Dr. Matthew L. Cooper Profile

Dr. Matthew L. Cooper

at FX Palo Alto Lab

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Proceedings Article | 7 March 2013 Paper

Presentation video retrieval using automatically recovered slide and spoken text

Matthew Cooper

Proceedings Volume 8667, 86670E (2013) https://doi.org/10.1117/12.2008433

KEYWORDS: Video, Optical character recognition, Associative arrays, Visualization, Information visualization, Error analysis, Speech recognition, Multimedia, Feature extraction, Cameras

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Proceedings Article | 11 February 2011 Paper

Multimedia information retrieval at FX Palo Alto Laboratory

Matthew Cooper, John Adcock, Andreas Girgensohn, Jeremy Pickens, Lynn Wilcox

Proceedings Volume 7881, 78811B (2011) https://doi.org/10.1117/12.878615

KEYWORDS: Video, Visualization, Multimedia, Human-machine interfaces, Interfaces, Digital photography, Photography, Internet, Document management, Mobile devices

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Proceedings Article | 25 October 2005 Paper

Seamless presentation capture, indexing, and management

David Hilbert, Matthew Cooper, Laurent Denoue, John Adcock, Daniel Billsus

Proceedings Volume 6015, 60150X (2005) https://doi.org/10.1117/12.631069

KEYWORDS: Image segmentation, Video, Optical character recognition, Image classification, RGB color model, Human-machine interfaces, Projection systems, Multimedia, Prototyping, Switches

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Proceedings Article | 10 January 2003 Paper

Media segmentation using self-similarity decomposition

Jonathan Foote, Matthew Cooper

Proceedings Volume 5021, (2003) https://doi.org/10.1117/12.476302

KEYWORDS: Image segmentation, Video, Matrices, Data modeling, Distance measurement, Visualization, Research management, Databases, Analytical research, Statistical analysis

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Proceedings Article | 17 August 2000 Paper

Asymptotic analysis of pattern-theoretic object recognition

Matthew Cooper, Anuj Srivastava

Proceedings Volume 4050, (2000) https://doi.org/10.1117/12.395592

KEYWORDS: Signal to noise ratio, Object recognition, Automatic target recognition, Sensors, Video, Error analysis, Target recognition, Remote sensing, Mathematical modeling, 3D modeling

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Automated target recognition (ATR) is a problem of great importance in a wide variety of applications: from military target recognition to recognizing flow-patterns in fluid- dynamics to anatomical shape-studies. The basic goal is to utilize observations (images, signals) from remote sensors (such as videos, radars, MRI or PET) to identify the objects being observed. In a statistical framework, probability distributions on parameters representing the object unknowns are derived an analyzed to compute inferences (please refer to [1] for a detailed introduction). An important challenge in ATR is to determine efficient mathematical models for the tremendous variability of object appearance which lend themselves to reasonable inferences. This variation may be due to differences in object shapes, sensor-mechanisms or scene- backgrounds. To build models for object variabilities, we employ deformable templates. In brief, the object occurrences are described through their typical representatives (called templates) and transformations/deformations which particularize the templates to the observed objects. Within this pattern-theoretic framework, ATR becomes a problem of selecting appropriate templates and estimating deformations. For an object (alpha) (epsilon) A, let I^(alpha ) denote a template (for example triangulated CAD-surface) and let s (epsilon) S be a particular transformation, then denote the transformed template by sI^(alpha ). Figure 1 shows instances of the template for a T62 tank at several different orientations. For the purpose of object classification, the unknown transformation s is considered a nuisance parameter, leading to a classical formulation of Bayesian hypothesis- testing in presence of unknown, random nuisance parameters. S may not be a vector-space, but it often has a group structure. For rigid objects, the variation in translation and rotation can be modeled through the action of special Euclidean group SE(n). For flexible objects, such as anatomical shapes, higher-dimensional groups such as a diffeomorphisms are utilized.

Proceedings Article | 24 August 1999 Paper

Efficient estimation of thermodynamic state incorporating Bayesian model order selection

Aaron Lanterman, Matthew Cooper, Michael Miller

Proceedings Volume 3718, (1999) https://doi.org/10.1117/12.359939

KEYWORDS: Data modeling, Thermodynamics, Sensors, Target recognition, Principal component analysis, 3D modeling, Infrared radiation, Silicon, Visualization, Automatic target recognition

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Proceedings Article | 24 August 1999 Paper

Multispectral sensor fusion for ground-based target orientation estimation: FLIR, ladar, HRR

Joseph Kostakis, Matthew Cooper, Thomas Green, Michael Miller, Joseph O'Sullivan, Jeffrey Shapiro, Donald Snyder

Proceedings Volume 3718, (1999) https://doi.org/10.1117/12.359949

KEYWORDS: Sensors, LIDAR, Signal to noise ratio, Radar, Error analysis, Forward looking infrared, Infrared sensors, Statistical modeling, Sensor fusion, Imaging systems

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Proceedings Article | 13 August 1999 Paper

Quadratic analysis of information measures for object recognition

Matthew Cooper, Anuj Srivastava, Michael Miller

Proceedings Volume 3721, (1999) https://doi.org/10.1117/12.357696

KEYWORDS: Sensors, Object recognition, Signal to noise ratio, Solids, Video, Sensor fusion, Remote sensing, Imaging systems, Forward looking infrared, Image processing

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Proceedings Article | 18 September 1998 Paper

Multispectral active-passive sensor fusion for ground-based target orientation estimation

Joseph Kostakis, Matthew Cooper, Thomas Green, Michael Miller, Joseph O'Sullivan, Jeffrey Shapiro, Donald Snyder

Proceedings Volume 3371, (1998) https://doi.org/10.1117/12.323868

KEYWORDS: Sensors, LIDAR, Error analysis, Forward looking infrared, Radar, Signal to noise ratio, Sensor fusion, Infrared sensors, Passive sensors, Imaging systems

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Proceedings Article | 15 September 1998 Paper

Information measures for object recognition

Matthew Cooper, