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检索条件"任意字段=Conference on Image Reconstruction from Incomplete Data V"

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Transfer Learning for image Classification with incomplete Multiple Sources

Transfer Learning for Image Classification with Incomplete M...

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International Joint conference on Neural Networks

作者： Zhengming Ding Ming Shao Yun Fu Department of Electrical and Computer Engineering Northeastern University Boston MA 02115 USA College of Computer and Information Science Northeastern University Boston MA 02115 USA

ISBN: (纸本)9781509006212

Transfer learning plays a powerful role in mitigating the discrepancy between test data (target) and auxiliary data (source). There is often the case that multiple sources are available in transfer learning. However, naively combining multiple sources does not lead to valid results, since they will introduce negative transfer as well. Furthermore, each single source from multiple sources may not cover all the labels of the target data. In this paper, we consider the problem that how to better utilize multiple incomplete sources for effective knowledge transfer. To this end, we propose a Bi-directional Low-Rank Transfer learning framework (BLRT). First, we adapt the conventional low-rank transfer learning to multiple sources knowledge transfer scenario. Second, an iterative structure learning is proposed to better use prior knowledge for transfer learning coefficients matrix. Third, a cross-source regularizer is added to couple the same labels from multiple incomplete sources, so that they could jointly compensate missing data from other sources. Experimental results on three groups of databases including face and object images have demonstrated that our method can successfully inherit knowledge from incomplete multiple sources and adapt to the target data successfully.

关键词： Knowledge transfer Silicon Bidirectional control image reconstruction Computers Computer vision Computational modeling

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High fidelity imaging of geosynchronous satellites with the MROI 5

High fidelity imaging of geosynchronous satellites with the ...

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conference on Optical and Infrared Interferometry and Imaging v

作者： Young, John Haniff, Christopher Buscher, David Creech-Eakman, Michelle Payne, Ifan Univ Cambridge Cavendish Lab JJ Thomson Ave Cambridge CB3 0HE England New Mexico Inst Min & Technol Magdalena Ridge Observ Socorro NM 87801 USA

ISBN: (纸本)9781510601932

Interferometry currently provides the only practicable way to image satellites in Geosynchronous Earth Orbit (GEO) with sub-meter spatial resolution. The Magdalena Ridge Observatory Interferometer (MROI) is being funded by the US Air Force Research Laboratory to demonstrate the 9.5 magnitude sensitivity (at 2.2 mu m wavelength) and baseline-bootstrapping capability that will be needed to realize a useful turn-key GEO imaging capability. This program will utilize the central three telescopes of the MROI and will aim to validate routine acquisition of fringe data on faint well-resolved targets. In parallel with this effort, the University of Cambridge are investigating the spatial resolution and imaging fidelity that can be achieved with different numbers of array elements. We present preliminary simulations of snapshot GEO satellite imaging with the MROI. Our results indicate that faithful imaging of the main satellite components can be obtained with as few as 7 unit telescopes, and that increasing the number of telescopes to 10 improves the effective spatial resolution from 0.75 meter to 0.5 meter and enables imaging of more complex targets.

关键词： optical interferometry image reconstruction geosynchronous satellite

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Temporal Enhancement of Two-Dimensional Color Doppler Echocardiography

Temporal Enhancement of Two-Dimensional Color Doppler Echoca...

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conference on Medical Imaging - image Processing

作者： Terentjev, Alexey B. Settlemier, Scott H. Perrin, Douglas P. del Nido, Pedro J. Shturts, Igor v. vasilyev, Nikolay v. Peter Great St Petersburg Polytech Univ Polytechnicheskaya 29 St Petersburg Russia Philips Healthcare 3000 Minuteman Rd Andover MA USA Harvard Med Sch Boston Childrens Hosp 300 Longwood Ave Boston MA USA

ISBN: (纸本)9781510600195

Two-dimensional color Doppler echocardiography is widely used for assessing blood flow inside the heart and blood vessels. Currently, frame acquisition time for this method varies from tens to hundreds of milliseconds, depending on Doppler sector parameters. This leads to low frame rates of resulting video sequences equal to tens of Hz, which is insufficient for some diagnostic purposes, especially in pediatrics. In this paper, we present a new approach for reconstruction of 2D color Doppler cardiac images, which results in the frame rate being increased to hundreds of Hz. This approach relies on a modified method of frame reordering originally applied to real-time 3D echocardiography. There are no previous publications describing application of this method to 2D Color Doppler data. The approach has been tested on several in-vivo cardiac 2D color Doppler datasets with approximate duration of 30 sec and native frame rate of 15 Hz. The resulting image sequences had equivalent frame rates to 500Hz.

关键词： color doppler Ultrasound temporal resolution enhancement

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Accurate image reconstruction of Few-view Ptychrography X-ray Computed Tomography

Accurate Image Reconstruction of Few-view Ptychrography X-ra...

