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

共 4723 条记录，以下是811-820 订阅

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statistical Modeling of ISAR Imaging based on Bayesian Compressive Sensing of Pareto Distribution F amily

Statistical Modeling of ISAR Imaging based on Bayesian Compr...

引用

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

作者： C.Y Xia Z.L Zhou Chun-Bo Guo Y. S Hao The 2-nd Research Institute Civil Aviation Administration Chengdu China Company of Air Control Chengdu China

ISBN: (数字)9781728185897

ISBN: (纸本)9781728185903

The paper aims to compare the potential of two popular flexible laws in Pareto distribution family, the Exponentiated generalized Pareto distribution (exGPD) and the Gamma-ParetoIV distribution (GPIVD), for the statistical modeling of inverse synthetic aperture radar (ISAR) imaging data. From the perspective of enhancing compressibility and sparsity of the target echo, a novel model named GPIVD-priorbased Bayesian compressed sensing (GPIVCS) is deducted, the analysis proves that the sparsity and compressibility of the GPIVCS model are obtained, further improve the image quality. And from the perspective of reducing imaging complexity, a Bayesian compressed sensing method based on exGPD prior is proposed (EX-GPCS), compared with the conventional methods, the EX-GPCS method is capable of imaging targets in a short period of time. This paper gives a detailed formula derivation of the imaging process of the above two models. Finally, the stateof-the-art performance of these proposed methods is verified by experiments with a wide range of ISAR images.

关键词： Imaging Radar imaging Bayes methods Radar Compressed sensing image reconstruction image quality

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statistical optical image analysis for information system 7

Statistical optical image analysis for information system

引用

7th International conference on Future Internet of Things and Cloud Workshops, FiCloudW 2019

作者： Ryazantsev, Oleksandr Khoroshun, Ganna Riazantsev, Andrii Ivanov, Vitalii Baturin, Aleksandr Volodymyr Dahl East Ukrainian National University Faculty of Information Technology and Electronics Severodonetsk Ukraine Dept. of Programming and Mathematics Volodymyr Dahl East Ukrainian National University Severodonetsk Ukraine

ISBN: (纸本)9781728144115

Optical image processing methods are important for creation and development of an automated measuring information optical system. statistical methods provide additional information, a better understanding of the object that is being studied. They allow describing main features of the image by several parameters, such as mean value, median and standard deviation. For correlation of the computer simulation and experimental data the method of signal quantization is described. It is defined the parameters of the fitted normal distribution for the optical image variation curve and consent criteria of empirical and theoretical data by Romanovsky method. Obtained results can be used as the preliminary analysis for digital processing of the optical image with application in compression and encryption, holography, identification and tracking. © 2019 IEEE.

关键词： Optical systems

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Effect of statistical Mismatch between Training and Test images for CNN-Based Deformable Registration

Effect of Statistical Mismatch between Training and Test Ima...

引用

conference on Medical Imaging: image processing

作者： Ketcha, M. D. De Silva, T. Han, R. Uneri, A. Vogt, S. Kleinszig, G. Siewerdsen, J. H. Johns Hopkins Univ Dept Biomed Engn Baltimore MD 21218 USA Siemens Healthcare XP Div Erlangen Germany

ISBN: (纸本)9781510625464

Recently, convolutional neural networks (CNNs) have been proposed as a method for deformable image registration, offering a variety of potential advantages compared to physical model-based methods, including faster runtime and ability to learn complicated functions without explicit models. A persistent question for CNNs is the uncertainty in their behavior when the image statistics (e.g., noise and resolution) of the test data deviate from those of the training data. In this work we investigated the influence of statistical properties of image noise (in CT, for example, related to radiation dose). We trained registration networks over a range of dose levels and evaluated registration performance (target registration error, TRE) as the statistics of the test data deviated from that of the training data. Generally, registration performance was optimal when the statistics of the test data matched that of the training data. Furthermore, TRE was found to be limited by the highest dose training data, with no improvement in TRE for test images of higher dose than that in the training data. Understanding and quantifying the relationship between statistical aspects of the training and test data - and the failure modes caused by statistical mismatch - is an important step in the development of CNN based registration methods. This work provided new insight on the optima and tradeoffs with respect to image noise (dose), providing important guidance in building training sets that are best-suited to particular imaging conditions and applications.

关键词： Convolutional Neural Networks Deformable image Registration image Statistics

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Underwater image enhancement of improved retinex base on statistical learning 8

Underwater image enhancement of improved retinex base on sta...

