, M. Santha, T. Department of Computer Science Dr G R Damodaran College of Science CoimbatoreTamilnadu641014 India

ISBN: (纸本)9783030166564

A fully-labeled image dataset provides an exclusive resource for reproducible analysis, investigation inquiries and data analyses in different research computational fields like machine learning, computer vision and deep learning machine intelligence. this research paper present a large scale fully-labeled natural image dataset for Multi-Operator content aware image retargeting techniques. the image dataset is feely available for image processing research field. the current research natural image dataset entitled CRIST900, it include 900 natural images and uses for content aware image retargeting. the proposed CRIST is an image retargeting Multi-Operator method called Content Retargeting image reSizing Technique. the proposed image resizing method has three phases, image object boundary identification, the image objects feature importance visual saliency map generation and Multi-Operator techniques for efficient retargeting of image objects. the Multi-Operator retargeted image quality assessment was done by different subjective and objective image quality assessment matrices. the experimental result shows that the proposed approach can attain better result than the traditional methods and content aware state-of-the-art image retargeting techniques. the research dataset is publicly available at https://***/view/abhayadevmalayil/home. © Springer Nature Switzerland AG 2020.

关键词： image quality

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