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

共 16440 条记录，以下是2021-2030 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 ...

引用

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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On Momentum Acceleration for Randomized Coordinate Descent in Matrix Completion

On Momentum Acceleration for Randomized Coordinate Descent i...

引用

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

作者： Matthew Callahan Trung Vu Raviv Raich School of EECS Oregon State University Corvallis OR USA Department of CSEE University of Maryland Baltimore County MD USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Matrix completion plays an important role in machine learning and signal processing, with applications ranging from recommender systems to image inpainting. Many approaches have been considered to solve the problem and some offer computationally efficient solutions. In particular, a highly-efficient random coordinate descent approach reduces the per-epoch computation dramatically. This paper is concerned with further improvement of computational efficiency to expand the range of problem sizes and conditions that can be solved. Momentum acceleration is a well-known method to improve the efficiency of iterative algorithms, but applying it to random coordinate descent methods without increasing the computational complexity is non-trivial. To address this challenge, we introduce a momentum-accelerated randomized coordinate descent for matrix completion approach that does not increase computational complexity by accelerating at the level of epochs. Additionally, we propose an analysis-driven, tuning-free method for step size selection. To that end, we offer a convergence rate analysis for the algorithm. Using numerical evaluations, we demonstrate the competitiveness of the method and verify the theoretical analysis.

关键词： Closed-form solutions Shape Signal processing algorithms Machine learning Signal processing Computational efficiency Computational complexity Speech processing Recommender systems Convergence

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A Survey of Artificial Intelligence Integrating image processing Techniques for Predicting Solar Energy Production: Challenges and Innovations

A Survey of Artificial Intelligence Integrating Image Proces...

引用

2024 International conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Gupta, Tushar Tiwari, Gagan Joshi, Kapil Noida International University Department of Computer Science Uttar Pradesh G.B.Nagar India Uttaranchal Institute of Technology Uttaranchal University Department of Computer Science Engineering Dehradun India

ISBN: (纸本)9798350379716

Solar energy production prediction is an essential prerequisite for the successful implementation of renewable energy sources and the elaboration of sustainable strategies. The paper provides a detailed overview of artificial intelligence methods used to predict the performance of solar energy facilities. It examines traditional prediction methods, such as meteorological reports, and their disadvantages, such as numerous factors' influence on the report, changes on the current data availability, and atmospheric conditions. This probes the exploitation of satellite pictures, earth-based cameras, and drone monitoring, depicting how these graphic evidentiary sources are handled using sophisticated machine gaining formulas for example Convolutional Neural systems and Creative Opposing Networks. The potential of AI-based prediction models that apply machine learning and neural networks to analyze numerous data and identify complicated dependencies is underlined. As conclusive remarks, we have reviewed various techniques and frameworks so far, organized AI algorithms based on their respective advantages and limitations, and compared their performance by presenting examples. Furthermore, we have indicated future work directions, particularly the incorporation of advanced data sources, enhanced model interpretability, scalability optimization, adaptive learning algorithm development, multidisciplinary cooperation, strengthened validation processes, and widened spread in practice. By combining these findings in a comprehensive manner, we aspire to propel knowledge on AI-driven solar energy prediction and inspire increased reliance on it for a cleaner world. © 2024 IEEE.

关键词： Artificial intelligence Deep learning Machine learning Neural networks Prediction accuracy Renewable energy Solar energy prediction Sustainability

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

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

引用

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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Design of Real-time Target Detection System in CCD Vertical Target Coordinate Measurement 3

Design of Real-time Target Detection System in CCD Vertical ...

引用

3rd International conference on Information Science, Parallel and Distributed systems, ISPDS 2022

作者： Zhang, xin Ding, Lu Xu, Zhaohui Liu, Hui Xi'an Institute of Optics and Precision Machinery of Cas Xi'an China

ISBN: (数字)9781665487474

ISBN: (纸本)9781665487474

High speed dim and small target detection is an important technology in CCD vertical target coordinate measurement. Its difficulty lies in the high frame rate real-time image processing speed requirements, weak and small target capture rate and extraction accuracy is not high [1]. In order to solve these problems, FPGA is designed and applied as the core of embedded hardware platform, and high-efficiency parallel operation, background iteration and false target detection algorithm are used to realize the real-time detection of high-speed weak and small targets in CDD images with a frame rate of 4096 lines up to 50KHz. The time delay of target acquisition and output measurement results is less than 10 ms, and the real-time performance is very good. In a certain application, under the background illumination of sky, the capture rate of dim high-speed projectile (5.8 mm projectile) can reach 100%, and the measurement accuracy σ is less than 13 mm, and the acquisition rate test of targets larger than 5.8 mm reaches a higher standard. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Dynamic Floor Recognition Method Based on Corner Detection

Dynamic Floor Recognition Method Based on Corner Detection

引用

Natural Language processing (ICNLP), International conference on

作者： Yongyi Mao Chengkai Tian Zhiwei Wu Zhixian Cheng Shool of Electronic Engineering Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

Floor positioning is an important aspect of indoor positioning technology, which is closely related to location-based services (LBS). A dynamic floor recognition method based on corner detection is proposed to solve the problem that the traditional fingerprint location online stage has high time cost and cannot accurately reflect the transition state of pedestrians in the staircase. LDA multi-classification algorithm is used for initial floor identification. Accelerometers and barometers are used to complete step frequency detection and stair recognition. The real-time floor changes of pedestrians will be reflected by the proposed method in multi-storey buildings. The experiment shows that the accuracy of static floor recognition is 96.7%, the accuracy of corner recognition is 100%, and the accuracy of dynamic floor recognition is 100%. In this method, the positioning result can be obtained when the pedestrian moves up and down stairs at the end of the movement. Compared with the existing fingerprint floor location algorithm, it only needs one fingerprint location, which reduces the time cost of online location.

关键词： Training Pedestrians Accuracy Costs Heuristic algorithms Fingerprint recognition Stairs Real-time systems Floors Corner detection

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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...

引用

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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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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MSE-based Sampling of Bandlimited Product Graph Signals via Joint Low-pass Impulse Responses

MSE-based Sampling of Bandlimited Product Graph Signals via ...

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

作者： Fen Wang Baoyi Xu Xuyao Kang Peng Ren Long Yang Hangzhou Institute of Technology Xidian University Hangzhou China State Key Laboratory of Integrated Services Networks Xidian University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Matrix graph signals, which are associated with two factor graphs, are ubiquitous in daily life, such as time-varying physical signals in sensor networks and rating matrices in recommendation systems. In practice, due to the row-wise and column-wise smoothness, they are modeled as bandlimited (BL) graph signals on the product of two factor graphs. In this paper, we propose a fast sampling method for BL product graph signals by minimizing the reconstruction mean squared error (MSE). First, we formulate the MSE-based sampling objective for BL signals on a product graph. Next, we simplify the greedy subproblem using matrix inversion approximation, which involves straightforward computations between the defined joint low-pass impulse responses (JLIRs). To eliminate the huge computation and storage complexity, we propose a strategy to directly compute the greedy evaluation score based on low-dimensional LIRs on two factor graphs, leveraging the property of Kronecker product. Employing this strategy, we also extend existing popular LIR-based methods to sample signals from single graphs to product graphs with low complexity. Extensive experiments on synthetic and real-world product graph signals demonstrate that our proposed method maintains superior MSE performance compared to other extended graph sampling schemes.

关键词： Signal processing algorithms Signal processing Sampling methods Approximation algorithms Acoustics Complexity theory Speech processing Time-varying systems Recommender systems

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