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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181-190 订阅

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Functional Graph image Representation applied to EEG-based Mental Workload Classification 46

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

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46th Annual International conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Sarkis, Maria Rizkallah, Mira Moussaoui, Saïd Nantes Université École Centrale Nantes LS2N Cnrs Umr 6004 NantesF-44300 France

ISBN: (纸本)9798350371499

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. © 2024 IEEE.

关键词： Convolutional neural networks

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A New Elimination Based Multi-Material Reconstruction for Spectral CT Imaging 3

A New Elimination Based Multi-Material Reconstruction for Sp...

引用

3rd International conference on Optics and image processing, ICOIP 2023

作者： Yu, Xiaohuan Cai, Ailong Li, Lei Yan, Bin Henan Key Laboratory of Imaging and Intelligent Processing PLA Strategic Support Force Information Engineering University Zhengzhou China

ISBN: (纸本)9781510667426

Spectral computed tomography (spectral CT) is an emerging imaging technology that is capable of distinguishing material properties. However, the difficulty of decomposition process is intensified by the nonlinearity of the measurements and ill-conditioned problem, particularly when the number of materials and energies do not match. Therefore, the key issue in spectral CT is to design an improved algorithm in the accurate material decomposition. This study proposes a one-step multi-material algorithm that combines a statistical reconstruction model with a gradient-sparsity based prior. In our approach, the elimination based method under volume conservation constrained condition is designed for the inconsistent scanning of number of materials and energies. Newton descent method is adopted to efficiently solve the optimization problem based on a simple surrogate function. Through simulated experiments, the proposed method achieves a significantly higher peak signal-to-noise ratio (PSNR) compared to other algorithms, with an increase of approximately 23.988 dB and 23.462 dB. Numerical experiments have confirmed the efficiency of our proposed method in reconstructing the material distributions while reducing noise compared to state-of-the-art methods. © 2023 SPIE. All rights reserved.

关键词： Signal to noise ratio

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Evaluating the Capacity of Deep Generative Models to Reproduce Measurable High-order Spatial Arrangements in Diagnostic images

Evaluating the Capacity of Deep Generative Models to Reprodu...

引用

conference on Medical Imaging - image processing

作者： Deshpande, Rucha Anastasio, Mark A. Brooks, Frank J. Washington Univ Dept Biomed Engn St Louis MO 63130 USA Univ Illinois Dept Bioengn Urbana IL 61801 USA

ISBN: (数字)9781510649408

ISBN: (纸本)9781510649408;9781510649392

Given the recent interest in the role of deep generative models (DGM) in medical imaging pipelines, it is imperative to evaluate the capacity of such models to generate medically accurate images. Popular methods of evaluation of natural images generated using generative adversarial networks (GANs), a type of DGM, are often applied to medical data. Such methods are insufficient to evaluate anatomical realism, representations of which include high-order spatial information. To our knowledge, no test exists for the faithful replication of spatial statistics beyond the second-order. In this work, purposefully designed stochastic object models (SOMs) are proposed to encode predetermined rules governing the prevalence of features within single images, thus encoding known high-order spatial information within each realization. These SOMs are independent of the network architecture being tested and can also be applied to any new architecture that may be proposed. Two popular GANs are trained on these SOM datasets and the generated images are tested for the encoded statistics. It is observed that although ensemble statistics might be well replicated, this is not necessarily true for realization i.e., per-image statistics. Thus, GAN-generated images might not be ready for clinical use. With the proposed SOMs, the rate of image errors and the rate of feature malformation can be quantified for any architecture, while providing one measure of GAN utility in a diagnostic scenario.

关键词： high-order statistics deep generative models stochastic object models generative adversarial network (GAN) evaluation

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Stability and Generalization for Markov Chain stochastic Gradient methods 36

Stability and Generalization for Markov Chain Stochastic Gra...

