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

共 16532 条记录，以下是2091-2100 订阅

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A Statistical Approach for Improving image Quality and Cell Quantification from Imaging Mass Cytometry

A Statistical Approach for Improving Image Quality and Cell ...

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

作者： Xu xiao Yating Lin Lei Zhang Wenxian Yang Rongshan Yu School of Informatics Xiamen University Xiamen China Aginome Scientific Xiamen China

ISBN: (数字)9798350356656

ISBN: (纸本)9798350356663

Imaging Mass Cytometry (IMC), a multiplexed imaging technology, has become a valuable tool in biomedical research due to its capability to measure over 100 markers theoretically. However, the presence of noise in IMC images can affect cell phenotyping and the precision of clinical analysis. To address this challenge, we propose IMCell, a highly effective and generalizable method that enhances image quality and cell quantification by leveraging biological priors. IMCell begins by generating decoy cells using Monte Carlo sampling and incorporates cell shape information for each protein channel. Subsequently, it estimates background noise from the expression of decoy cells by fitting Gaussian mixture models with the EM algorithm. Additionally, it identifies positive cell expressions by comparing distribution patterns between real cells and decoy cells. Based on the analysis results from IMCell, we further introduce an image quality index for comprehensive image quality assessment by combining noise and positive cell expressions. Experiment results demonstrate the efficacy of our proposed approach in enhancing image quality and facilitating downstream analysis. By improving the quality of IMC images, IMCell holds the potential to enhance our understanding of complex biological systems and disease mechanisms.

关键词： image quality Proteins Multiplexing Protein engineering Monte Carlo methods Shape Noise Signal processing algorithms Clustering algorithms Background noise

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An Improved Unet Algorithm for the processing of Magnetic Resonance Imaging (MRI) images

An Improved Unet Algorithm for the Processing of Magnetic Re...

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Computer Information Science and Application Technology (CISAT), International conference on

作者： Lipin Li Yiyu Wu Zhao’En Yan Wei Tian Yixuan Wang Yanni Hui Shanxi Key Laboratory of Measurement and Control Technology for Oiland Gas Wells Xi’an Shiyou University Xi’an China PetroChina Changqing Oilfield Company National Engineering Laboratory for exploration and development of low permeability oil and gas fields Oil and Gas Technology Research Institute Xi’an China

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

A machine learning algorithm has been proposed to address the issues of low image resolution, long sampling times, and high costs in MRI. It processes under-sampled MRI K-space data, enabling the reconstruction of high-quality images while significantly reducing sampling time and costs. This paper utilizes a Unet fully convolutional neural network for feature extraction and fusion of K-space data, incorporating an attention module to highlight important image features and suppress irrelevant information. The total variation minimization algorithm is used for the preliminary reconstruction of under-sampled data. High-quality images are then input into a Unet network with a gated attention module for training, achieving high-quality reconstruction of under-sampled data. Experimental results prove that the proposed image reconstruction algorithm outperforms the conventional convolutional neural network Unet, significantly enhancing image reconstruction performance. It improves the PSNR metric by over 4dB and the SSIM metric by approximately 5% compared to the conventional convolutional neural network Unet.

关键词： Measurement Visualization Machine learning algorithms Costs Magnetic resonance imaging Logic gates Minimization Feature extraction Convolutional neural networks image reconstruction

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Optimization of a MIMO Antenna using the API metaheuristic algorithm

Optimization of a MIMO Antenna using the API metaheuristic a...

引用

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

作者： Bitat Hiba Ines Maamri Fouzia Khelfaoui Fatima Djellab Hanane Messai Yacine Aouissi Feriel SATIT Laboratory University Abbes Laghrour Khenchela Khenchela Algeria University Alarbi TBESSI Tebessa Tebessa Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Antenna technology has undergone significant advancements with the integration of innovative techniques such as MIMO (Multiple Input Multiple Output) and phased array systems. By utilizing multiple antennas for transmission and reception, MIMO enhances data rates, spectral efficiency, and overall system performance. Phased array antennas, on the other hand, introduce beamforming capabilities, allowing for dynamic control and steering of radio wave beams without physical movement. The goal of this work is to optimize the control of the antenna network (MIMO) from the perspective of directivity and secondary lobe reduction using the API (pachycondyla apicalis algorithm) metaheuristic algorithm. Specifically, the API algorithm will be employed to determine the optimal element spacing for an N=16 element linear array operating at the LTE frequency of 2.5 GHz, and phase shift Ψ=2.3 radians with the objective of achieving the best value of the inter element spacing. while minimizing the secondary lobes.

关键词： Phased arrays Array signal processing Heuristic algorithms System performance Metaheuristics Signal processing algorithms Space exploration

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Quick and Efficient Large Scale Approximate Nearest Neighbor Search on High-Dimensional Data

Quick and Efficient Large Scale Approximate Nearest Neighbor...

