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

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statistical based CNN algorithm for SAR image despeckling 13

Statistical based CNN algorithm for SAR image despeckling

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13th European conference on Synthetic Aperture Radar (EUSAR)

作者： Vitale, Sergio Ferraioli, Giampaolo Pascazio, Vito Univ Napoli Parthenope Dipartimento Ingn Naples Italy Univ Napoli Parthenope Dipartimento Sci & Tecnol Naples Italy

ISBN: (纸本)9783800754571

Despeckling is a key tool for the SAR image understanding and it is the first image pre-processing for other application such as classification, segmentation and detection. The importance of having a filter able to suppress noise without losing spatial details is fundamental. Among different approaches, nowadays several deep learning based algorithm for SAR despeckling are proposed. Most of them rely on simulated datasets based on certain hypothesis for the speckle that usually not fully exploit the characteristics of real SAR images, leading to methods that produce artefacts in areas with different characteristics from those present in the training. The aim of this work is to propose a multi-step despeckling process: in the first step a convolutional neural network trained under the fully developed speckle hypothesis with a statistical loss function is used for despeckling;later, by means of a statistical test and a ratio edge detector, the noise predicted by the network is used for detecting the not fully developed areas where the network will produce artefacts. Once this detection is done, an ad hoc filtering policy can be considered.

关键词： Synthetic aperture radar

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stochastic Solutions for Linear Inverse Problems using the Prior Implicit in a Denoiser 35

Stochastic Solutions for Linear Inverse Problems using the P...

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35th Annual conference on Neural Information processing Systems (NeurIPS)

作者： Kadkhodaie, Zahra Simoncelli, Eero P. NYU Ctr Data Sci New York NY 10003 USA NYU Courant Inst Math Sci New York NY 10003 USA Simons Fdn Flatiron Inst New York NY USA

ISBN: (纸本)9781713845393

Deep neural networks have provided state-of-the-art solutions for problems such as image denoising, which implicitly rely on a prior probability model of natural images. Two recent lines of work - Denoising Score Matching and Plug-and-Play propose methodologies for drawing samples from this implicit prior and using it to solve inverse problems, respectively. Here, we develop a parsimonious and robust generalization of these ideas. We rely on a classic statistical result that shows the least-squares solution for removing additive Gaussian noise can be written directly in terms of the gradient of the log of the noisy signal density. We use this to derive a stochastic coarse-to-fine gradient ascent procedure for drawing high-probability samples from the implicit prior embedded within a CNN trained to perform blind denoising. A generalization of this algorithm to constrained sampling provides a method for using the implicit prior to solve any deterministic linear inverse problem, with no additional training, thus extending the power of supervised learning for denoising to a much broader set of problems. The algorithm relies on minimal assumptions and exhibits robust convergence over a wide range of parameter choices. To demonstrate the generality of our method, we use it to obtain state-of-the-art levels of unsupervised performance for deblurring, super-resolution, and compressive sensing.

关键词： Deep neural networks

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Functional Graph image Representation applied to EEG-based Mental Workload Classification

Functional Graph Image Representation applied to EEG-based M...

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Annual International conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Maria Sarkis Mira Rizkallah Saïd Moussaoui Nantes Université É Cole Centrale Nantes Nantes France

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

A lot of research has been lately dedicated to develop machine learning and statistical signal processing methods exploiting graph representations to solve inference and estimation problems. This is particularly relevant in the case of functional connectivity analysis from Electroencephalography (EEG) signals. However, the widely adopted functional connectivity metrics are hand-crafted, and thus suffer from the redundant and sometimes irrelevant information due to the volume conduction problem. Besides, the actual locations of the nodes (i.e. the electrodes) in the functional graph are often overlooked. In this work, we introduce an innovative approach leveraging the image representation of functional graphs learned from EEG signals under sparsity and structural constraints, where both the locations of the electrodes and a sparse functional connectivity learned from data are explicitly encoded. The resulting images are then fed to a convolutional neural network to extract meaningful latent features prior to inference. The proposed method is applied to Mental Workload (MW) classification. Experimental results on a public dataset demonstrate promising performance compared to state-of-the-art spatial filtering techniques and those based on hand-crafted functional connectivities.

关键词： Electrodes Measurement Filtering Estimation Machine learning image representation Signal processing Feature extraction Electroencephalography Graph neural networks

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Estimation of stochastic Time Lags between Data Sources in Distributed Production Facilities Based on Cross-Correlated Signals 22

Estimation of Stochastic Time Lags between Data Sources in D...