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36th conference on Neural Information processing Systems (NeurIPS)

作者： Wang, Puyu Lei, Yunwen Ying, Yiming Zhou, Ding-Xuan City Univ Hong Kong Liu Bie Ju Ctr Math Sci Hong Kong Peoples R China Univ Birmingham Sch Comp Sci Birmingham W Midlands England SUNY Albany Dept Math & Stat Albany NY USA Univ Sydney Sch Math & Stat Sydney NSW Australia

ISBN: (纸本)9781713871088

Recently there is a large amount of work devoted to the study of Markov chain stochastic gradient methods (MC-SGMs) which mainly focus on their convergence analysis for solving minimization problems. In this paper, we provide a comprehensive generalization analysis of MC-SGMs for both minimization and minimax problems through the lens of algorithmic stability in the framework of statistical learning theory. For empirical risk minimization (ERM) problems, we establish the optimal excess population risk bounds for both smooth and non-smooth cases by introducing on-average argument stability. For minimax problems, we develop a quantitative connection between on-average argument stability and generalization error which extends the existing results for uniform stability [38]. We further develop the first nearly optimal convergence rates for convex-concave problems both in expectation and with high probability, which, combined with our stability results, show that the optimal generalization bounds can be attained for both smooth and non-smooth cases. To the best of our knowledge, this is the first generalization analysis of SGMs when the gradients are sampled from a Markov process.

关键词： Markov processes

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statistical Analysis of Hair Detection and Removal Techniques Using Dermoscopic images 7th

Statistical Analysis of Hair Detection and Removal Technique...

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7th International conference on Computer Vision and image processing, CVIP 2022

作者： Shinde, Apurva Chaudhari, Sangita Ramrao Adik Institute of Technology D.Y. Patil Deemed to be University Nerul Navi Mumbai India

ISBN: (纸本)9783031314162

Deaths due to various types of cancers have increased to a greater extent in decades. Computer-aided diagnosis is the fast and efficient way used in the medical field all around the globe for early diagnosis and treatment of cancer. The design of such automated systems is a major challenge in the medical field due to various aspects and the availability of data for testing these systems. Skin cancer is one such type of cancer that if treated at an early stage helps to reduce the mortality rate. Many technological solutions have been provided by researchers in the last decade for the early detection and classification of skin cancer. Hair detection and removal is one of the primary pre-processing step in the skin cancer detection process. Dull Razor, Adaptive Principal Curvature, E-shaver, etc. are common techniques used for hair detection and removal. These methods aim to remove the hairs from the lesion image, but some artifacts and background abnormalities are left behind in the resultant images. In this paper, slight functional modification using different color spaces and hybridization of various existing techniques for hair detection and removal has been proposed. The proposed techniques are evaluated on standard dermoscopic datasets using different standard performance metrics like Accuracy, Sensitivity, Specificity, False Positive Rate, Peak Signal to Noise Ratio, and Structural Similarity Index Measure. The proposed pre-processing methods are tested for classification accuracy using VGG-16 model. The evaluation results indicate that Modified E-shaver and Modified Dull Razor methods perform better than existing systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Lung cancer

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Application of Computer Simulation Virtual Reality Simulation Technology in the statistical Analysis of Urban Building Communities

Application of Computer Simulation Virtual Reality Simulatio...

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2022 IEEE Asia-Pacific conference on image processing, Electronics and Computers, IPEC 2022

作者： Xie, Yiwen Wuhan University of Technology School of Civil Engineering and Architecture Wuhan430070 China

ISBN: (纸本)9781665409025

The sustainable development of urban building communities in China needs the support of new concepts and integrated technologies such as virtual geographic environment. Based on the related theories and technical methods of virtual geographic environment, this paper proposes a multi-disciplinary cross-discipline such as planning and design, surveying and mapping geographic information, geotechnical engineering, ecological protection;planning designers, GIS analysts, government departments, project owners, the public and other roles participate;multi-link integration of urban building community statistics. Experiments show that this method can create different types of virtual scenes of three-dimensional architectural spaces such as residences, public buildings, gardens and landscapes, and has better real-time performance and better user experience. © 2022 IEEE.

关键词： Virtual reality

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stochastic Optimization of Vector Quantization methods in Application to Speech and image processing

Stochastic Optimization of Vector Quantization Methods in Ap...

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

作者： Mohammad Hassan Vali Tom Bäckström Department of Signal Processing and Acoustics Aalto University Finland

Vector quantization (VQ) methods have been used in a wide range of applications for speech, image, and video data. While classic VQ methods often use expectation maximization, in this paper, we investigate the use of stochastic optimization employing our recently proposed noise substitution in vector quantization technique. We consider three variants of VQ including additive VQ, residual VQ, and product VQ, and evaluate their quality, complexity and bitrate in speech coding, image compression, approximate nearest neighbor search, and a selection of toy examples. Our experimental results demonstrate the trade-offs in accuracy, complexity, and bitrate such that using our open source implementations and complexity calculator, the best vector quantization method can be chosen for a particular problem.

