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

共 4722 条记录，以下是591-600 订阅

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stochastic Gradient Descent in Correlated Settings: A Study on Gaussian Processes 34

Stochastic Gradient Descent in Correlated Settings: A Study ...

引用

34th conference on Neural Information processing Systems (NeurIPS)

作者： Chen, Hao Zheng, Lili Al Kontar, Raed Raskutti, Garvesh Univ Wisconsin Madison Madison WI 53706 USA Univ Michigan Ann Arbor MI USA

ISBN: (纸本)9781713829546

stochastic gradient descent (SGD) and its variants have established themselves as the go-to algorithms for large-scale machine learning problems with independent samples due to their generalization performance and intrinsic computational advantage. However, the fact that the stochastic gradient is a biased estimator of the full gradient with correlated samples has led to the lack of theoretical understanding of how SGD behaves under correlated settings and hindered its use in such cases. In this paper, we focus on the Gaussian process ( GP) and take a step forward towards breaking the barrier by proving minibatch SGD converges to a critical point of the full loss function and recovers model hyperparameters with rate O(1/K) up to a statistical error term depending on the minibatch size. Numerical studies on both simulated and real datasets demonstrate that minibatch SGD has better generalization over state-of-the-art GP methods while reducing the computational burden and opening up a new, previously unexplored, data size regime for GPs.

关键词： Gaussian noise (electronic)

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Design and synthesis of FIR filter banks using area and power efficient stochastic Computing

Design and synthesis of FIR filter banks using area and powe...

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4th World conference on Smart Trends in Systems, Security and Sustainability (WorldS4)

作者： Mahesh, V. V. Shahana, T. K. Cochin Univ Sci & Technol Sch Engn Div Elect Engn Kochi Kerala India

ISBN: (数字)9781728168234

ISBN: (纸本)9781728168234

stochastic computing is based on probability concepts which are different from conventional mathematical operations. Advantages of stochastic computing in the fields of neural networks and digital image processing have been reported in literature recently. Arithmetic operations especially multiplications can be performed either by logical AND gates in unipolar format or by EXNOR gates in bipolar format in stochastic computation. stochastic computing is inherently fault-tolerant and requires fewer logic gates to implement arithmetic operations. Long computing time and low accuracy are the main drawbacks of this system. In this presentation, to reduce hardware requirement and delay, modified stochastic multiplication using AND gate array and multiplexer are used for the design of Finite Impulse Response Filter cores. Performance parameters such as area, power and delay for FIR filter using modified stochastic computing methods are compared with conventional floating point computation.

关键词： stochastic computing Linear feedback shift register Finite Impulse Response Filter

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Stability and generalization for Markov chain stochastic gradient methods 22

Stability and generalization for Markov chain stochastic gra...

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Proceedings of the 36th International conference on Neural Information processing Systems

作者： Puyu Wang Yunwen Lei Yiming Ying Ding-Xuan Zhou Liu Bie Ju Centre for Mathematical Sciences City University of Hong Kong School of Computer Science University of Birmingham Department of Mathematics and Statistics State University of New York at Albany School of Mathematics and Statistics University of Sydney

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.

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Locally Optimal Detection of stochastic Targeted Universal Adversarial Perturbations

Locally Optimal Detection of Stochastic Targeted Universal A...

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IEEE International conference on Acoustics, Speech and Signal processing

作者： Amish Goel Pierre Moulin University of Illinois at Urbana-Champaign Urbana Illinois

Deep learning image classifiers are known to be vulnerable to small adversarial perturbations of input images. In this paper, we derive the locally optimal generalized likelihood ratio test based detector for detecting stochastic targeted universal adversarial perturbations to a classifier's input. We employ a two-stage process to learn the detector's parameters, which involves unsupervised maximum likelihood estimation followed by supervised training and demonstrates better performance of the detector compared to other detection methods on several popular image classification datasets.

关键词： Training Deep learning Maximum likelihood estimation Perturbation methods conferences Detectors Signal processing

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Research on image Enhancement Model Based on Variable Order Fractional Differential CLAHE 16th

Research on Image Enhancement Model Based on Variable Order ...

