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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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An Autoencoder based Technique for DNA Microarray image Denoising

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

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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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Locality Preserving Projection Based Multiple Copy-Paste Forgery Detection

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

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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 normalizing flows 20

Stochastic normalizing flows

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

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

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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...

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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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statistical Post-processing Approaches for Active Infrared Thermography: A Comparative Study

Statistical Post-processing Approaches for Active Infrared T...

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IEEE Annual Computing and Communication Workshop and conference (CCWC)

作者： Kirandeep Kaur Ravibabu Mulaveesala Indian Institute of Technology Ropar Punjab India

ISBN: (数字)9781665414906

ISBN: (纸本)9781665430586

Active infrared thermography is one of the favourable non-destructive testing and evaluation methods popularly being used for remote inspection of various materials/products/components/structures. It captures the temperature distribution over the test material for predefined thermal stimulus onto the surface, which is further processed to detect the sub-surface anomalies/defects hidden inside the test object. Various attempts have been made by several research groups to reveal the hidden finer subsurface features with improved sensitivity and resolution. Present work highlights a principal component analysis and its extension, robust principal component analysis to inspect for sub-surface flat-bottomed hole defects inside a mild steel sample. Further, the proposed data analysis approaches and their capabilities have been compared on the temporal thermal experimental sequence for a frequency modulated incident thermal stimulus. It is clear from the obtained results that principal component analysis outperforms the robust principal component analysis in providing the information regarding the hidden defect details lying deep inside the material with enhanced signal to noise ratio leading to increased temperature contrast over the detected sub-surface defects.

关键词： Photothermal effects Inspection Thermal analysis Steel Sparse matrices image reconstruction Principal component analysis

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SSGD: Sparsity-Promoting stochastic Gradient Descent Algorithm for Unbiased Dnn Pruning

SSGD: Sparsity-Promoting Stochastic Gradient Descent Algorit...

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IEEE International conference on Acoustics, Speech and Signal processing

作者： Ching-Hua Lee Igor Fedorov Bhaskar D. Rao Harinath Garudadri Department of ECE University of California San Diego ARM ML Research

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066315;9781509066322

While deep neural networks (DNNs) have achieved state-of-the-art results in many fields, they are typically over-parameterized. Parameter redundancy, in turn, leads to inefficiency. Sparse signal recovery (SSR) techniques, on the other hand, find compact solutions to over-complete linear problems. Therefore, a logical step is to draw the connection between SSR and DNNs. In this paper, we explore the application of iterative reweighting methods popular in SSR to learning efficient DNNs. By efficient, we mean sparse networks that require less computation and storage than the original, dense network. We propose a reweighting framework to learn sparse connections within a given architecture without biasing the optimization process, by utilizing the affine scaling transformation strategy. The resulting algorithm, referred to as Sparsity-promoting stochastic Gradient Descent (SSGD), has simple gradient-based updates which can be easily implemented in existing deep learning libraries. We demonstrate the sparsification ability of SSGD on image classification tasks and show that it outperforms existing methods on the MNIST and CIFAR-10 datasets.

关键词： compressed sensing gradient methods learning (artificial intelligence) neural nets

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Lung Cancer Analysis and Diagnosis by Coalition of Photo Metric and Quality Metric Parameters

Lung Cancer Analysis and Diagnosis by Coalition of Photo Met...

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Emerging Smart Computing and Informatics (ESCI), conference on

作者： Vaishnaw G. Kale Vandana B. Malode Dr. Vithalrao Vikhe Patil College of Engineering Ahmednagar MS India Jawaharlal Engineering College (J.N.E.C) Aurangabad MS India