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22nd IEEE International conference on Industrial Technology (ICIT)

作者： Zumsande, Johannes Kortmann, Karl-Philipp Ortmaier, Tobias Wielitzka, Mark Leibniz Univ Hannover Inst Mechatron Syst Hannover Germany

ISBN: (纸本)9781728157306

The increasing degree of automation in modern industrial production is accompanied by a growing number of data sources located in the shop floor at the machines and products. methods of data mining allow to make statements and predictions about the machine processing hidden in heterogeneous data. In condition monitoring, however, large and often indefinite time delays exist between the collected process data and quality measurements at distributed work stations, which make these analyses difficult. In order to estimate the time lags, a probabilistic directed graph can be used. Parameters of such a graph are the sojourn times of the production goods in the stations. To identify these times, cross-correlation and transfer entropy can be applied to signals with known collinearity. To compare the two methods, both simulated and real-world data of an industrial return sand cooler are used. For evaluation, we propose a novel method for image-based time delay distribution measurement in bulk material processing by use of a fluorescent contrast medium. A dependence of the estimation result on the signal's statistic is shown. Nevertheless, without knowledge of the signal model a statement about the magnitude and scattering of the stochastic time delay can be made.

关键词： Production systems lagged signals cross-correlation transfer entropy Gaussian processes

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Hybrid Single Input and Multiple Output Method For Compressing Features Towards Machine Vision Tasks

Hybrid Single Input and Multiple Output Method For Compressi...

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IEEE International conference on image processing

作者： Zifu Zhang Shengxi Li Tie Liu Mai Xu Tao Xu Zhenyu Guan Zhuoyi Lv Beihang University Beijing China vivo Mobile Communication Company Limited Beijing China

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

With the advance of deep learning in the BigData era, image/video coding for machines (VCM) as called for proposals by the moving picture experts group (MPEG) now becomes the pivotal technique for extensive intelligent vision tasks. However, existing VCM methods typically focus on compressing features independently at each scale, ignoring the redundancy of features across multiple scales. This paper thus introduces a simple yet effective architecture called hybrid single input and multiple output (H-SIMO) for VCM, which can significantly reduce the redundancy across scales of features. More specifically, as the pyramid structure is commonly employed for localising multi-scale objects, our H-SIMO method proposes to compress all features by inputting a single-scale feature while retaining the ability to decompress all the features. Moreover, an entropy model is seamlessly integrated into the training process to efficiently reduce the statistical redundancy of features. During the testing phase, the hybrid coding method, in conjunction with the versatile video coding (VVC), is employed to compress the features from both images and videos. We comprehensively evaluate the performance of our H-SIMO method in two standard machine vision tasks: object detection and instance segmentation, in which the experimental results verify the superior performances of our H-SIMO method.

关键词： Video coding Instance segmentation Training image coding Machine vision Termination of employment Transform coding

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Novel Framework for the Improvement of Object Detection Accuracy of Smart Surveillance Camera Visuals using Modified Convolutional Neural Network Technique compared with Global Colour Histogram 1

Novel Framework for the Improvement of Object Detection Accu...

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1st International conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Pooja, Ch. Jaisharma, K. Department of Computer Science Engineering Saveetha University Tamilnadu Chennai602105 India

ISBN: (纸本)9781607685395

Aim: The objective of the proposed work is to evaluate the accuracy of Global Colour Histogram (GCH) and Modified Convolutional Neural Networks Techniques (MCNNT) by changing the hierarchical data processing for Smart Surveillance System. methods and Materials:Our novel framework withMCNNT uses the data in hierarchical models of data processing, which is constructed with fully connected layers and the neurons were connected to every other node to handle complex object detection tasks and evaluated using the sample size of 20 per group with Model image dataset. Result: The recorded mean accuracy of MCNNT (95.28%) is better than GCH (80.48%). There is a statistical significant difference in accuracy for two algorithms performed by independent sample t-test (P © The Electrochemical Society

关键词： Graphic methods

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statistical Prior Knowledge for Robust Medical image Segmentation by Level Set

Statistical Prior Knowledge for Robust Medical Image Segment...

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European Signal processing conference (EUSIPCO)

作者： Dorsaf Hmida Mohamed Amine Mezghich Ines Sakly Slim Mhiri Fouzi Ghorbel CRISTAL Laboratory GRIFT Research Group Ecole Nationale des Sciences de l'Informatique (ENSI) La Manouba University La Manouba Tunisia

Region-based active contours give interesting results when applied for objects detection with poor contrasting edges and noise, but for medical image segmentation, where objects of interest are often present with inhomogeneity, these models fail. In [16], we presented an original approach that integrate prior knowledge on the target shape into active contours with free registration based on a complete and stable set of invariant descriptors. In this work, we intend to present a novel method that incorporate statistical prior knowledge into level set model for robust medical image segmentation resulting from machine learning algorithms. In fact, for medical imaging, in many situations like tumors segmentation, prior knowledge on shape is insufficient or unavailable because it vary from a patient to another. The adopted approach is demonstrated with two unsupervised learning methods. The first one use the Fuzzy C-Means (FCM) and the second one is based on the Hidden Markov Random Field Expectation-Maximization algorithm (HMRF-EM). Both of the two are used to classify the pixels of the target image into two classes: the object and the background to construct our reference shape to be used as prior knowledge. A weighting schema is then considered to guide the active contour model under both forces: the traditional and the proposed one. We validate the robustness and the accuracy of our approach using ground truths given from experts segmentation and standard data bases. Experiments and comparative studies with several recent works are highlighted and show that the integrated method achieves good performance results.

