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International conference on Communication and Signal Processing

作者： Bhumika Makwana Manojkumar Gupra Hiren Mewada Charotar Institute of Technology Changa Ta Anand India. the institute of Plasma Research at Ghandhinagr Gujarat India. the Charotar Institute of Technology Changa Ta Anand India.

ISBN: (纸本)9781467348652

Nuclear fusion will be one of the promising ways to generate electrical power in the next century. This source should be a safe and clean source of electricity for which fuels are abundant. Most current research into nuclear fusion is directed towards magnetic confinement of extremely hot plasma in a device called Tokamak. Before Fusion reactors become operational, still many problems need to be solved, both in fundamental and technological problems from which plasma wall interaction and magnetic field fluctuation are some of the major ones. To address these problems Aditya Tokamak in the Institute for plasma research is equipped with several high resolution diagnostics. One of them visible light Tomography setup is described in this paper. This system is suitable for studying fluctuations and to determine the local density of species that give rise to radiation which is important for plasma wall interaction. This paper describes use of tomography diagnostic to resolve the local emission of visible light in plasma from line integrated measurements in one poloidal cross section. Multiplicative algebraic reconstruction technique is developed to reconstruct the H_a emission profile. A code is written in MATLAB for extracting the informative projection data from the raw data file obtained from Aditya Tokamak. MART code written in MATLAB has flexibility to change the various parameters like geometrical parameters, relaxation factor and pixel size.

关键词： image reconstruction Multiplicative algebraic reconstruction technique Plasma diagnostics Tomography

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Transmission-less brain TOF PET imaging using MLACF

Transmission-less brain TOF PET imaging using MLACF

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： v. Y. Panin H. Bal M. Defrise C. Hayden M.E. Casey Siemens Healthcare Molecular Imaging Knoxville TN USA Department of Nuclear Medicine University Hospital UZ-VUB Brussels Belgium

Recent theoretical investigations concluded that both activity and attenuation distributions can be obtained from PET emission TOF data alone up to the knowledge of the sinogram scaling parameter. Attenuation is recovered in the form of attenuation factors. This allows for development of a fast iterative algorithm, referred to as ML-ACF, where iterative reconstruction is performed with respect to the activity image. Attenuation is a by-product of the estimation of activity. In general, the estimation of attenuation from PET data alone is not currently necessary due to the diagnostic value of PET-CT scanning. Nevertheless, it is desirable to limit patient CT doses. CT-less PET brain imaging is currently being pursued by manufacturers. This transmission-less brain imaging uses the assumption of a uniform attenuation map. The boundary can be derived from the processing of the activity image and reconstructed without attenuation correction. In this paper we enrich this approach by exploiting this uniform attenuation map as a starting condition for MLACF iterations. The scatter correction and the proper scaling of the activity image are based on this information. The head, especially the slices close to the top of the head, is relatively small compared to account the current scanner's time resolution. Therefore it is not obvious a priori that TOF information can be exploited for brain imaging. In this paper we demonstrate that the brain image attenuation can be reconstructed by exploiting just two TOF bins, with the TOF bin size currently used on the Siemens mCT scanner. Preliminary investigations showed that the head activity image will be improved by MLACF compared to the standard CT-less processing based on a uniform attenuation.

关键词： Attenuation image reconstruction Computed tomography Estimation Head Brain

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Handling Noise in Single image Deblurring using Directional Filters

Handling Noise in Single Image Deblurring using Directional ...

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IEEE conference on Computer vision and Pattern Recognition

作者： Lin Zhong Sunghyun Cho Dimitris Metaxas Sylvain Paris Jue Wang Rutgers University Adobe Research

ISBN: (纸本)9781467364102

State-of-the-art single image deblurring techniques are sensitive to image noise. Even a small amount of noise, which is inevitable in low-light conditions, can degrade the quality of blur kernel estimation dramatically. The recent approach of Tai and Lin [17] tries to iteratively denoise and deblur a blurry and noisy image. However, as we show in this work, directly applying image denoising methods often partially damages the blur information that is extracted from the input image, leading to biased kernel estimation. We propose a new method for handling noise in blind image deconvolution based on new theoretical and practical insights. Our key observation is that applying a directional low-pass filter to the input image greatly reduces the noise level, while preserving the blur information in the orthogonal direction to the filter. Based on this observation, our method applies a series of directional filters at different orientations to the input image, and estimates an accurate Radon transform of the blur kernel from each filtered image. Finally, we reconstruct the blur kernel using inverse Radon transform. Experimental results on synthetic and real data show that our algorithm achieves higher quality results than previous approaches on blurry and noisy images.~1

关键词： blurry noisy image estimates Noisy image image reconstruction directional filter Noise Radon transforms image images imagery (Psychotherapy) Seeds Noise levels

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LEARNING A TREE-STRUCTURED DICTIONARY FOR EFFICIENT image REPRESENTATION WITH ADAPTIvE SPARSE CODING

LEARNING A TREE-STRUCTURED DICTIONARY FOR EFFICIENT IMAGE RE...