关键词： Additives Speech coding Vector quantization Bit rate Toy manufacturing industry Neural networks Nearest neighbor methods

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Characterization of Early Termination for stochastic Computing image processing Circuits

Characterization of Early Termination for Stochastic Computi...

引用

International conference on Computer Applications and Industrial Electronics (ICCAIE)

作者： Izam Kamisian Muhammad Nadzir Marsono Norlina Paraman Afiq Hamzah Jamaluddin Zakaria Universiti Teknologi Malaysia (UTM) Johor Bahru Malaysia

ISBN: (数字)9798350348798

ISBN: (纸本)9798350348804

stochastic computing (SC) performance is signifi-cantly impacted by the length of the bitstream, which directly affects both computation time and throughput. Researchers have proposed early termination methods to reduce bitstream length, relying on stochastic bitstream patterns generated by stochastic number generators (SNGs). In this study, we investigate early termination specifically for median filter image processing ap-plications. We analyze several SNGs, including those based on ‘randperm,’ linear feedback shift registers (LFSR), Streaming Accurate (SA), and counter-based approaches. These SNGs are evaluated using different image inputs. Our experimental results demonstrate that early termination can be safely employed. Notably, the use of counter-based SNGs naturally facilitates early termination in median filter applications. By leveraging this approach, we achieve bitstream length reduction without sacrificing accuracy. The extent of reduction varies based on the specific image inputs. In summary, our findings highlight the effectiveness of early termination strategies, particularly when utilizing counter-based SNGs for median filter computations.

关键词： Industrial electronics Accuracy image processing Linear feedback shift registers image edge detection Circuits Benchmark testing

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Neuromorphic image Coding Based on a Sample of Counts Partition by a System of Receptive Fields 26th

Neuromorphic Image Coding Based on a Sample of Counts Partit...

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26th International conference on Pattern Recognition, ICPR 2022

作者： Antsiperov, Viacheslav Kotelnikov Institute of Radioengineering and Electronics of RAS Mokhovaya 11-7 Moscow Russia

ISBN: (纸本)9783031377419

The problem of synthesis of image encoding methods based on the data of the images themselves is considered. The proposed approach is based on the previously developed special representation of images by samples of counts (sampling representations). Since sampling representations are essentially random constructions, the synthesis of encoding methods is carried out strictly within the generative approach. Within the framework of this approach, the image coding procedure is treated as a special case of statistical parametric estimation of the probability distribution density. The choice of a parametric family of possible distributions is limited in work by a model of a mixture of predefined components. Accordingly, a set of estimates of the weights of the mixture components, calculated from a sampling representation, considered as input data, is interpreted as the output of the image encoding procedure. In this context, the natural criterion for optimal coding is the maximum likelihood criterion. For the algorithmic implementation of the coding procedure, the mixture model is equipped with a structure of receptive fields, known in neurophysiology as the basic principle of organizing human retinal receptors. On this basis, a relatively simple recurrent coding algorithm is synthesized, close to the popular EM algorithm in machine learning. The article presents an interpretation of some features of the algorithm from the point of view of known facts about image processing in the periphery of the visual system, discusses options for implementing the algorithm, and presents some results of numerical simulation. © 2023, Springer Nature Switzerland AG.

关键词： Encoding (symbols)

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A statistical Method for the Number and Size of Luminescent Zooplankton in Deep Sea 5

A Statistical Method for the Number and Size of Luminescent ...

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5th International conference on Advanced Electronic Materials, Computers and Software Engineering, AEMCSE 2022

作者： Zhou, Yulong Zhang, Xi Wang, Yuxing Zhao, Fei Shanghai University School of Mechatronic Engineering and Automation Shanghai China

ISBN: (数字)9781665484749

ISBN: (纸本)9781665484749

As an important part of marine ecosystem, luminescent zooplankton is of great significance to the study of marine ecology and carbon cycle. The statistics of the number and size of luminous zooplankton is an important content of research. In recent years, the statistical methods of quantity and size based on visual image technology have attracted extensive attention. However, visual image technology has the problems of low accuracy, and can't deal with sensor noise and too close biological distance. In order to solve the above problems, a statistical method for the number and size of luminescent zooplankton in deep-sea is proposed based on the shape characteristics of luminous zooplankton and multi feature matching rules. Through comparative experiments, the algorithm proposed in this paper has higher accuracy in quantity statistics and particle size calculation than other algorithms. © 2022 IEEE.

关键词： image processing

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