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16th EAI International conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2020

作者： Huang, Guo Xu, Li Chen, Qing-li Zhang, Xiu-qiong Men, Tao Qin, Hong-ying Sichuan Province University Key Laboratory of Internet Natural Language Intelligent Processing Leshan Normal University Leshan614000 China School of Electronics and Materials Engineering Leshan Normal University Leshan614000 China School of Computer Science Sichuan University Chengdu610064 China

ISBN: (纸本)9783030775681

image visual effects can be enhanced primarily through edge and texture enhancement or contrast enhancement. image enhancement based on fractional differential can effectively enhance image details such as edge and texture using the weak derivative property of the 0–1-order fractional differential operator. image enhancement based on gray statistics involves the redistribution of light and dark pixels to enhance the overall contrast of the enhanced image as well as the enlargement of the gray-level dynamic range, thereby improving the visual effect of the image effectively. To enhance the edge and texture information of the image, enhance the contrast of the image effectively, and then improve the visual effect of the image, an image enhancement model based on contrast limited adaptive histogram equalization incorporating a fractional differential operator is proposed. The image enhancement model incorporates a fractional differential operator into the adaptive limited contrast image enhancement model, which can enhance the image contrast and effectively enhance the edge and texture details of the image simultaneously. Experimental results show that the proposed variable-order fractional differential contrast-limited adaptive histogram equalization image enhancement model can significantly improve the contrast of the image compared with the traditional fractional differential image enhancement model;additionally, it can effectively enhance the edge and texture details of the image compared with the traditional image enhancement model, which is based on statistical methods. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： image enhancement

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Preprocessing method comparisons for VGG16 fast-RCNN pistol detection 29

Preprocessing method comparisons for VGG16 fast-RCNN pistol ...

引用

29th International conference on Software Engineering and Data Engineering, SEDE 2020

作者： Li, Jiahao Ablan, Charles Wu, Rui Guan, Shanyue Yao, Jason Department of Engineering East Carolina University GreenvilleNC United States Department of Computer Science East Carolina University GreenvilleNC United States

In recent years, gun detection and threat surveillance became a popular issue as gun violence continued to threaten public safety. Convolution Neural Networks (CNN) has achieved impressive gun detection precision with the advancements in graphic processing units. While many articles have proposed beneficial complex architectures within the neural network, there has been little study on effective image preprocessing techniques that supplement neural networks. With the objective of increasing neural net precision using image processing techniques, this research analyzes three different approaches to image preprocessing using a VGG16 trained Fast Regional Convolutional Neural Network (F- RCNN) pistol detector. The base VGG16 was trained with transfer learning in MATLAB on a dataset of 1500 pistol images and tested on 500 more. The results of the original VGG16 detector are compared with the results of the other VGG16 detectors trained with various image processing techniques to determine the viability of each technique. The three image processing techniques are as follows, color contrast enhancement, principle component analysis (PCA), and a combined preprocessing method. After testing the detector trained with the three methods above, it was found that the color enhancement technique had the best success in raising precision with proper levels of color contrast adjustments. The PCA analysis proved to be incompatible for the neural net to learn features of images that has not underwent PCA processing and thus the method failed to produce beneficial results on the unmodified testing dataset. The combined method processing took both PCA and color contrast enhancement techniques and combined the results into a single training dataset. The combined preprocessing method proved to be ineffective in raising precision potentially due to conicting features. © 2021, EasyChair. All rights reserved.

关键词： statistical tests

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Discriminant and Complementary Autoencoder Network for Multi-view Learning

Discriminant and Complementary Autoencoder Network for Multi...

引用

image processing, Electronics and Computers (IPEC), Asia-Pacific conference on

作者： Fu Changqing Zhang Hanmei Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781728190198

In real-world applications, objects can be sampled by different equipment or collected from different angles, which appears a significant and challenging problem for classifying samples from these heterogeneous views. Although effectiveness is achieved, most of the previous methods could not learn a discriminant and complementary representation simultaneously. Differently, in this paper, we propose a novel model termed Discriminant and Complementary Autoencoder Network (DCAEN), which integrates discriminant information between views into a compact representation by the variant of the traditional LDA-like framework. The proposed method has the following merits: (1) our model incorporates supervised information into the common representation subspace, i.e., maximize the between-class distance and minimize the within-class distance from the inter-view and intra-view. (2) our model performs view-specific representation and common representation in a unified framework. The proposed model is solved by the stochastic gradient descent (SGD) algorithm and its performance is far better than many state-of-the-art methods, demonstrating its superiority.

关键词： Computers image coding image recognition Computational modeling conferences stochastic processes Data models

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SSTNET: DETECTING MANIPULATED FACES THROUGH SPATIAL, STEGANALYSIS AND TEMPORAL FEATURES

SSTNET: DETECTING MANIPULATED FACES THROUGH SPATIAL, STEGANA...

引用

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

作者： Wu, Xi Xie, Zhen Gao, YuTao Xiao, Yu Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9781509066315

Compared to conventional object detection which focuses on high-level image content, face manipulation detection pays more attention to low-level artifacts and temporal discrepancies. However, there are few methods considering both of these two characteristics. In this work, we propose a novel manipulation detection framework, named SSTNet, which detects tampered faces through Spatial, Steganalysis and Temporal features. Spatial features are extracted by a deep neural network for finding visible tampering traces like unnatural color, shape and texture. We propose a constraint on convolutional filters to extract steganalysis features for detecting hidden tampering artifacts like abnormal statistical characteristics of image pixels. Temporal features are extracted by a recurrent network for discovering inconsistency between consecutive frames. Experimental results on Face-Forensics++ dataset demonstrate that SSTNet outperforms other methods and achieves state-of-the-art performance on accuracy and robustness to compression. Furthermore, the generalization capability of SSTNet is verified on the GAN-based DeepFakes dataset.

关键词： Face manipulation detection Fake video detection image forensics

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Audio Cough Analysis by Parametric Modelling of Weighted Spectrograms to Interpret the Output of Convolutional Neural Networks

Audio Cough Analysis by Parametric Modelling of Weighted Spe...

引用

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

作者： P. Amado-Caballero J. R. Garmendia-Leiza M. D. Aguilar-García C. Fernández-Martínez-De-Septiem L. M. San-José-Revuelta A. García-Ruano C. Alberola-López P. Casaseca-De-La-Higuera Laboratory of Image Processing (LPI) E.T.S. Ingenieros de Telecomunicación Universidad de Valladolid Valladolid Spain Centro de Salud Los Jardinillos SACYL Palencia Spain Hospital Universitario de Burgos SACYL Burgos Spain Centro de Salud de Alaejos SACYL Alaejos (Valladolid) Spain

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

This study explores the feasibility of employing eXplainable Artificial Intelligence (XAI) methodologies for the analysis of cough patterns in respiratory diseases. A cohort of 20 adult patients, all presenting persistent cough as a symptom of respiratory disease, was monitored for 24 hours using a smartphone. The audio signals underwent frequency domain transformation to yield 1-second spectrograms, subsequently processed by a CNN to detect cough events. Quantitative analysis of spectrogram regions relevant for cough detection highlighted by occlusion maps, revealed significant differences between patient groups. Notably, distinctions were observed between the Chronic Obstructive Pulmonary Disease (COPD) patient group and groups with other respiratory pathologies, both chronic and non-chronic. In conclusion, interpretability analysis methods applied to neural networks offer insights into cough-related distinctions among patients with varying respiratory conditions.

关键词： Pathology Explainable AI statistical analysis Frequency-domain analysis Neural networks Chronic obstructive pulmonary disease Parametric statistics Convolutional neural networks Spectrogram Monitoring

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Detection of Tampered Region Boundaries in Splicing Forgery images 28

Detection of Tampered Region Boundaries in Splicing Forgery ...

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28th Signal processing and Communications Applications conference (SIU)

作者： Mutlu, Ozlem Genc, Yakup Gebze Tekn Univ Dept Comp Engn Kocaeli Turkey

ISBN: (纸本)9781728172064

The development of technology caused a significant increase in the use of images in forensic cases. It is common that manipulated images are presented as evidence in courts which requires an authenticity check. In this study, we analyze splicing forgeries where the manipulations are obtained by combining different images. The proposed method divides the original images and the manipulated images into small sub-blocks. After the distinctive statistical information of the images is extracted using ELA (Error Level Analysis), the necessary discrimitative information is learned using a convolutional neural network. The method was tested on the CASIA dataset and is shown to perform comparable or better than some existing methods.

关键词： Forensic image Analysis image Splicing Error Level Analysis Convolutional Neural Networks

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