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International conference on Wavelet Active Media Technology and Information processing (ICWAMTIP)

作者： Hu Zhibing University Of Electronic Science and Technology Of China

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

Large scale approximate nearest neighbor search on high-dimensional data is always a critical research question, including recommendation systems and image search. Graph-based ANNS algorithms have received more and more attention due to its accuracy and efficiency. Nevertheless, the graph-based ANNS algorithms faces the following challenges: computation latency and memory requirements. In this work, we present a novel graph-based similarity search algorithm called HNSWQ, which optimizes the HNSW algorithm by building quick and comprehensive uppers without using compression. Those uppers can bring high-quality entry point to accelerate query processing by reducing the number of points to be accessed during the search and shorten the whole computation latency. At the same time, we consider the parallelism of GPU and take advantage of GPU to shorten the build and search time of the novel HNSWQ algorithm. Finally, we verify the presented algorithm on several representative datasets. Compared with the state-of-the-art graph-based algorithm, our novel method can acquire great accuracy and search speed.

关键词： Accuracy High dimensional data Query processing Memory management Graphics processing units Nearest neighbor methods Parallel processing Media Approximation algorithms Recommender systems

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RADCI: A Synchronized Radar-RGBT Object Detecting-Tracking Dataset And A Benchmark

RADCI: A Synchronized Radar-RGBT Object Detecting-Tracking D...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Heng Yu Ruiheng Zhang Haoyang Sun Zhe Cao Biwen Yang Jin Zhang Guanyu Liu Beijing Institute of Technology Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

High-quality perception is crucial in autonomous driving and monitoring systems, where millimeter-wave radar and infrared cameras play important roles due to their robustness and reliability under harsh conditions. Both technologies can serve as low-cost supplements to optical image detection, improving overall system robustness. However, there is currently a lack of widely applicable feature-level fusion methods and multimodal datasets to effectively integrate visible light with these two heterogeneous data types for multiple tasks. In this work, we collect a new multimodal dataset, RADCI8, which synchronizes data from a camera, an infrared camera, and a radar for target detection and tracking. The dataset includes 2D image annotations, radar RAD tensor data with distance, angle, and Doppler information, as well as target ID annotations in both data formats. In addition, to address the incomplete use of radar data in previous fusion algorithms, we propose a detection method that fuses image and radar features using feature concatenation and an attention mechanism. Our proposed algorithm achieves 51.5% AP with an IOU of 50:95 on 2D bounding box prediction, significantly improving average detection accuracy over vision-based methods and maintaining robustness even when a single sensor degrades.

关键词： Tensors Radar detection Signal processing algorithms Radar imaging Radar tracking Cameras Prediction algorithms Robustness Doppler radar Synchronization

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Adaptive Optimization Algorithm for High Proportion Distributed Photovoltaic Access to Distribution Network Based on RNN

Adaptive Optimization Algorithm for High Proportion Distribu...

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Intelligent Control, Measurement and Signal processing and Intelligent Oil Field (ICMSP), International conference on

作者： Yifeng xie Jian Zou Ziyi Dong Zhengcheng Wu State Grid Haiyan County Electric Power Company Jiaxing China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

Conventional adaptive optimization algorithm for high proportion distributed photovoltaic access to distribution network mainly uses ARMA (Autoregressive moving average model) to obtain incremental load, which is vulnerable to the impact of centralized absorption fluctuation, resulting in poor algorithm optimization indicators. Therefore, an RNN based adaptive optimization algorithm for high proportion distributed photovoltaic access to distribution network is proposed. That is, RNN is used to build a high proportion distributed photovoltaic access distribution network model, and an adaptive optimization objective function of photovoltaic distribution network is designed to generate an adaptive optimization algorithm for distributed photovoltaic access distribution network. The experimental results show that the optimization performance indicators of the designed adaptive optimization algorithm for distribution networks are good, and the power supply capacity under different scenarios is the best, effective, and has certain economic value. It has made certain contributions to improving the centralized power supply capacity of distribution networks and reducing power supply and distribution losses.

关键词： Photovoltaic systems Adaptive systems Fluctuations Voltage fluctuations Power supplies Signal processing algorithms Distribution networks Prediction algorithms Optimization Autoregressive processes

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Comprehensive image Classification and Object Detection Using Convolutional Neural Networks

Comprehensive Image Classification and Object Detection Usin...

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Disruptive Technologies (ICDT), International conference on

作者： Archana Kumari Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

In the modern era, Deep Learning (DL) methodology is used extensively in multiple areas and Convolutional Neural Network (CNN) is majorly used for addressing the problems in real-time for computer vision activities due to its capability of enabling acquisitions with higher accuracy. The concept at the back of this DL technique was inspired from architecture of biotic visual systems. In the current time, it is applied extensively in multiple areas and industries like automotive, medical and healthcare, space, aviation, agriculture etc. Moving forward, numerous algorithms on CNN were proposed to address the requirement of multiple areas, with Natural Language processing (NLP) and computer vision as the major fields for application. The activities in computer vision with major application of CNN includes image categorization, object identification, recognition of face, estimation of human pose, recognition of action, scene labelling along with document analysis. This paper delves into modern techniques of CNN for classification of images and detection of objects.