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IEEE International conference on Acoustics, Speech and Signal Processing

作者： Jeremy Aghaei Mazaheri Christine Guillemot Claude habit INRIA Rennes Campus universitaire de Beaulieu 35042 Rennes Cedex France

ISBN: (纸本)9781479903573

We introduce a new method, called Tree K-SvD, to learn a tree-structured dictionary for sparse representations, as well as a new adaptive sparse coding method, in a context of image compression. Each dictionary at a level in the tree is learned from residuals from the previous level with the K-SvD method. The tree-structured dictionary allows efficient search of the atoms along the tree as well as efficient coding of their indices. Besides, it is scalable in the sense that it can be used, once learned, for several sparsity constraints. We show experimentally on face images that, for a high sparsity, Tree K-SvD offers better rate-distortion performances than state-of-the-art "flat" dictionaries learned by K-SvD or Sparse K-SvD, or than the predetermined overcomplete DCT dictionary. We also show that our adaptive sparse coding method, used on a tree-structured dictionary to adapt the sparsity per level, improves the quality of reconstruction.

关键词： Dictionary learning tree-structured dictionary sparse coding sparse representations image coding Sparse coding Dictionaries as Topic image coding sparse representation image compression data compression Trees images

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Bayesian image and pattern reconstruction from incomplete and noisy data

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Pattern Recognition and image Analysis 2012年第1期22卷 99-107页

作者： vengrinovich, v.L. Institute of Applied Physics National Academy of Sciences Minsk Belarus

A Bayesian iterative method can be the basis for a wide range of technologies in the field of pattern recognition and image reconstruction. It involves finding the most probable solutions for images or patterns, if functionals describing the likelihood function and a priori information, respectively, are already known. The article describes the basic principles and recent advances in the development of BIM and its applications in various fields, mainly in tomography and restoration of functions from incomplete and noisy data. © 2012 Pleiades Publishing, Ltd.

关键词： Restoration

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Learning Classifiers from Chains of Multiple Interlinked RDF data Stores

Learning Classifiers from Chains of Multiple Interlinked RDF...

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IEEE International conference on Services Economics (SE)

作者： Harris T. Lin vasant Honavar Department of Computer Science Iowa State University Ames IA USA

The emergence of many interlinked, physically distributed, and autonomously maintained RDF stores offers unprecedented opportunities for predictive modeling and knowledge discovery from such data. However existing machine learning approaches are limited in their applicability because it is neither desirable nor feasible to gather all of the data in a centralized location for analysis due to access, memory, bandwidth, computational restrictions, and sometimes privacy and confidentiality constraints. Against this background, we consider the problem of learning predictive models from multiple interlinked RDF stores. Specifically we: (i) introduce statistical query based formulations of several representative algorithms for learning classifiers from RDF data, (ii) introduce a distributed learning framework to learn classifiers from multiple interlinked RDF stores that form a chain, (iii) identify three special cases of RDF data fragmentation and describe effective strategies for learning predictive models in each case, (iv) consider a novel application of a matrix reconstruction technique from the field of Computerized Tomography [1] to approximate the statistics needed by the learning algorithm from projections using count queries, thus dramatically reducing the amount of information transmitted from the remote data sources to the learner, and (v) report results of experiments with a real-world social network data set (***), which demonstrate the feasibility of the proposed approach.

关键词： Resource description framework Distributed databases Predictive models vectors image reconstruction Computed tomography Subspace constraints

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Dense Object reconstruction with Semantic Priors

Dense Object Reconstruction with Semantic Priors

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IEEE conference on Computer vision and Pattern Recognition

作者： Sid Yingze Bao Manmohan Chandraker Yuanqing Lin Silvio Savarese University of Michigan at Ann Arbor NEC Labs America

ISBN: (纸本)9781467364102

We present a dense reconstruction approach that overcomes the drawbacks of traditional multiview stereo by incorporating semantic information in the form of learned category-level shape priors and object detection. Given training data comprised of 3D scans and images of objects from various viewpoints, we learn a prior comprised of a mean shape and a set of weighted anchor points. The former captures the commonality of shapes across the category, while the latter encodes similarities between instances in the form of appearance and spatial consistency. We propose robust algorithms to match anchor points across instances that enable learning a mean shape for the category, even with large shape variations across instances. We model the shape of an object instance as a warped version of the category mean, along with instance-specific details. Given multiple images of an unseen instance, we collate information from 2D object detectors to align the structure from motion point cloud with the mean shape, which is subsequently warped and refined to approach the actual shape. Extensive experiments demonstrate that our model is general enough to learn semantic priors for different object categories, yet powerful enough to reconstruct individual shapes with large variations. Qualitative and quantitative evaluations show that our framework can produce more accurate reconstructions than alternative state-of-the-art multiview stereo systems.

