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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301-310 订阅

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NATURAL SCENE STATISTICS AND CNN BASED PARALLEL NETWORK FOR image QUALITY ASSESSMENT

NATURAL SCENE STATISTICS AND CNN BASED PARALLEL NETWORK FOR ...

引用

IEEE International conference on image processing (ICIP)

作者： Jain, Parima Shikkenawis, Gitam Mitra, Suman K. Indian Inst Technol Jammu Jammu India CR Rao Adv Inst Math Stat & Comp Sci Hyderabad India Dhirubhai Ambani Inst Informat & Commun Technol Gandhinagar India

ISBN: (纸本)9781665441155

image Quality Assessment (IQA) tasks have increasing importance in today's world due to the widespread use of imaging devices and social media. statistical studies proved that naturalness measures are good discriminators for evaluating image quality. Convolutional neural networks (CNN) based IQA models gained popularity in recent years due to their enhanced performance. In this article, we present a no-reference image quality assessment method that integrates natural image statistics (NSS) with CNN. The proposed approach extracts NSS features from the image, integrates them with the CNN features to predict the quality score. Our experimental results show that the performance of the proposed method is competitive against the existing methods of image quality assessment. Cross database testing on Live in the Wild (LIVE-itW) and Smartphone Photography Attribute and Quality (SPAQ) databases shows excellent generalization.

关键词： No-reference image Quality Assessment Natural Scene Statistics Convolutional Neural Networks

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Feature Extraction for Pneumonia Diagnosis Using Conventional methods

Feature Extraction for Pneumonia Diagnosis Using Conventiona...

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Electrical, Computer, Telecommunication and Energy Technologies (ECTE-Tech), International conference on

作者： Imane Beghoura Mustapha Benssalah Fazia Sbargoud Signal Processing Laboratory Ecole Militaire Polytechnique Algiers Algeria Comlplexe Systems Control Laboratory Ecole Militaire Polytechnique Algiers Algeria

ISBN: (数字)9798350388480

ISBN: (纸本)9798350388497

Medical image processing plays a crucial role in pneumonia diagnosis and classification using chest X-ray images. This research investigates key phases of medical image processing, focusing on Preprocessing, Feature Extraction, and Classification. Texture analysis methods, notably Local Binary Patterns (LBP) and Gray-Level Co-occurrence Matrix (GLCM), have been widely employed in medical imaging due to the inher-ent importance of textures in medical images. The significance of robust feature extraction methods lies in their ability to identify complex patterns indicative of various medical issues. By conducting experiments on a public chest X-ray dataset, this research employs established machine-learning classifiers to evaluate the efficacy of LBP and GLCM. The findings highlight the superior performance of GLCM in capturing nuanced textu-ral features, showcasing its potential as a principal contributor to enhancing pneumonia classification accuracy. However, the combination of both techniques produces even superior outcomes. LBP, known for its proficiency in capturing local texture patterns, complements GLCM, which excels in extracting global statistical textural information. The combination of these methods enhances the feature space, incorporating both local variations and long-range dependencies, thereby enriching the disease-discriminating patterns, particularly in pneumonia cases. From a wider perspective, texture analysis methods play a crucial role in enhancing the comprehension of medical images, thereby improving the accuracy and reliability of diagnoses.

关键词： Pneumonia Accuracy Focusing Machine learning Feature extraction Biomedical image processing Reliability Iterative methods Medical diagnostic imaging X-ray imaging

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Comparison of Pansharpening Algorithms: Developing a Data-Driven Evaluation Framework

Comparison of Pansharpening Algorithms: Developing a Data-Dr...

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

作者： Yu Liu Qingwei Tong Zhipeng Zhao School of Big Data and Intelligent Engineering Southwest Forestry University Kunming China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Convolutional neural network (CNN)-based methods are increasingly crucial in image processing and computer vision. In remote sensing image pansharpening, CNNs have shown excellent fusion performance, attracting significant research interest. However, challenges remain, such as the lack of detailed pansharpening dataset simulations, standardized training-testing datasets, and simple, unified coding frameworks. Additionally, there is insufficient evaluation of recent deep learning pansharpening methods and evaluation of algorithms from the perspective of different subsequent applications, limiting comprehensive evaluation. This study constructs a comprehensive evaluation system for datasets, fusion algorithms, and evaluation methods. By statistically analyzing results, improve the accuracy and richness of algorithm evaluations, enabling more effective and objective method comparisons. Used WorldView-2 satellite imagery for visual assessments, supervised and unsupervised quantitative evaluations on 100 images, and land cover classification accuracy experiments. Traditional methods like BDSD and PRACS showed strong fusion results and land cover classification accuracy, deep learning methods generally outperformed traditional ones. This research provides detailed images and data resources for pansharpening, offering valuable references for future studies in this field.

关键词： Deep learning Measurement Visualization Accuracy statistical analysis Land surface Pansharpening Classification algorithms Satellite images Remote sensing

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Analysis of the Application of stochastic Gradient Descent to Detect Network Violations 6

Analysis of the Application of Stochastic Gradient Descent t...

