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检索条件"任意字段=Conference on Statistical and Stochastic Methods for Image Processing"

共 4715 条记录，以下是71-80 订阅

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statistical Reconstruction of Heterogenous Microstructures Based on 2D images 17

Statistical Reconstruction of Heterogenous Microstructures B...

引用

conference on Behavior and Mechanics of Multifunctional Materials XVII

作者： Kessenich, Nathaniel K. Wang, Long Calif Polytech State Univ San Luis Obispo Mech Engn Dept 1 Grand Ave San Luis Obispo CA 93407 USA Calif Polytech State Univ San Luis Obispo Civil & Environm Engn Dept 1 Grand Ave San Luis Obispo CA 93407 USA

ISBN: (纸本)9781510660755;9781510660762

Polymer nanocomposites typically possess heterogeneous microstructures that significantly affect structure-property relationships of these material systems. Various microscopic imaging techniques, such as optical microscopy, scanning electron microscopy (SEM), and X-ray microscopy, are essential for characterizing nanocomposite material systems and have provided informative insights of microstructural features. However, microscopic imaging through experiments can be expensive when large amounts of microstructural data are needed. One promising approach to address the imaging limitation and more efficiently generate large microstructural dataset is to statistically reconstruct similar images from a single original input image. A common method used to generate statistically equivalent images is the simulated annealing optimization algorithm. However, due to the high computational cost associated with the stochastic search path used in the simulated annealing algorithm, it can be challenging to reconstruct images with a high degree of agreement. Thus, in this study, a novel and more efficient image reconstruction method was developed by optimizing the simulated annealing algorithm through the manipulation of search path domain and available statistical information. The optimization technique was implemented to reconstruct several example two-dimensional (2D) images to evaluate its capabilities.

关键词： heterogeneous materials image processing microstructure nanocomposites statistical reconstruction

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Learning the image Prior by Unrolling an Optimization Method 30

Learning the Image Prior by Unrolling an Optimization Method

引用

30th European Signal processing conference (EUSIPCO)

作者： Bonettini, Silvia Franchini, Giorgia Pezzi, Danilo Prato, Marco Univ Modena & Reggio Emilia Dept Informat Phys & Math Modena Italy

ISBN: (纸本)9789082797091

Nowadays neural networks are omnipresent thanks to the amazing adaptability they possess, despite their poor interpretability and the difficulties they give when manipulating the parameters. On the other side, we have the classical variational approach, where the restoration is obtained as the solution of a given optimization problem. The bilevel approach is connected to both approaches and consists first in devising a parametric formulation of the variational problem, then in optimizing these parameters with respect to a given dataset of training data. In this work we analyze the classical bilevel approach in combination with unrolling techniques, where the parameters of the variational problem are trained with respect to the results obtained after a fixed number of iterations of an optimization method applied to it. This results in a large scale optimization problem which can be solved by means of stochastic methods;as we observed in our numerical experiments, the stochastic approach can produce medium accuracy results in very few epochs. Moreover, our experiments also show that the unrolling approach leads to results which are comparable with those of the original bilevel method in terms of accuracy.

关键词： bilevel optimization unfolding image denoising sparse model stochastic gradient machine learning Green AI

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stochastic Unrolled Proximal Point Algorithm for linear image inverse problems 31

Stochastic Unrolled Proximal Point Algorithm for linear imag...

引用

31st European Signal processing conference, EUSIPCO 2023

作者： Le Bon, Brandon Le Pendu, Mikaël Guillemot, Christine INRIA Rennes - Bretagne Atlantique Rennes France INTERDIGITAL Cesson-Sévigné France

ISBN: (纸本)9789464593600

Unrolled optimization methods have emerged as a way to combine classical iterative optimization techniques with learned priors to efficiently solve image restoration problems. However, learning the regularization prior along the unrolled iterations requires intensive memory usage due to the deep explicit backpropagation, hence making the number of unrolled iterations usually small in practice. Inspired by deep equilibrium models, unrolling models with implicit backpropagation have been considered for solving this issue. Nevertheless, while these methods yield good restoration quality with reduced memory usage, the theory of implicit backpropagation relies on the knowledge of the fixed point of the function to optimize, usually unknown and estimated by iterating until convergence. Therefore, these methods require intensive computation time to ensure a stable backpropagation. In this paper, we present an unrolled Proximal Point Algorithm method, where the end-to-end optimization problem is re-defined as a per unrolled iteration optimization problem. We prove that the proposed optimization strategy is memory-efficient and applicable for any number of computed unrolled iteration. We empirically show that our method achieves state-of-the-art image restoration quality. © 2023 European Signal processing conference, EUSIPCO. All rights reserved.