关键词： Shape Three-dimensional displays image reconstruction Semantics Training Silicon Object detection

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Robust Object Co-Detection

Robust Object Co-Detection

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IEEE conference on Computer vision and Pattern Recognition

作者： Xin Guo Dong Liu Brendan Jou Mojun Zhu Anni Cai Shih-Fu Chang Beijing University of Posts and Telecommunications Dept. of Electrical Engineering Columbia University

ISBN: (纸本)9781467364102

Object co-detection aims at simultaneous detection of objects of the same category from a pool of related images by exploiting consistent visual patterns present in candidate objects in the images. The related image set may contain a mixture of annotated objects and candidate objects generated by automatic detectors. Co-detection differs from the conventional object detection paradigm in which detection over each test image is determined one-by-one independently without taking advantage of common patterns in the data pool. In this paper, we propose a novel, robust approach to dramatically enhance co-detection by extracting a shared low-rank representation of the object instances in multiple feature spaces. The idea is analogous to that of the well-known Robust PCA [28], but has not been explored in object co-detection so far. The representation is based on a linear reconstruction over the entire data set and the low-rank approach enables effective removal of noisy and outlier samples. The extracted low-rank representation can be used to detect the target objects by spectral clustering. Extensive experiments over diverse benchmark datasets demonstrate consistent and significant performance gains of the proposed method over the state-of-the-art object co-detection method and the generic object detection methods without co-detection formulations.

关键词： Object detection dataset image set candidate species Noise images

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Discriminative Non-blind Deblurring

Discriminative Non-blind Deblurring

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IEEE conference on Computer vision and Pattern Recognition

作者： Uwe Schmidt Carsten Rother Sebastian Nowozin Jeremy Jancsary Stefan Roth Department of Computer Science TU Darmstadt Microsoft Research Cambridge

ISBN: (纸本)9781467364102

Non-blind deblurring is an integral component of blind approaches for removing image blur due to camera shake. Even though learning-based deblurring methods exist, they have been limited to the generative case and are computationally expensive. To this date, manually-defined models are thus most widely used, though limiting the attained restoration quality. We address this gap by proposing a discriminative approach for non-blind deblurring. One key challenge is that the blur kernel in use at test time is not known in advance. To address this, we analyze existing approaches that use half-quadratic regularization. from this analysis, we derive a discriminative model cascade for image deblurring. Our cascade model consists of a Gaussian CRF at each stage, based on the recently introduced regression tree fields. We train our model by loss minimization and use synthetically generated blur kernels to generate training data. Our experiments show that the proposed approach is efficient and yields state-of-the-art restoration quality on images corrupted with synthetic and real blur.

关键词： generative case computationally expensive real blur deblurring camera shake image blur image reconstruction TESTING TIME integral component

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LEARNING OvERCOMPLETE SPARSIFYING TRANSFORMS FOR SIGNAL PROCESSING

LEARNING OVERCOMPLETE SPARSIFYING TRANSFORMS FOR SIGNAL PROC...

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IEEE International conference on Acoustics, Speech and Signal Processing

作者： Saiprasad Ravishankar Yoram Bresler Department of Electrical and Computer Engineering and the Coordinated Science Laboratory University of Illinois Urbana-Champaign IL 61801 USA

ISBN: (纸本)9781479903573

Adaptive sparse representations have been very popular in numerous applications in recent years. The learning of synthesis sparsifying dictionaries has particularly received much attention, and such adaptive dictionaries have been shown to be useful in applications such as image denoising, and magnetic resonance image reconstruction. In this work, we focus on the alternative sparsifying transform model, for which sparse coding is cheap and exact, and study the learning of tall or overcomplete sparsifying transforms from data. We propose various penalties that control the sparsifying ability, condition number, and incoherence of the learnt transforms. Our alternating algorithm for transform learning converges empirically, and significantly improves the quality of the learnt transform over the iterations. We present examples demonstrating the promising performance of adaptive overcomplete transforms over adaptive overcomplete synthesis dictionaries learnt using K-SvD, in the application of image denoising.

关键词： Sparsifying transform learning Sparse representations dictionary learning Overcomplete representations signal processing Transform sparse representation Dictionaries as Topic image denoising Learning incoherence Self tuning Sparse coding penalty iterative methods

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