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6th International conference on Computational Linguistics and Intelligent Systems, COLINS 2022 - Volume I: Main

作者： Boyko, Nataliya Khomyshyn, Iryna Ortynska, Natalia Terletska, Viktoria Bilyk, Oksana Hasko, Oleksandra Lviv Polytechnic National University Profesorska Street 1 Lviv79013 Ukraine

Over the past decade, data sizes have grown faster than processor speeds. In this context, the capabilities of statistical machine learning methods are limited by computational time, not sample size. A more accurate analysis reveals qualitatively different trade-offs in the case of small and large learning problems. This work considers the occurrence of a large-scale case involving the basic algorithm's computational complexity and the implementation of optimization in non-trivial ways. Optimization algorithms such as stochastic gradient descent, which demonstrate exceptional performance for large-scale problems, are considered. The work compares the algorithms of gradient descent, stochastic gradient, and standard deterministic gradient descent. Here are given ways to apply them in machine learning and considered a formal description of the operation of gradient descent, stochastic gradient, and standard deterministic gradient descent algorithms. Here is analyzed the difference in the process of algorithms. The advantages and disadvantages of each of them are given. A comparison of both approaches is considered: deterministic and stochastic algorithms. This work has analyzed the effectiveness of these two approaches. Convolutional (convolutional) neural networks are considered, which, unlike conventional deep neural networks, use convolutions to find patterns in images, thereby improving prediction in image recognition problems. A model of a convolutional neural network is presented. Here are analyzed the parameters of training the input sample and its testing. As a result of using a simulation of the usual gradient descent, when the weight is updated only after viewing all the elements, the value of the loss function is considered. The analysis of the selected problem and model also justifies the results of its use. © 2022 Copyright for this paper by its authors.

关键词： Convolution

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image Segmentation and Pre-processing for Lung Cancer Detection in Humans Based on Deep-Learning

Image Segmentation and Pre-Processing for Lung Cancer Detect...

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International conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Drishti Singh Jaspreet Singh School of Engineering and Sciences GD Goenka University Gurugram

When it comes to cancer and its linked disorders, lung cancer is consistently ranked among the top causes of mortality. The primary method for making the diagnosis is to do a scan analysis of the patient's lungs. It's possible that an MRI, CT scan, or X-ray will be analyzed in this scan analysis. Due to the wide variety of imaging techniques that can be used to a patient's lungs, one of the challenging jobs that must be completed is the automated classification of lung cancer. methods involving machine learning, deep learning and image processing have demonstrated a significant amount of promise for the classification and identification of lung cancer. In this research, we demonstrate a successful strategy for detecting and classifying malignant and benign lung cancer-related CT scan pictures. This approach was developed as part of our research for this paper. The proposed method begins by classifying the photos after they have been processed with image processing techniques. After that, the supervised learning algorithms are utilized to further process the images. In this section, we have extracted statistical features as well as textural characteristics, and then we have fed several extracted features to multiple classifiers. We have utilized a total of seven distinct classifiers, which are referred to as the KNN classifier, SVM classifier, multinomial naive Bayes classifier, decision tree, SGD(stochastic gradient descent), MLP (multi-layer perceptron) and random forest. When training and testing these classifiers, we employed a dataset that included both benign and malignant lung cancer-related images. In the findings that were collected, it was discovered that the accuracy is highest which is approx. 88 percent(for MLP classifier), when compared to other classifiers.

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Establishment of Comprehensive Evaluation Indicators in Globalized National image Using Quantile-Type statistical methods 1

Establishment of Comprehensive Evaluation Indicators in Glob...

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1st International conference on Digitalization and Management Innovation, DMI 2022

作者： Cheng, Hao National Academy of Innovation Strategy China Association for Science and Technology Beijing100038 China

ISBN: (纸本)9781643683782

National image is a comprehensive reflection of a country's performance on international stage and could be affected by an enormous amount of factors. To evaluate national image, the paper screens key variables using lasso penalized quantile regression first and then proposes a comprehensive evaluation indicator and modified partial least square algorithms based on the quantile-based third-order latent variable model. After data processing procedure, we choose 223 indicators and 61 economies from International Institute for Management Development World Competitiveness Yearbook. All 223 indicators can be summarized into four dimensionsthey are Economic Performance, Government Efficiency, Business Efficiency and Infrastructure. Each dimension can be reflected from 5 sub-dimensions, and each sub-dimension contains different numbers of indicators. Based on the real data, we accomplish national image evaluation and get different conclusions according to the explored quantile of interest. © 2023 The authors and IOS Press.

关键词： Efficiency

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Research on image Information Security Algorithm Based on Wavelet Neural Network 2

Research on Image Information Security Algorithm Based on Wa...

