1 / 35

Jarvis Haupt Department of Electrical and Computer Engineering University of Minnesota

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.

signa
Download Presentation

Jarvis Haupt Department of Electrical and Computer Engineering University of Minnesota

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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. Read the TexPoint manual before you delete this box.: AAAAA

  2. – What’s so Interesting about Sparsity? –

  3. Sparsity and Your Digital Camera Acquire… Original Image Raw Data (Megapixels…) Goldy.jpg (~300kB) Compress… (DWT) Store…

  4. Sparsityin Science and Medicine Wide-field Infrared Survey Explorer (WISE) Functional Magnetic Resonance Imaging (fMRI) Fornax Galaxy Cluster Feb. 17 2010

  5. Sparsity in Communications Are we alone? Sample & DFT Fourier representation… Received signal…

  6. A Sparse Signal Model number of nonzero signal components

  7. Compressed/Compressive Sensing

  8. Sparse Recovery…an Active Area! 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…)

  9. – Beyond Sparsity –

  10. A “Simple” Extension

  11. Recovery of Simple Signals

  12. What’s so “Interesting” about Simple Signals?

  13. – A Generalized Sparse Recovery Task –

  14. Problem Formulation

  15. – Compressive Saliency Sensing – Salient Support Recovery from Compressive Measurements

  16. Assumptions

  17. Some Examples

  18. Approach: Solve a Proxy Problem

  19. Compressive Saliency Sensing

  20. Main Result

  21. – Experimental Results –

  22. – Simple Signals –

  23. Simple Signal – Salient Support Recovery

  24. – An Application in Computer Vision –

  25. Visual Saliency Much MUCH work has been done developing techniques to automatically identify salient regions of a given image: (Itti, Koch, & Niebur, Itti & Koch; Harel, Koch, & Perona; Bruce & Tsotsos, …)

  26. Saliency in Computer Vision

  27. A Generalized form of Sparsity

  28. Subspace Outlier Models for Saliency Vectorize 10x10 patches 100 x 988 matrix Original Image (380x260) (A simplified case of the GMM subspace models used by Yu & Sapiro 2011)

  29. Is This a Good Model for Image Saliency? Prior work exploiting sparse and low-rank models for saliency (Yan, Zhu, Liu & Liu; Shen & Wu;…)

  30. Saliency Maps from Compressive Samples

  31. Saliency Maps from Compressive Samples

  32. Extensions?

  33. – Extra Slides –

  34. Parallel Gigapixel Imagers From H. S. Son, et al., “Design of a spherical focal surface using close packed relay optics,” Optics Express, vol. 19, no. 17, 2011 (Duke University)

  35. MosaicingGigapixel Imagers CAVE Group – Columbia University (www.cs.columbia.edu/CAVE/projects/gigapixel/) dgCam (www.dgcam.org/) GigaPan (www.gigapan.com/)

More Related