Results 1 - 10
of
17
Video Compressive Sensing Using Gaussian Mixture Models
"... A Gaussian mixture model (GMM) based algorithm is proposed for video reconstruction from temporally-compressed video measurements. The GMM is used to model spatio-temporal video patches, and the reconstruction can be efficiently computed based on analytic expressions. The GMM-based inversion method ..."
Abstract
-
Cited by 11 (5 self)
- Add to MetaCart
A Gaussian mixture model (GMM) based algorithm is proposed for video reconstruction from temporally-compressed video measurements. The GMM is used to model spatio-temporal video patches, and the reconstruction can be efficiently computed based on analytic expressions. The GMM-based inversion method benefits from online adaptive learning and parallel computation. We demonstrate the efficacy of the proposed inversion method with videos reconstructed from simulated compressive video measurements, and from a real compressive video camera. We also use the GMM as a tool to investigate adaptive video compressive sensing, i.e., adaptive rate of temporal compression.
1 Coded Hyperspectral Imaging and Blind Compressive Sensing
"... Blind compressive sensing (CS) is considered for reconstruction of hyperspectral data imaged by a coded aperture camera. The measurements are manifested as a superposition of the coded wavelengthdependent data, with the ambient three-dimensional hyperspectral datacube mapped to a two-dimensional mea ..."
Abstract
-
Cited by 10 (4 self)
- Add to MetaCart
(Show Context)
Blind compressive sensing (CS) is considered for reconstruction of hyperspectral data imaged by a coded aperture camera. The measurements are manifested as a superposition of the coded wavelengthdependent data, with the ambient three-dimensional hyperspectral datacube mapped to a two-dimensional measurement. The hyperspectral datacube is recovered using a Bayesian implementation of blind CS. Several demonstration experiments are presented, including measurements performed using a coded aperture snapshot spectral imager (CASSI) camera. The proposed approach is capable of efficiently reconstructing large hyperspectral datacubes. Comparisons are made between the proposed algorithm and other techniques employed in compressive sensing, dictionary learning and matrix factorization. Index Terms hyperspectral images, image reconstruction, projective transformation, dictionary learning, non-parametric Bayesian, Beta-Bernoulli model, coded aperture snapshot spectral imager (CASSI). I.
3d imaging spectroscopy for measuring hyperspectral patterns on solid objects
- ACM Trans. Graph
"... 3D hyperspectral pattern An example of measuring a 3D spectral pattern of UV fluorescence of a mineral ore (willemite & calcite) Radiometric measurements in 3D Hyperspectral texturesHyperspectral imaging Angles (0°~360°) Sp ec tru m ..."
Abstract
-
Cited by 8 (3 self)
- Add to MetaCart
3D hyperspectral pattern An example of measuring a 3D spectral pattern of UV fluorescence of a mineral ore (willemite & calcite) Radiometric measurements in 3D Hyperspectral texturesHyperspectral imaging Angles (0°~360°) Sp ec tru m
RECONSTRUCTING AND SEGMENTING HYPERSPECTRAL IMAGES FROM COMPRESSED MEASUREMENTS
"... A joint reconstruction and segmentation model for hyperspectral data obtained from a compressive measurement system is described. Although hyperspectral imaging (HSI) technology has incredible potential, its utility is currently limited because of the enormous quantity and complexity of the data it ..."
Abstract
-
Cited by 2 (0 self)
- Add to MetaCart
A joint reconstruction and segmentation model for hyperspectral data obtained from a compressive measurement system is described. Although hyperspectral imaging (HSI) technology has incredible potential, its utility is currently limited because of the enormous quantity and complexity of the data it gathers. Yet, often the scene to be reconstructed from the HSI data contains far less information, typically consisting of spectrally and spatially homogeneous segments that can be represented sparsely in an appropriate basis. Such vast informational redundancy thus implicitly contained in the HSI data warrants a compressed sensing (CS) strategy that acquires appropriately coded spectral-spatial data from which one can reconstruct the original image more efficiently while still enabling target identification procedures. A codedaperture snapshot spectral imager (CASSI) that collects compressed measurements is considered here, and a joint reconstruction and segmentation model for hyperspectral data obtained from CASSI compressive measurements is described. Promising test results on simulated and real data are reported. Index Terms — Hyperspectral images, compressive measurements, reconstruction, segmentation, classification, target identification.
1Compressive Sensing by Learning a Gaussian Mixture Model from Measurements
"... Abstract—Compressive sensing of signals drawn from a Gaus-sian mixture model (GMM) admits closed-form minimum mean squared error (MMSE) reconstruction from incomplete linear measurements. An accurate GMM signal model is usually not available a priori, because it is difficult to obtain training signa ..."
Abstract
-
Cited by 2 (2 self)
- Add to MetaCart
(Show Context)
Abstract—Compressive sensing of signals drawn from a Gaus-sian mixture model (GMM) admits closed-form minimum mean squared error (MMSE) reconstruction from incomplete linear measurements. An accurate GMM signal model is usually not available a priori, because it is difficult to obtain training signals that match the statistics of the signals being sensed. We propose to solve that problem by learning the signal model in situ, based directly on the compressive measurements of the signals, without resorting to other signals to train a model. A key feature of our method is that the signals being sensed are treated as random variables and are integrated out in the likelihood. We derive a maximum marginal likelihood estimator (MMLE), that maximizes the likelihood of the GMM of the underlying signals given only their linear compressive measurements. We extend the MMLE to a GMM with dominantly low-rank covariance matrices, to gain computational speedup. We report extensive experimental results on image inpainting, compressive sensing of high-speed video, and compressive hyperspectral imaging (the latter two based on real compressive cameras). The results demonstrate that the proposed methods outperform state-of-the-art methods by significant margins. Index Terms—Compressive sensing, Gaussian mixture model (GMM), mixture of factor analyzers (MFA), maximum marginal likelihood estimator (MMLE), inpainting, high-speed video, hy-perspectral imaging
with Side Information
"... All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
Abstract
-
Cited by 1 (0 self)
- Add to MetaCart
(Show Context)
All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
CODED APERTURE DESIGN FOR X-RAY TOMOSYNTHESIS by
"... INFORMATION TO ALL USERS The quality of this reproduction is dependent upon the quality of the copy submitted. In the unlikely event that the author did not send a complete manuscript and there are missing pages, these will be noted. Also, if material had to be removed, a note will indicate the del ..."
Abstract
- Add to MetaCart
(Show Context)
INFORMATION TO ALL USERS The quality of this reproduction is dependent upon the quality of the copy submitted. In the unlikely event that the author did not send a complete manuscript and there are missing pages, these will be noted. Also, if material had to be removed, a note will indicate the deletion. Microform Edition © ProQuest LLC. All rights reserved. This work is protected against
IMAGING by
, 2012
"... Rights Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the aut ..."
Abstract
- Add to MetaCart
Rights Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author. Downloaded 15-Sep-2016 18:35:16
An Investigation of Image Segmentation Method for Remotely Sensed Hyperspectral Images with Region Object Aggregations
"... An important aspect of spectral image analysis is identification of materials present in the object or scene being imaged. Since multi-spectral or hyper spectral imagery is generally low resolution, it is possible for pixels in the image to contain several materials. A paramount issue in image proce ..."
Abstract
- Add to MetaCart
(Show Context)
An important aspect of spectral image analysis is identification of materials present in the object or scene being imaged. Since multi-spectral or hyper spectral imagery is generally low resolution, it is possible for pixels in the image to contain several materials. A paramount issue in image processing area is to design and implement an efficient segmentation and classification techniques demanding optimal resources. This paper presents a survey on all prominent region growing segmentation techniques analyzing each one and thus sorting out an optimal and promising technique. Finally study the importance of the best merge region growing normally produces segmentations with closed connected region objects. Recognizing that spectrally similar objects often appear in spatially separate locations, present an approach for tightly integrating best merge region growing with nonadjacent region object aggregation, which we call hierarchical segmentation or HSeg. The effectiveness of the proposed methodology is illustrated by comparing its performance with the state-of-the-art methods on synthetic and real hyper spectral image data sets. The reported results give clear evidence of the relevance of using both spatial and spectral information in hyper spectral image segmentation.