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IEEE International conference on Information and Automation

作者： J. Fu J. Z.Wang School of Mechanical Engineering and Automation Beijing University of Aeronautics and Astronautics No. 37 Xueyuan Road Beijing 100191 China

ISBN: (纸本)9781509041039

Ptychrograhic x-ray computed tomography (PX-CT) is a novel analysis tool in materials science. It is based on the coherent diffraction and enables the non-destructive observation of the internal structures of the specimen at the nano-scale. Few-view PX-CT can permit rapid scanning with a reduced amount of dataset and has a great potential on material performance evaluation and non-destructive testing. There are often challenges for the image reconstruction in few-view PX-CT because of the insufficient projection data. In this paper, an accurate reconstruction method is developed and investigated for few-view PX-CT. It is based on an algebraic iteration reconstruction technique, which minimizes the image total variation and permits accurate tomographic imaging with less data. The numerical results demonstrate that the presented method can handle incomplete projection data in few-view PX-CT. It will be helpful for PX-CT applications in materials science.

关键词： X-ray coherent diffraction imaging Computed tomography Ptychrography Few-view imaging image reconstruction

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Sparse Recovery Algorithms Based on Dictionary Learning for MR image reconstruction

Sparse Recovery Algorithms Based on Dictionary Learning for ...

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IEEE International conference on Wireless Communications, Signal Processing and Networking (WiSPNET)

作者： Manimala, M. v. R. Naidu, C. D. Giriprasad, M. N. Maturi Venkata Subba Rao Engn Coll Hyderabad Andhra Pradesh India VNR Vignana Jyothi Inst Engn & Tech Hyderabad Andhra Pradesh India Jawaharlal Nehru Technol Univ Anantapur Anantapuramu India

ISBN: (纸本)9781467393379

Compressive Sensing is the present sampling paradigm which acquires only useful data, and hence reduces the acquisition time, eliminates the necessity of fast analog to digital converters and data compression stage, prior to transmission. Challenge lies in recovering the data from these few samples, which are far below Nyquist rate. Compressive sensing accomplishes this, provided the signal is sparse in either signal domain or transform domain. It is particularly useful to employ compressive sensing for Magnetic Resonance (MR) imaging as these images can be sparsely represented in transform domain. A patch based dictionary can reconstruct images with great accuracy even at very high undersampling rates, as it exploits patch based sparsity. This paper investigates the performance of three different recovery strategies - Orthogonal Matching Pursuit, Compressive Sampling Matching Pursuit and Basis Pursuit on different MR images when integrated with an adaptive patch based dictionary.

关键词： Compressive Sensing Orthogonal Matching Pursuit Compressive Sampling Matching Pursuit Basis Pursuit

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A Field Model for Repairing 3D Shapes

A Field Model for Repairing 3D Shapes

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IEEE conference on Computer vision and Pattern Recognition

作者： Duc Thanh Nguyen Binh-Son Hua Minh-Khoi Tran Quang-Hieu Pham Sai-Kit Yeung Sch. of Inf. Technol. Deakin Univ. Melbourne VIC Australia Singapore Univ. of Technol. & Design Singapore Singapore

ISBN: (纸本)9781467388528

This paper proposes a field model for repairing 3D shapes constructed from multi-view RGB data. Specifically, we represent a 3D shape in a Markov random field (MRF) in which the geometric information is encoded by random binary variables and the appearance information is retrieved from a set of RGB images captured at multiple viewpoints. The local priors in the MRF model capture the local structures of object shapes and are learnt from 3D shape templates using a convolutional deep belief network. Repairing a 3D shape is formulated as the maximum a posteriori (MAP) estimation in the corresponding MRF. variational mean field approximation technique is adopted for the MAP estimation. The proposed method was evaluated on both artificial data and real data obtained from reconstruction of practical scenes. Experimental results have shown the robustness and efficiency of the proposed method in repairing noisy and incomplete 3D shapes.

关键词： Three-dimensional displays Shape Solid modeling Computational modeling Two dimensional displays image reconstruction Surface reconstruction three-dimensional displays entity modeling field simulation Surface reconstruction Computational modeling image reconstruction Wound Healing 3D shape Repair Bayesian networks

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CONvEX CLUSTERING AND RECOvERY OF PARTIALLY OBSERvED data

CONVEX CLUSTERING AND RECOVERY OF PARTIALLY OBSERVED DATA

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IEEE International conference on image Processing

作者： Sunrita Poddar Mathews Jacob Department of Electrical and Computer Engineering The University of Iowa

ISBN: (纸本)9781467399623

We propose a convex clustering and reconstruction algorithm for data with missing entries. The algorithm uses a similarity measure between every pair of points to cluster and recover the data. The cluster centres can be recovered reliably when the ground-truth similarity matrix is available. Moreover, the similarity matrix can also be reliably estimated from the partially observed data, when the clusters are well-separated and the coherence of the difference between points from different clusters is low. The algorithm performs well using the estimated similarity matrix on a simulated dataset. The method is also successful in reconstructing images from under-sampled Fourier data.

