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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821-830 订阅

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A Bayesian Perspective on the Deep image Prior 32

A Bayesian Perspective on the Deep Image Prior

引用

32nd IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Cheng, Zezhou Gadelha, Matheus Maji, Subhransu Sheldon, Daniel Univ Massachusetts Amherst MA 01003 USA

ISBN: (纸本)9781728132938

The deep image prior was recently introduced as a prior for natural images. It represents images as the output of a convolutional network with random inputs. For inference, gradient descent is performed to adjust network parameters to make the output match observations. This approach yields good performance on a range of image reconstruction tasks. We show that the deep image prior is asymptotically equivalent to a stationary Gaussian process prior in the limit as the number of channels in each layer of the network goes to infinity, and derive the corresponding kernel. This informs a Bayesian approach to inference. We show that by conducting posterior inference using stochastic gradient Langevin dynamics we avoid the need for early stopping, which is a drawback of the current approach, and improve results for denoising and impainting tasks. We illustrate these intuitions on a number of 1D and 2D signal reconstruction tasks.

关键词： Deep Learning Low-level Vision Optimization methods statistical Learning

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GlobalSIP 2019 - 7th IEEE Global conference on Signal and Information processing, Proceedings

GlobalSIP 2019 - 7th IEEE Global Conference on Signal and In...

引用

7th IEEE Global conference on Signal and Information processing, GlobalSIP 2019

ISBN: (纸本)9781728127231

The proceedings contain 206 papers. The topics discussed include: sampling signals on meet/join lattices;deep learning methods for image segmentation containing translucent overlapped objects;low-correlation low-cost stochastic number generators for stochastic computing;the onset of parietal alpha- and beta- band oscillations caused by an initial video delay;new filtering approaches to improve the classification capability of resting-state fMRI transfer functions;an accurate evaluation of MSD log-likelihood and its application in human action recognition;computer-generated holography using a digital signal processor;and fixed-point accuracy analysis of 2D FFT for the creation of computer generated holograms.

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Vision Based Liquid Level Detection and Bubble Area Segmentation in Liquor Distillation

Vision Based Liquid Level Detection and Bubble Area Segmenta...

引用

IEEE International conference on Imaging Systems and Techniques (IST) . IEEE International School on Imaging

作者： Ma, Haocheng Peng, Lihui Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Tsinghua Univ Tsinghua Univ LuZhou LaoJiao Joint Res Ctr Intell Beijing 100084 Peoples R China

ISBN: (纸本)9781728138688

In order to automatically measure the flow rate, alcohol strength and roughly determine the quality of distilled liquor, a system was developed with two transparent standard containers, two load cell sensors, and a camera. This paper presents the imaging part of the measurement system, including the optical path as well as a set of image processing methods to detect the position of the liquid level and calculate the amount of the bubbles on the top of the liquid. Four ArUco markers are used to locate the containers in the captured image and the containers are cropped out. Then the liquid level is detected and the area of bubbles are segmented using statistical information of the pixels in each container. According to the test results, the proposed methods archive accurate and real-time detection on an embedded processor and is robust to the change of the illumination, flowrate, liquid level and camera position.

关键词： liquid level detection embedded image processing marker location segmentation

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Approach using Transforming Structural Data into image for Detection of Malicious MS-DOC Files based on Deep Learning Models

Approach using Transforming Structural Data into Image for D...

引用

Annual Summit and conference of the Asia-Pacific-Signal-and-Information-processing-Association (APSIPA ASC)

作者： Yang, Shaojie Chen, Wenbo Li, Shanxi Xu, Qingxiang Lanzhou Univ Lanzhou Peoples R China

ISBN: (纸本)9781728132488

Malicious MS-DOC file has a long history in cybersecurity and has rapid growth with tremendous appearance of advanced persistent threat (APT) attacks. Due to its obfuscation and complexities, regular detection methods are not ideal, and the specific detection methods are limited, either. This paper presents a new approach for malware detection of MS-DOC files. Inspired by analysis of MS-DOC files and tremendous success made by convolutional neural network (CNN) in the field of feature identification, especially image identification, a new approach including data extraction and conversion is designed to identify MS-DOC malicious files and benign files. Based on three CNN models, experiment results show that the accuracy rate of detection for test dataset reaches 94.09%, and in simulated zero-day malware detection experiment, the average accuracy rate reaches 94.70% The approach proves the feasibility of MS-DOC malicious file detection based on convolutional neural network and proposes a new idea to detect zero-day MS-DOC malware.