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2nd International conference on Networking, Communications and Information Technology, NetCIT 2022

作者： Li, Yiran Central South University Hunan Changsha410083 China

ISBN: (纸本)9781665492737

With the continuous emergence of digital images and digital videos, a large number of images containing sensitive information need to be kept secret, and a large number of digital media works need intellectual property protection. image security and confidentiality technology has become another hot spot in the field of information security. In view of the shortcomings of the statistical processing algorithm for the verification results adopted by various enterprises in the network information security evaluation, through the research on the characteristics of computer security, this paper compares the selection of weights in the commonly used weighted arithmetic average method. image information encryption originates from the early classical encryption theory. Its purpose is to transform a given image into a disordered image in the spatial or frequency domain according to certain transformation rules, so as to hide the real information of the image information itself. How to protect image information security has become a hot topic in the international research. In order to better adapt to the unique nature of image information and meet the needs of data security and copyright protection, scholars are actively seeking new technologies and methods. This paper studies the basic algorithm of wavelet neural network and applies it to image information security. The main idea of the algorithm is to combine the theory of wavelet transform with the algorithm of neural network, and then get the algorithm of wavelet neural network. © 2022 IEEE.

关键词： Cryptography

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Beyond Invariance: Test-Time Label-Shift Adaptation for Addressing "Spurious" Correlations 37

Beyond Invariance: Test-Time Label-Shift Adaptation for Addr...

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37th conference on Neural Information processing Systems, NeurIPS 2023

作者： Sun, Qingyao Murphy, Kevin Ebrahimi, Sayna D'Amour, Alexander Cornell University United States Google DeepMind Google Cloud AI Research

ISBN: (纸本)9781713899921

Changes in the data distribution at test time can have deleterious effects on the performance of predictive models p(y|x). We consider situations where there are additional meta-data labels (such as group labels), denoted by z, that can account for such changes in the distribution. In particular, we assume that the prior distribution p(y, z), which models the dependence between the class label y and the "nuisance" factors z, may change across domains, either due to a change in the correlation between these terms, or a change in one of their marginals. However, we assume that the generative model for features p(x|y, z) is invariant across domains. We note that this corresponds to an expanded version of the widely used "label shift" assumption, where the labels now also include the nuisance factors z. Based on this observation, we propose a test-time label shift correction that adapts to changes in the joint distribution p(y, z) using EM applied to unlabeled samples from the target domain distribution, pt(x). Importantly, we are able to avoid fitting a generative model p(x|y, z), and merely need to reweight the outputs of a discriminative model ps(y, z|x) trained on the source distribution. We evaluate our method, which we call "Test-Time Label-Shift Adaptation" (TTLSA), on several standard image and text datasets, as well as the CheXpert chest X-ray dataset, and show that it improves performance over methods that target invariance to changes in the distribution, as well as baseline empirical risk minimization methods. Code for reproducing experiments is available at https://***/nalzok/test-time-label-shift. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： statistical tests

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stochastic Unrolled Proximal Point Algorithm for Linear image Inverse Problems

Stochastic Unrolled Proximal Point Algorithm for Linear Imag...

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European Signal processing conference (EUSIPCO)

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

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.

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Frequency vs Azimuth (FVAZ): the younger member of the family of seismic driven fracture characterization methods

Frequency vs Azimuth (FVAZ): the younger member of the famil...

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2023 SPE/AAPG/SEG Latin America Unconventional Resources Technology conference, JLAU 2023

作者： Tavella, Juan Lazzari, Victoria Frydman, Marcelo Herrera, Néstor JT Reservorios United States Phoenix Global Resources Argentina Wellfield Services L.T.D.A. Brazil

Predicting fracture distribution is a key issue during development of unconventional reservoirs. HTI anisotropy due to natural fractures affects the AVO gradient, amplitude, and travel times. According to this, in the last 25 years geophysicists have developed methods to retrieve reservoir fracture model from seismic data by means of inversion techniques (AVAZ, VVAZ and azimuthal inversion). More recently, another anisotropic effect on seismic response due to natural fractures appealed geophysicists: fractures also affect absorption: a propagation perpendicular to fracture suffers higher attenuation due to friction in medium. The phenomenon is called FVAZ, and we accepted the challenge of testing it in the context of Vaca Muerta to compare the results with those obtained from other methods. Previous works settled for qualitative results and did not consider that attenuation is an effective magnitude. To overcome this, a "layer-by-layer" inversion was implemented. processing sequence was revisited to ensure the preservation of frequency changes. The reservoir was mapped into a stratigraphic grid in time and normalized quotients of spectral decay were obtained by FFT. Based on statistical methods, a complete stage of data conditioning was run. An error minimizing problem on the theoretical attenuation was stated on a layer-by-layer basis for three unknowns (reservoir parameters): fracture orientation, fracture intensity and isotropic absorption (Q factor). Finally, the obtained maps for each level and for each parameter were put together into single volumes. A comparison between the results of three methodologies is presented. Fracture patterns from FVAZ were very encouraging, with detailed image of the high organic content level known as the Cocina. Despite the limited organic formation thickness, several thin level maps could be extracted showing multiple patterns with clear trends. Isotropic absorption –an innovative parameter- is also remarkable as it shows a sugg

关键词： Anisotropy

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