引用

8th IEEE Data Driven Control and Learning Systems conference, DDCLS 2019

作者： Xu, Lixue Wang, Xiubo Wang, Xudong Harbin Engineering University Department of Automation Harbin150001 China Heilongjiang Institute of Geological Surveying and Mapping Harbin China

ISBN: (纸本)9781728114545

Underwater image enhancement technology is one of the key parts of underwater image processing, Since under water environment has greater attenuation and scattering effects on reflected light than the onshore environment that lead to uneven imaging, low contrast, color decay, etc. The underwater enhancement process is intended to highlight some key information of the image and eliminate the irrelevant information so as to make the preparations for subsequent scene segmentation, target recognition, target tracking and predictive analysis and processing. In this paper, we will focus on the problem of underwater image with low quality, and analyze the typical methods of box-based image enhancement methods. At the same time, Retinex image enhancement method based on optical analysis was introduced, and this paper proposed an improved enhancement method based on the Retinex methods. First we convert the image into HSV space for saliency analysis, next, learn the parameters of the harmonic HSV space by statistical learning. And then, we use the weights obtained by learning to predict the enhancement parameter for the decay image. Finally, the experiment verified that the method had better adaptability and better effect over the traditional Retinex algorithm in underwater image enhancement. © 2019 IEEE.

关键词： image enhancement

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Text-To-image Synthesis Method Evaluation Based On Visual Patterns

Text-To-Image Synthesis Method Evaluation Based On Visual Pa...

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IEEE International conference on Acoustics, Speech and Signal processing

作者： William Lund Sommer Alexandros Iosifidis Department of Engineering Aarhus University Denmark

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

A commonly used evaluation metric for text-to-image synthesis is the Inception score (IS) [1], which has been shown to be a quality metric that correlates well with human judgment. However, IS does not reveal properties of the generated images indicating the ability of a text-to-image synthesis method to correctly convey semantics of the input text descriptions. In this paper, we introduce an evaluation metric and a visual evaluation method allowing for the simultaneous estimation of the realism, variety and semantic accuracy of generated images. The proposed method uses a pre-trained Inception network [2] to produce high dimensional representations for both real and generated images. These image representations are then visualized in a 2-dimensional feature space defined by the t-distributed stochastic Neighbor Embedding (t-SNE) [3]. Visual concepts are determined by clustering the real image representations, and are subsequently used to evaluate the similarity of the generated images to the real ones by classifying them to the closest visual concept. The resulting classification accuracy is shown to be a effective gauge for the semantic accuracy of text-to-image synthesis methods.

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Stationarity testing in 2D image analysis 42

Stationarity testing in 2D image analysis

引用

conference on Applications of Digital image processing XLII

作者： Kukal, Jaromir Nachtigalova, Iva Krbcova, Zuzana Svihlik, Jan Fliegel, Karel Univ Chem & Technol Tech 5 Prague 6 Czech Republic Czech Tech Univ Tech 2 Prague 6 Czech Republic

ISBN: (数字)9781510629684

ISBN: (纸本)9781510629684

Signal and image stationarity is the underlying assumption for numerous signal analysis methods. However, this assumption is usually not valid in real case scenarios. The paper is focused on local stationarity testing using a small symmetric neighborhood. The neighborhood is split into two parts, which should have the same statistical properties when the hypothesis of image stationarity is valid. We apply various testing approaches (two-sampled F -test, t-test, WMW, K-S) to obtain adequate p-values for a given pixel, mask position, and test type. Finally, using a set of masks and tests, we obtain a series of p-values for every pixel. Applying False Discovery Rate (FDR) methodology, we localize all the pixels where any hypothesis fails. Resulting binary image is an alternative to traditional edge detection but with a strong statistical background.

关键词： image stationarity local testing edge detection multiple hypothesis testing

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A NEW SPATIAL STEGANOGRAPHIC SCHEME BY MODELING image RESIDUALS WITH MULTIVARIATE GAUSSIAN MODEL 44

A NEW SPATIAL STEGANOGRAPHIC SCHEME BY MODELING IMAGE RESIDU...