关键词： Inverse problems

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VARIANCE-REDUCED RANDOMIZED KACZMARZ ALGORITHM IN XFEL SINGLE-PARTICLE IMAGING PHASE RETRIEVAL 29

VARIANCE-REDUCED RANDOMIZED KACZMARZ ALGORITHM IN XFEL SINGL...

引用

IEEE International conference on image processing (ICIP)

作者： Xian, Yin Liu, Haiguang Tai, Xuecheng Wang, Yang Hong Kong Univ Sci & Technol Hong Kong Peoples R China Microsoft Res Lab Asia Beijing Peoples R China Hong Kong Baptist Univ Dept Math Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Math Hong Kong Peoples R China

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

In this paper, we propose the Variance Reduced Randomized Kaczmarz (VR-RK) algorithm for XFEL signal particle imaging phase retrieval. The VR-RK algorithm is inspired by the randomized Kaczmarz algorithm and the variance reduction in stochastic gradient methods. The formulations of the VR-RK algorithm under the L-1 and L-2 constraints are also presented. Numerical simulations demonstrate that the VR-RK method has a faster convergence rate compared with the randomized Kaczmarz method. Tests on the synthetic signal particle imaging data and the PR772 XFEL real imaging data show that the VR-RK algorithm can recover information with higher accuracy. It is useful for biological data processing.

关键词： Phase retrieval randomized Kaczmarz stochastic optimization variance reduction XFEL single particle imaging

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A Comprehensive Survey of image Steganography 2

A Comprehensive Survey of Image Steganography

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2nd International conference on Sustainable Computing and Smart Systems (ICSCSS)

作者： Kalaiarasi, G. Sudharani, B. Jonnalagadda, Sharon Christiana Battula, Harsha Vardhan Sanagala, Bhavana Vignans Fdn Sci Technol & Res Dept ACSE Guntur Andhra Pradesh India

ISBN: (纸本)9798350391558;9798350379990

This research study analyzes the multidimensional landscape of steganography, examining its historical roots, theoretical background, contemporary approaches, and various applications. Beginning with a historical overview, this study investigates the evolution of steganography from its ancient roots to its present iterations in the digital world. Next, the study progresses towards analyzing the fundamental principles and theoretical frameworks that underpin steganographic systems, such as cryptography and digital signal processing. Finally, this study presents a thorough evaluation of contemporary steganographic technologies, which range from simple LSB (Least Significant Bit) substitution techniques to advanced adaptive algorithms and machine learning methods by including deep-learning based steganography and coverless steganography. Notably, this study identifies key challenges, including detection resistance, payload capacity, and robustness against attacks. Overall, this study presents a thorough understanding of steganography, emphasizing its significance as a versatile tool for communication in the digital era, while also highlighting the challenges that pave way for future innovations.

关键词： image steganography Information hiding Convolutional Neural Network Steganography (CNN) Generative Adversarial Network Steganography (GAN) stochastic computing

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The application of deep learning algorithms in power system load forecasting 6

The application of deep learning algorithms in power system ...

引用

6th International conference on Wireless Communications and Smart Grid, ICWCSG 2024

作者： Wang, Shilin Cui, Wei Yang, Yang Meng, Xiangliang State Grid Information & Telecommunication Group Co. Ltd. No. 10 Xuanwumen Outer Street Xicheng District 95555-0345 Beijing100029 China

ISBN: (纸本)9781510682962

This article explores the application of deep learning (DL) algorithms in power system load forecasting. With the continuous advancement of the construction of new power systems, traditional load forecasting models designed based on time series analysis are no longer in line with the actual needs of the current power system. To improve the accuracy of load forecasting, this paper attempts to transform time series data into images for analysis, and then use deep learning methods widely used in the field of image processing for power load forecasting. Convolutional neural network (CNN), as a commonly used image processing algorithm, has been applied in time series data processing. However, data is still processed as a sequence matrix, and the advantages of CNN algorithm in processing image matrices have not been fully utilized. Therefore, this article proposes a CNN algorithm based on sequence to image conversion (STI-CNN). Firstly, the load sequence is converted into a load image. Then, a dual branch deep network model is used to accurately cluster the input data. Finally, the STI-CNN model is used for load prediction. Matlab simulation experiments show that the proposed STI-CNN model has excellent performance in different prediction metrics and has higher prediction accuracy. © 2024 SPIE.

关键词： Data modeling Data conversion image processing Systems modeling Convolutional neural networks Evolutionary algorithms statistical modeling Education and training Support vector machines

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Textonmap Optimization for Spine Segmentation using Adaptive Differential Evolution 22

Textonmap Optimization for Spine Segmentation using Adaptive...