关键词： statistical tests

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An Autoencoder based Technique for DNA Microarray image Denoising

An Autoencoder based Technique for DNA Microarray Image Deno...

引用

International conference on Communications and Signal processing

作者： Arya Mohandas Steffy Maria Joseph P. S. Sathidevi ECE department NIT Calicut India

ISBN: (数字)9781728149882

ISBN: (纸本)9781728149899

DNA Microarray is one of the proven tools for genomics. It can be used to detect all the gene expression variations between two different types of cells in a single experiment. The microarray image is a rectangular grid with many subgrids, and each subgrid has organized gene samples called spots, the number of which varies with the manufacturer. The spot intensity information is the most important parameter for gene expression analysis, disease diagnosis, and drug discovery. But, obtaining the spot intensity of DNA microarray images (MAI) is highly challenging as the image is of low contrast and noisy. The various steps involved in obtaining the intensity of the spot are image enhancement, Gridding, Spot segmentation and Extraction of intensity. Out of these steps, image enhancement is of utmost importance as it can affect the accuracy of the extraction of spot intensity. In this paper, we propose an autoencoder based image denoising for enhancing the DNA MAI. It is a stochastic extension to classic autoencoder. The method is tested on SIB and Derisi datasets. The experimental results indicate that there is a considerable reduction in noise when compared with other recent related methods.

关键词： Noise reduction DNA Gene expression image segmentation Noise measurement image denoising image enhancement

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stochastic normalizing flows 20

Stochastic normalizing flows

引用

Proceedings of the 34th International conference on Neural Information processing Systems

作者： Hao Wu Jonas Köhler Frank Noé Tongji University Shanghai P.R. China FU Berlin Berlin Germany

ISBN: (纸本)9781713829546

The sampling of probability distributions specified up to a normalization constant is an important problem in both machine learning and statistical mechanics. While classical stochastic sampling methods such as Markov Chain Monte Carlo (MCMC) or Langevin Dynamics (LD) can suffer from slow mixing times there is a growing interest in using normalizing flows in order to learn the transformation of a simple prior distribution to the given target distribution. Here we propose a generalized and combined approach to sample target densities: stochastic Normalizing Flows (SNF) - an arbitrary sequence of deterministic invertible functions and stochastic sampling blocks. We show that stochasticity overcomes expressivity limitations of normalizing flows resulting from the invertibility constraint, whereas trainable transformations between sampling steps improve efficiency of pure MCMC/LD along the flow. By invoking ideas from non-equilibrium statistical mechanics we derive an efficient training procedure by which both the sampler's and the flow's parameters can be optimized end-to-end, and by which we can compute exact importance weights without having to marginalize out the randomness of the stochastic blocks. We illustrate the representational power, sampling efficiency and asymptotic correctness of SNFs on several benchmarks including applications to sampling molecular systems in equilibrium.

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ISAR Imaging Based on Block Bayesian Compressed Sensing by Learning the Clustering Structure

ISAR Imaging Based on Block Bayesian Compressed Sensing by L...

引用

International conference of Signal processing and Intelligent Systems (ICSPIS)

作者： Iman Faramarzi Rahim Entezari Alijabbar Rashidi Faculty of Electrical and Computer Engineering Malek-e-Ashtar University of Technology Tehran Iran

ISBN: (数字)9781728186290

ISBN: (纸本)9781728186306

In conventional inverse synthetic aperture radar (ISAR) imaging methods based on compressed sensing (CS), some of the weak scatter points cannot be recovered completely with a short coherent processing interval (CPI). Therefore in this paper, according to the continuity pattern of the ISAR image and location of the dominant scatter points in some clusters, we present a high-resolution ISAR imaging method based on Block Bayesian Compressed Sensing to reconstruction of unknown Sparse Clustering Structure, which is named as BBCS-SCS. For this reason, we first use complex Gaussian prior distribution to model statistical dependencies between neighboring dominant scatterers. Then, a variational Bayesian (VB) inference has been designed and developed to learn the clustering structure of scatterers in the target scene. Simulation results show that the proposed algorithm has better performance in terms of image contrast, image entropy, and running time in comparison with other methods in strong noise.

关键词： Imaging Signal processing algorithms Clustering algorithms Inference algorithms Bayes methods Inverse synthetic aperture radar Compressed sensing

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Locality Preserving Projection Based Multiple Copy-Paste Forgery Detection

Locality Preserving Projection Based Multiple Copy-Paste For...

