jarvis haupt department of electrical and computer engineering university of minnesota
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Compressive Saliency Sensing: Locating Outliers in Large Data Collections from Compressive Measurements. Jarvis Haupt Department of Electrical and Computer Engineering University of Minnesota. Supported by:. TexPoint fonts used in EMF. - PowerPoint PPT PresentationTRANSCRIPT
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Jarvis Haupt
Department of Electrical and Computer Engineering
University of Minnesota
Compressive Saliency Sensing:Locating Outliers in Large Data Collections
from Compressive Measurements
Supported by:
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– What’s so Interesting about Sparsity? –
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Sparsity and Your Digital Camera
Compress
…
(DW
T)
Original Image
Store…
Goldy.jpg(~300kB)
Raw Data(Megapixels…)
Acquire…
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Sparsity in Science and Medicine
Wide-field Infrared Survey Explorer (WISE)
Fornax Galaxy Cluster Feb. 17 2010
Functional Magnetic Resonance Imaging (fMRI)
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Sample & DFT
Received signal…
Sparsity in Communications
Fourier representation…
Are we alone?
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A Sparse Signal Model
number of nonzero signal components
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Compressed/Compressive Sensing
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Convex Optimizations:(Chen, Donoho & Saunders; Donoho; Candes, Romberg, & Tao; Candes & Tao; Wainwright; Zhao & Yu; Yuan & Lin; Chandrasekaran, Recht, Parrilo, & Willsky;
Rao, Recht, & Nowak; Wright, Ganesh, Min, & Ma;…)
Greedy Methods:(Mallat & Zhang; Pati, Rezaiifar, & Krishnaprasad; Davis, Mallat, & Zhang;
Temlyakov; Tropp & Gilbert; Donoho, Tsaig, Drori, & Starck; Needell & Tropp;…)
Sketching:(Indyk & Motwani; Indyk; Charikar, Chen, & Farach-Colton; Cormode &
Muthukrishnan; Muthukrishnan; Indyk & Gilbert; Berinde; Li, Church, & Hastie;…)
Bayesian Approaches:(Tipping; Ji, Xue, & Carin; Ji, Dunson & Carin; Seeger & Nickisch; Wipf, Palmer, & Rao; Vila & Schniter;…)
Group Testing:(Dorfman; Feller; Sterrett; Sobel & Groll; Du & Huang; Indyk, Ngo, & Rudra; Gilbert & Strauss; Iwen; Gilbert, Iwen, & Strauss; Emad & Milenkovic; Atia &
Saligrama;Cheraghchi, Hormati, Karbasi, & Vetterli; Chan, Che, Jaggi & Saligrama…)
Sparse Recovery…an Active Area!
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– Beyond Sparsity –
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A “Simple” Extension
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Recovery of Simple Signals
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What’s so “Interesting” about Simple Signals?
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– A Generalized Sparse Recovery Task –
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Problem Formulation
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– Compressive Saliency Sensing –Salient Support Recovery from Compressive Measurements
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Assumptions
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Some Examples
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Approach: Solve a Proxy Problem
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Compressive Saliency Sensing
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Main Result
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– Experimental Results –
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– Simple Signals –
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Simple Signal – Salient Support Recovery
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– An Application in Computer Vision –
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Visual Saliency
Much MUCH work has been done developing techniques to automaticallyidentify salient regions of a given image:
(Itti, Koch, & Niebur, Itti & Koch; Harel, Koch, & Perona; Bruce & Tsotsos, …)
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Saliency in Computer Vision
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A Generalized form of Sparsity
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Subspace Outlier Models for Saliency
Original Image (380x260)
Vectorize
10x10 patches
100 x 988 matrix
(A simplified case of the GMM subspace models
used by Yu & Sapiro 2011)
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Is This a Good Model for Image Saliency?
Prior work exploiting sparse and low-rank models for saliency (Yan, Zhu, Liu & Liu; Shen & Wu;…)
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Saliency Maps from Compressive Samples
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Saliency Maps from Compressive Samples
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Extensions?
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– Extra Slides –
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Parallel Gigapixel Imagers
FromH. S. Son, et al., “Design of a spherical focal surface using close packed relay optics,” Optics Express, vol. 19, no. 17, 2011
(Duke University)
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Mosaicing Gigapixel ImagersCAVE Group – Columbia University(www.cs.columbia.edu/CAVE/projects/gigapixel/)
GigaPan(www.gigapan.com/)
dgCam(www.dgcam.org/)