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Genetic and Evolutionary Computation conference (GECCO)

作者： Yamakawa, Sohei Ono, Keiko Makihara, Erina Tawara, Daisuke Yakushijin, Shoma Ikushima, Naoya Doshisha Univ Grad Sch Sci & Engn Kyoto Japan Doshisha Univ Fac Sci & Engn Kyoto Japan Ryukoku Univ Fac Adv Sci & Technol Kyoto Japan

ISBN: (纸本)9781450392686

With the development of machine learning techniques in the image processing field, research related to semantic segmentation has attracted much attention. Especially in medical image segmentation requires highly pixel-by-pixel accurate results. Given that it is difficult to obtain test images with their ground truth in the medical field, we aim to develop a robust method in an environment with few test images. Specifically, we improve Textonboost by using differential evolution and stochastic hill climbing methods. Experimental results showed that the proposed method outperformed conventional methods in terms of accuracy.

关键词： image Segmentation Differential Evolution stochastic Hill Climbing Medical image processing

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Quantifying Uncertainty: statistical Speech Signal processing 2

Quantifying Uncertainty: Statistical Speech Signal Processin...

引用

2nd International conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Prada Peesapaty, Sree Harsha Joseph, Chinchu Vineeth Kumar, Eguri Samson Naik, Apurva Bazil Raj, A.A. Dr.Vishwanath Karad Mit World Peace University Department of Electrical and Electronics Engineering Pune India Defence Institute of Advanced Technology Rf Photonics Lab Department of Electronics Engineering Pune India

ISBN: (纸本)9798350382952

One of the most important aspects of any conversation is to maintain the intelligibility and integrity of the message. Assume that the data being transferred is accompanied by an undesired disturbance. In that instance, the intended message tends to be warped and the information to be delivered needs to be clarified. A variety of electrical devices and free space channels are frequently used to transfer different kinds of data, including text, music, and image files. In communication systems, noise frequently taints the data;therefore, eliminating noise is crucial in guaranteeing message clarity. Numerous methods of signal processing guarantee the same thing. This specific work, however, focuses on the statistical aspect of the transmitting signal and attempts to investigate how noisy segments in a signal might be identified and how their presence is reduced by using kurtosis in conjunction with power spectral density. © 2024 IEEE.

关键词： Speech enhancement

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A Method of Infrared image Mosaic Based on Keyframe Matching

A Method of Infrared Image Mosaic Based on Keyframe Matching

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2023 International conference on Machine Vision, image processing and Imaging Technology, MVIPIT 2023

作者： Shao, Jianming Yu, Ya Xu, Zheng Ye, Chen Jia, Dachang Yan, Minghao Jiangsu Electric Power Company Wuxi Power Supply Company Wuxi China

ISBN: (纸本)9798350306545

Infrared images usually have a narrow field of view, requiring splicing of multiple image sequences to meet the application requirements of wide field of view and high resolution. However, due to the large overlap between adjacent frame images in infrared image stitching, it can lead to accumulated stitching errors and increased computational costs. An infrared image mosaic method based on keyframe matching is proposed. Firstly, key frames are selected as the stitching object based on the similarity of image statistical vectors. Then, the SURF algorithm is used for feature extraction and image registration. Finally, three typical image fusion methods are used to complete panoramic image stitching. The experiments results based on on infrared image datasets show that this method can significantly reduce computational complexity while ensuring the quality of stitching. © 2023 IEEE.

关键词： Feature extraction

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An Empirical Study of Monte Carlo-based methods in Machine Learning 4

An Empirical Study of Monte Carlo-based Methods in Machine L...

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4th International conference on Signal processing and Computer Science, SPCS 2023

作者： Lei, Fei Wang, Yan Wang, Lulu Wu, Qiuping School of Finance and Economics Guangdong University of Science and Technology Dongguan China School of Management Guangdong University of Science and Technology Dongguan China

ISBN: (纸本)9781510672505

Empirical studies in the field of machine learning based on Monte Carlo methods explore a powerful statistical technique aimed at solving the core problems of uncertainty modeling, model evaluation, and decision making;Monte Carlo methods provide new perspectives and tools for the application of machine learning models through stochastic sampling and simulation;however, Monte Carlo methods are also facing a number of challenges, such as computational efficiency, high-dimensional space problems, and sampling bias, etc.;in order to overcome these problems, this paper explores future research directions that may involve improved sampling methods, innovative applications that incorporate deep learning, etc. Therefore, the continuous innovation in this field will provide new opportunities and challenges for the development and application of machine learning models. © 2023 SPIE.

关键词： Monte Carlo methods

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