引用

IEEE Applied Signal processing conference (ASPCON)

作者： Anjali Diwan Anil K. Roy Suman K. Mitra DA-IICT Gandhinagar Gujarat India

ISBN: (数字)9781728168821

ISBN: (纸本)9781728168838

Digital images are the dominant media of information in the digital world and used to convey the desired information. It has been noticed that manipulated images are put to wrong use to create a false perception about the image or story in support. Copy-paste forgery is one of the most exploited image manipulation approaches. In recent years the research community has proposed many methods for the detection of such forgery. In this work, the copy-paste forgery has been detected and localized by the proposed scheme using locality preserving projection (LPP). As the processing level increases, changes in pixel intensity and statistical changes in image pixels increase, which reduces the accuracy of localization. Similarity preserving property of LPP is used for developing a multifaceted method for copy-paste forgery detection for images attacked with various post-processing.

关键词： Forgery Feature extraction Histograms Signal processing algorithms image color analysis Signal processing conferences

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stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers 33

Stochastic Shared Embeddings: Data-driven Regularization of ...

引用

33rd conference on Neural Information processing Systems (NeurIPS)

作者： Wu, Liwei Li, Shuqing Hsieh, Cho-Jui Sharpnack, James Univ Calif Davis Dept Stat Davis CA 95616 USA Univ Calif Davis Dept Comp Sci Davis CA 95616 USA Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90095 USA

In deep neural nets, lower level embedding layers account for a large portion of the total number of parameters. Tikhonov regularization, graph-based regularization, and hard parameter sharing are approaches that introduce explicit biases into training in a hope to reduce statistical complexity. Alternatively, we propose stochastic shared embeddings (SSE), a data-driven approach to regularizing embedding layers, which stochastically transitions between embeddings during stochastic gradient descent (SGD). Because SSE integrates seamlessly with existing SGD algorithms, it can be used with only minor modifications when training large scale neural networks. We develop two versions of SSE: SSE-Graph using knowledge graphs of embeddings;SSE-SE using no prior information. We provide theoretical guarantees for our method and show its empirical effectiveness on 6 distinct tasks, from simple neural networks with one hidden layer in recommender systems, to the transformer and BERT in natural languages. We find that when used along with widely-used regularization methods such as weight decay and dropout, our proposed SSE can further reduce overfitting, which often leads to more favorable generalization results.

关键词： Embeddings

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Orientation control and non-linear trajectory tracking of colloidal particles using microfluidics

Orientation control and non-linear trajectory tracking of co...

引用

2020 AIChE Annual Meeting

作者： Kumar, Dinesh Shenoy, Anish Richter, Channing M. Schroeder, Charles M. Department of Chemical and Biomolecular Engineering Department of Mechanical Science and Engineering University of Illinois at Urbana-Champaign United States Beckman Institute of Advanced Science and Technology

ISBN: (纸本)9780816911141

Research Interests - My Ph.D. thesis research focuses on: (i) developing new tools for trapping and manipulatingmicro and nanoparticles in free solution using only fluid flow, and (ii) understanding the physics and emergentproperties of vesicle suspensions using these control-based techniques. To this end, I have developed a millisecondtime nonlinear model predictive control algorithm with real-time image processing (written in C++, LabVIEW andMATLAB) to control the center-of-mass and orientation of single and multiple anisotropic particle using only fluid *** tandem, I study the dynamics of lipid vesicles using fluidic trapping tools and advanced statistical tools. My researchhas direct implications to the processing of food active ingredient encapsulants, and personal care products such asshampoos and detergents. Overall, my Ph.D. research entails a distinct combination of numerical, experimental andtheoretical/modeling skills involving fluid dynamics, process control techniques, and numerical methods that hasprovided me with the confidence to address a wide spectrum of challenges faced by modern society. Abstract Text - In this work, we study the shape dynamics of vesicles in precisely defined steady or time-dependent flow fields using aStokes trap. Vesicles are membrane-bound soft containers that are often used for triggered release or reagent deliveryand play an integral role in key biological processes such as molecular transport in cells. Giant unilamellar vesicles(GUVs) have been used as model systems to study the equilibrium and non-equilibrium dynamics of simplified cellsthat do not contain a cytoskeleton or polymerized membrane commonly found in cells. A grand challenge in the field ofmembrane transport lies in understanding how interfacial mechanics and fluid dynamics on the inside or outside of asoft vesicle contributes to the overall shape instabilities. Here, we study the dynamics of single floppy vesicles underlarge strain rates (~20 s ) using a

关键词： Numerical methods

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