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Lossless image Compression Using a Multi-scale Progressive statistical Model 15th

Lossless Image Compression Using a Multi-scale Progressive ...

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15th Asian conference on Computer Vision, ACCV 2020

作者： Zhang, Honglei Cricri, Francesco R. Tavakoli, Hamed Zou, Nannan Aksu, Emre Hannuksela, Miska M. Nokia Technologies Tampere Finland Tampere University Tampere Finland

ISBN: (纸本)9783030695347

Lossless image compression is an important technique for image storage and transmission when information loss is not allowed. With the fast development of deep learning techniques, deep neural networks have been used in this field to achieve a higher compression rate. methods based on pixel-wise autoregressive statistical models have shown good performance. However, the sequential processing way prevents these methods to be used in practice. Recently, multi-scale autoregressive models have been proposed to address this limitation. Multi-scale approaches can use parallel computing systems efficiently and build practical systems. Nevertheless, these approaches sacrifice compression performance in exchange for speed. In this paper, we propose a multi-scale progressive statistical model that takes advantage of the pixel-wise approach and the multi-scale approach. We developed a flexible mechanism where the processing order of the pixels can be adjusted easily. Our proposed method outperforms the state-of-the-art lossless image compression methods on two large benchmark datasets by a significant margin without degrading the inference speed dramatically. © 2021, Springer Nature Switzerland AG.

关键词： Pixels

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Acoustic leak localization method based on signal segmentation and statistical analysis 10

Acoustic leak localization method based on signal segmentati...

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10th International conference on Modern Circuits and Systems Technologies (MOCAST)

作者： Kousiopoulos, Georgios-Panagiotis Karagiorgos, Nikolaos Kampelopoulos, Dimitrios Konstantakos, Vasileios Nikolaidis, Spyridon Aristotle Univ Thessaloniki Phys Dept Thessaloniki Greece

ISBN: (纸本)9781665418478

One of the most serious problems occurring in a pipeline network is the appearance of leaks. The process of detecting and localizing leaks in pipeline systems concerns a very extensive field of signal processing methods employed for this matter. In this paper a leak localization method combining the segmentation of acoustic leak signals, both in the time and in the frequency domain, with a statistical algorithm needed for dealing with the non-deterministic (stochastic) nature of these signals is proposed. This algorithm involves the use of cross-correlation techniques along with the grouping of the time-delay data in a histogram and selecting the bin with the largest number of elements as the one that provides the correct answer. The successful detection of the leak position requires the knowledge of the acoustic wave velocity in the pipe. In the present paper the calculation of the acoustic velocity is performed by the use of a PCB hammer to cover more realistic situations. The proposed leak localization method is tested experimentally in a laboratory setup containing a 67-meter steel pipeline and the results show that the presented method can localize leaks efficiently, since the average localization error is around 3%.

关键词： pipelines leak localization stochastic signals acoustic wave propagation wave velocity calculation

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Divide-and-Conquer Variational Bayesian Inference for Multi-task Learning of High-resolution SAR imagery

Divide-and-Conquer Variational Bayesian Inference for Multi-...

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

作者： Lei Yang Ming Sun Zhongwei Hu Zenan Zhang Wenxuan Yuan College of Electronic Information and Automation Civil Aviation University of China Tianjin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Conventional statistical-driven synthetic aperture radar (SAR) imaging algorithms can only encode a single and/or static prior, leading to that limited features can be accessed quantitatively. To this end, a novel multi-task learning framework is proposed by devising a divide-and-conquer variational Bayesian (DC-VB) inference, so that elaborated features of interests can be exploited for a high-resolution SAR imagery. Specifically, a flexible generalized Gaussian distribution (GGD) and a customized hybrid probability distribution are introduced and employed for the priors of features of interests. To resolve the resultant complicated Bayesian inference, splitted random variables are incorporated, so that the joint posterior problem can be decomposed into multiple local problems that are easy to be solved. Simultaneously, dual variables are established for residual errors of the decomposition. To guarantee a global solution for the image of the target of interests, the data augmentation is employed to coordinate multiple local solutions. Therefore, the intended Bayesian inference works in a divide-and-conquer manner, which is superior in quantization of multiple features of high-resolution SAR imagery. It is capable of incorporating multiple priors in a fully-statistical probability and guaranteeing closed-form solutions of posterior distributions. Unavoidable propagation errors can be minimized in the DC-VB process. Raw SAR data is applied to validate the effectiveness of the proposed algorithm. Comparisons with conventions show the superiority in terms of qualitative and quantitative aspects.

关键词： Quantization (signal) Closed-form solutions Signal processing algorithms Multitasking Data augmentation Radar polarimetry Inference algorithms Bayes methods Speech processing Synthetic aperture radar

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