关键词： Deep learning Computer vision image recognition Face recognition Medical services Real-time systems Natural language processing Agriculture Convolutional neural networks Biomedical imaging

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Implementation of Digital image Zooming On DSK6713 DSP Platform For Real-Time Applications

Implementation of Digital Image Zooming On DSK6713 DSP Platf...

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Electronic Engineering and Signal processing (EESP), International conference on

作者： Abdellah Skoudarli Lina Kebri Yasser Bouzaz LTIR Laboratory FGE - USTHB Algeria Department of Electronics FGE-USTHB Algeria

ISBN: (数字)9798331520632

ISBN: (纸本)9798331520649

Digital image zooming is very important process in image processing. Basically, zooming process consist of two steps: the creation of new pixels locations and the assignment of intensity of gray level to those new location. In general, zooming process provides an enlarged image to get better view of the original digital image with preserving as much as possible the information content of the original image. In fact, digital image zooming operation requires a high storage size and computational complexity. In this paper, we consider an interpolation method for zooming digital images 16 times because of their inherent abilities to preserve sharp edges and detail. In order to achieve real time application, the interpolation algorithm based on 15 filters has been implemented on DSK6713 Platform. According to the experimental results our implementation on DSK6713 platform is able to perform digital image zooming for 32×32 size in real time by using DSP internal memory. For larger sizes 64×64 and 128×128, we recommend to utilize DSK6414 with high DSP frequency and larger internal memory.

关键词： Interpolation Time-frequency analysis Filters Digital images System performance image processing image edge detection Signal processing algorithms Digital signal processing Real-time systems

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A Lightweight Generative Adversarial Network for Artistic Style Transfer

A Lightweight Generative Adversarial Network for Artistic St...

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International conference on Fuzzy systems and Knowledge Discovery (FSKD)

作者： Ping'an Qiao Tongtong He College of Computer Science Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

Currently, image style transfer is one of the hotspots in image processing. Generative adversarial networks have been successfully applied in many fields by virtue of their advantages, and the technology of image style transfer has also seen improvements. However, there are still some problems with the existing artistic style transfer models. For example, the generated images will contain noisy textures, the style and color effects are not ideal, and the model has many parameters, which is easy to cause model breakdown and unstable training. To overcome these problems, an image transfer method that improves the cycleconsistent generative adversarial network (CycleGAN) is presented in this paper. According to its structural characteristics, distributed shift convolution (DSConv) is introduced through the quantized neural network, and the reverse residual block is used to optimize the generator network structure, which can greatly reduce the calculation amount of the model while also improving the model's perception ability and accuracy and the quality of the generated images. Experiments show that the proposed approach can improve the content details of the generated image andthe color effect of the style texture, significantly reduces the number of model parameters, reduces the computational cost, improves the transfer rate, upgrading the model performance, and has strong style transfer capabilities.

关键词： Training image color analysis Convolution Computational modeling Neural networks Generative adversarial networks Knowledge discovery Generators Noise measurement Computational complexity

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Brain Tumor Segmentation of MRI images Based on K-Means and White Shark Optimizer 24

Brain Tumor Segmentation of MRI Images Based on K-Means and ...

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24th International Arab conference on Information Technology, ACIT 2023

作者： Braik, Malik Al-Hiary, Heba Al-Betar, Mohammed Azmi Al-Balqa Applied University Department of Computer Science Al-salt Jordan Al-Balqa Applied University Department of Computer Information Systems Al-salt Jordan Department of Information Technology Ajman United Arab Emirates Al-Balqa Applied University Irbid Jordan

ISBN: (纸本)9798350384307

Due to the intricacy of the process involved in segmenting and extracting the tumor regions in magnetic resonance imaging (MRI), it is quite difficult to successfully detect the diseases. Due to its critical significance in subsequent image processing phases, this creates a requirement for a suitable clustering algorithm. In this study, a clustering method is presented that may address a variety problems for MRI brain tumor segmentation. One of the most well-known conventional clustering methods that is simple to use, quick to process, and ensures convergence is k-means (KM). Its weakness, which is defined by its susceptibility to the initial center's random initialization, is present. Because of this, the KM was optimized in this study using the white shark optimizer (WSO) to address this weak side of the KM. The KM would set the starting point for WSO's final position. Evaluation results of WSO-based KM were judged on a publicly available datasets in terms of several relevant evaluation methods and contrasted with outcomes of KM, fuzzy c-means (FCM), and other meta-heuristics when practiced to cluster the same datasets. The WSO-based KM clustering approach outperformed the standard KM and other competing algorithms-based KM. © 2023 IEEE.

关键词： FCM image segmentation K-means White shark optimizer

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