关键词： Clustering Missing data image reconstruction Matrix Completion missing data image reconstruction similarity matrix Cluster Analysis similar measure reconstruction algorithm

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Sparse sensing for resource-constrained depth reconstruction

Sparse sensing for resource-constrained depth reconstruction

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2016 IEEE/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Fangchang Ma Luca Carlone Ulas Ayaz Sertac Karaman Laboratory for Information & Decision Systems Massachusetts Institute of Technology Cambridge MA USA

ISBN: (纸本)9781509037636

We address the following question: is it possible to reconstruct the geometry of an unknown environment using sparse and incomplete depth measurements? This problem is relevant for a resource-constrained robot that has to navigate and map an environment, but does not have enough on-board power or payload to carry a traditional depth sensor (e.g., a 3D lidar) and can only acquire few (point-wise) depth measurements. In general, reconstruction from incomplete data is not possible, but when the robot operates in man-made environments, the depth exhibits some regularity (e.g., many planar surfaces with few edges); we leverage this regularity to infer depth from incomplete measurements. Our formulation bridges robotic perception with the compressive sensing literature in signal processing. We exploit this connection to provide formal results on exact depth recovery in 2D and 3D problems. Taking advantage of our specific sensing modality, we also prove novel and more powerful results to completely characterize the geometry of the signals that we can reconstruct. Our results directly translate to practical algorithms for depth reconstruction; these algorithms are simple (they reduce to solving a linear program), and robust to noise. We test our algorithms on real and simulated data, and show that they enable accurate depth reconstruction from a handful of measurements, and perform well even when the assumption of structured environment is violated.

关键词： Robot sensing systems image reconstruction Transmission line matrix methods Three-dimensional displays Two dimensional displays Sparse matrices

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variational Bayesian image reconstruction with an Uncertainty Model for Measurement Localization

Variational Bayesian Image Reconstruction with an Uncertaint...

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European Signal Processing conference

作者： Filip Sroubek Jindrich Soukup Barbara Zitova Institute of Information Theory and Automation Czech Academy of Sciences

ISBN: (纸本)9781509018918

We propose a general data acquisition model with volatile random displacement of measured samples. Discrepancies between recorded and true positions of the original data is due to the nature of measured data or the acquisition device itself. A reconstruction method based on the variational Bayesian inference is proposed, which estimates the original data from samples acquired with the acquisition model, and its relation to Jensen's inequality is discussed. A model variant of 2D image reconstruction is analyzed in detail. Further, we outline a relation between the proposed method and the classic deconvolution problem, and illustrate superiority of the variational Bayesian approach in the case of small number of samples.

关键词： Bayesian inference RAW data image reconstruction reconstruction algorithm two dimentional images observational data Reconstructive Surgical Procedures TRUE POSITIONS

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Custom Field-of-view optofluidic imaging by synthetic digital holography 2

Custom Field-of-View optofluidic imaging by synthetic digita...

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conference on Optics and Biophotonics in Low-Resource Settings II

作者： Bianco, v. Paturzo, M. Marchesano, v. Ferraro, P. Italian Natl Res Council ISASI CNR Inst Appl Sci & Intelligent Syst Via Campi Flegrei 34 I-80078 Pozzuoli NA Italy

ISBN: (纸本)9781628419337

A recently proposed imaging modality, named Space-Time Scanning Interferometry (STSI), exploits object scanning to synthesize interferograms mapped in a hybrid space-time domain. A single linear sensor array is sufficient to create such interferograms with unlimited Field of view (Fov) along the scanning direction. If properly selected, three detector lines allows to recovery the phase information by Phase Shifting (PS) interferometry algorithms. Here we show the application of the STSI method to microfluidic imaging of biological samples, where the required phase shift between interferograms is intrinsically offered due to the sample movement, i.e. scanning does not need to be provided. Besides, out-of-focus recordings are performed using a single line detector, in order to synthesize an unlimited Fov Space-Time Digital Hologram (STDH). As conventional DH, a STDH yields full-field, quantitative, flexible focusing imaging. In addition, in a STDH the Fov is customizable in one direction, so that the desired magnification of a large size sample can be set, still being able to image this entirely. Besides, STDH allows to capture data from a multitude of flowing samples and to process the corresponding information in a single operation, thus avoiding hologram stitching. Experiments have been carried out to demonstrate the capability of STDH provide very high-throughput imaging of objects flowing in a liquid volume using a linear sensor array easily embeddable onboard LoC platforms.

关键词： (100.3175) Interferometric imaging (090.1995) Digital holography (050.5080) Phase shift (100.5070) Phase retrieval (180.5810) Scanning microscopy (170.0110) Imaging systems (110.3010) image reconstruction techniques

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