引用

44th IEEE International conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Qin, Xinghong Li, Bin Huang, Jiwu Shenzhen Univ Guangdong Key Lab Intelligent Informat Proc Shenzhen 518060 Peoples R China Shenzhen Univ Coll Informat Engn Shenzhen Key Lab Media Secur Shenzhen 518060 Peoples R China Peng Cheng Lab Shenzhen 518024 Peoples R China

ISBN: (纸本)9781479981311

Embedding costs used in content-adaptive image steganographic schemes can be defined in a heuristic way or with a statistical model. Inspired by previous steganographic methods, i.e., MG (multivariate Gaussian model) and MiPOD (minimizing the power of optimal detector), we propose a model-driven scheme in this paper. Firstly, we model image residuals obtained by high-pass filtering with quantized multivariate Gaussian distribution. Then, we derive the approximated Fisher Information (FI). We show that FI is related to both Gaussian variance and filter coefficients. Lastly, by selecting the maximum FI value derived with various filters as the final FI, we obtain embedding costs. Experimental results show that the proposed scheme is comparable to existing steganographic methods in resisting steganalysis equipped with rich models and selection-channel-aware rich models. It is also computational efficient when compared to MiPOD, which is the state-of-the-art model-driven method.

关键词： Steganography steganalysis multivariate Gaussian model spatial images

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stochastic deterministic methods for processing signals and images in optical electronic systems

Stochastic deterministic methods for processing signals and ...

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44th WILGA Symposium on Photonics Applications and Web Engineering

作者： Strikova, Tetiana O. Lytyuga, Oleksandr P. Skorupski, Krzysztof Bugubayeva, Alina Kharkiv Natl Univ Radio Elect Kharkiv Ukraine Lublin Univ Technol Nadbystrzycka 38A PL-20618 Lublin Poland East Kazakhstan State Tech Univ AK Protozanov St 69 Ust Kamenogorsk 070004 Kazakhstan

ISBN: (纸本)9781510630666

The theoretical and experimental studies of statistical properties of the input signals of optical electronic systems were carried out. The statistical models of interaction of an optical signal in optoelectronic systems based on the the distribution laws, characterized by a finite and infinite dispersion, were developed. The study of the asymptotic behavior of tailings of the output signals distribution densities for optoelectronic systems proved the possibility of using stable distribution laws describing the source signals of optoelectronic systems. The boundaries of statistical models applicability were outlined based on the central and generalized boundary theorems. Theoretical and experimental researches with the provision of high accuracy of determination of signals spatial-temporal characteristics with increased probabilistic detection characteristics were presented.

关键词： Optoelectronic systems photodetector signal fluctuations the Poisson model of signals the Gaussian and non-Gaussian statistics generalized limit theorems signal detection image processing methods

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Contrast Enhancement of Medical images Using statistical methods with image processing Concepts

Contrast Enhancement of Medical Images Using Statistical Met...

引用

International Engineering conference (IEC)

作者： Zohair Al-Ameen College of Computer Science and Mathematics University of Mosul Mosul Nineveh Iraq

ISBN: (数字)9781728159102

ISBN: (纸本)9781728159119

The process of generating pictorial representations of the inner human body for medical analysis and intervention is called medical imaging (MI). MI strives for revealing inner structures concealed by the existence of bones and skin to identify diseases in order to be treated properly. MI can be achieved through the use of various imaging systems, where each has different technology necessities. The output of these imaging systems is digital images that have meaningful medical information. Such images, however, do not own a high perceived quality due to the existence of image degradations. Despite modern technology advancement, various medical systems are still producing images with poor contrast owing to incorrect settings and device limitations. Such images must be processed efficiently to become clearer for better analysis, understanding and interpretation. In this study, a simple algorithm is developed, where it utilizes a combination of image processing concepts and statistical methods. The developed algorithm is appraised with a dataset of real-degraded low-contrast images only, assessed with one specialized no-reference metric and compared with four known contrast enhancement methods. From the conducted experiments, the proposed algorithm showed promising performances, since it produced acceptable-quality results rapidly and outperformed the comparison methods in different important aspects.

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Assessing Personally Perceived image Quality via image Features and Collaborative Filtering 32

Assessing Personally Perceived Image Quality via Image Featu...

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IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Korhonen, Jari Shenzhen Univ Sch Comp Sci & Software Engn Shenzhen Peoples R China

ISBN: (纸本)9781728132938

During the past few years, different methods for optimizing the camera settings and post-processing techniques to improve the subjective quality of consumer photos have been studied extensively. However, most of the research in the priorart has focused on finding the optimal method for an average user. Since there is large deviation in personal opinions and aesthetic standards, the next challenge is to find the settings and post-processing techniques thatfit to the individualusers 'personaltaste. In this study, we aim to predict the personallyperceived image quality by combining classical imagefeature analysis and collaboration filtering approach known from the recommendation systems. The experimental resultsfor the proposed method show promising results. As a practical application, our work can be used for personalizing the camera settings orpost-processingparameterfsor different users and images.

关键词： Low-level Vision statistical Learning Vision Applications and Systems

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