ISBN: (数字)9781728152639

ISBN: (纸本)9781728152646

Lung cancer still seems to be a very common cause of death among the people all over the world. This paper proposes a method which is a coalition of two statistical methods Photo Metric and Quality Metric enacted for lung cancer analysis and diagnosis. Suggested methodology is established on the basis of identified statistical and mathematical parameters exploring the light variation and quality features of the lung image to carry out the evaluation for cancerous and non-cancerous types. The distinguishing Photo Metric parameters are Brightness, Luminance, Contrast and LC index, whereas the Quality Metric parameters are VIF, MSE, VSNR and PSNR. These eight identified parameters are applied one by one on the cancerous and noncancerous lung images using image processing technique using MATLAB to get an individual parameter statistical range through number of iterations in real time. If the test image falls in the particular calculated statistical range for cancerous lung images, then the test image is infected or else normal. But practically some of the values of the statistical range for cancerous and noncancerous lungs overlaps due to which the final decision is taken by an image classifier. The ANN is used as an image classifier to sort out this problem resulting in an efficient lung cancer diagnosis. The microscopic lung images are used to implement the proposed methodology. The performance analysis of each identified parameter is the heart of this paper. The authentication of the proposed methods is tested successfully using Standard Diagnostic Test.

关键词： Lung Cancer Measurement Brightness Standards Indexes statistical analysis

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A Comparative Evaluation of Jpeg Steganography 5

A Comparative Evaluation of Jpeg Steganography

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5th IEEE International conference on Signal processing, Computing and Control (ISPCC)

作者： Watni, Dipti Chawla, Sonal MCM DAV Coll Women Chandigarh 160036 India Panjab Univ Chandigarh 160014 India

ISBN: (纸本)9781728139883

With the dependency on internet and smart gadgets, security of data has become a major concern. Steganography is one of the methods adopted to enhance the security of data by hiding it behind some cover which can be any amongst text, image, audio or video. images are the most preferred medium to apply steganography as it contains large amount of redundant data. image steganography hides the data behind the image in a manner that alterations are hardly noticeable to human eye. images are available in different formats, out of which jpeg images have become the most popular image format due to its small size. These images undergo lossy compression resulting in small sized images, but hiding data behind these images is a challenge. Many algorithms have been suggested till date. This paper intends to give understanding and evolution of different techniques for jpeg steganography. It summarizes various algorithms and techniques used in the past and in present. This paper also analysis's these algorithms based on the basic parameters of image steganography: Undetectability, robustness and embedding capacity.

关键词： Steganography Jpeg Steganography statistical Restoration Adaptive Steganography Robust Steganography

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A case study on homogeneous and heterogeneous reservoir porous media reconstruction by using generative adversarial networks 10

A case study on homogeneous and heterogeneous reservoir poro...

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10th International conference on Applied Energy (ICAE)

作者： Liu, Siyan Zhong, Zhi Takbiri-Borujeni, Ali Kazemi, Mohammad Fu, Qinwen Yang, Yuhao Univ Kansas Dept Chem & Petr Engn Lawrence KS 66045 USA Univ Texas Austin Bur Econ Geol Austin TX 78713 USA West Virginia Univ Dept Petr & Nat Gas Engn Morgantown WV 26506 USA Slippery Rock Univ Dept Petr & Nat Gas Engn Slippery Rock PA 16057 USA

The advancing of modern X-ray computer tomography technology provides a powerful tool for us to illustrate the details inside the reservoir rock in three-dimensional space. Pore-scale rock characterization, modeling, and related fluid flow simulation can be challenging due to the high complexity of various rock samples. Conventional pore scale structure modeling methods such as various stochastic methods were developed for reservoir rock 3D microscopic structure reconstruction in order to generate representative realizations for numerical simulations and property upscaling approaches. In this work, generative adversarial networks (GANs) is used for generating the synthetic micro representations of porous rock by acquiring non-linear statistical information from the real 3D rock images in an unsupervised learning scheme. The related 3D image pre-processing, network training and adjusting as well as data post-processing procedures are addressed. The network prediction results from a homogeneous Berea sandstone and a heterogeneous Estaillades carbonate demonstrated the capability of GANs for high resolution porous rock image representations reconstruction, generated and real images are compared via various visualizations and inspections. The study also illustrated the importance of the training image preprocessing, which indicating the data augmentation techniques can be one of the promising improvements in terms of capturing the sparsely distributed features from heterogenous 3D images and reconstructing the synthetic realizations, meanwhile, the robustness of the model during training process is enhanced when limited real data is available. (C) 2019 The Authors. Published by Elsevier Ltd.

关键词： porous media image reconstruction generative adversarial networks neural networks

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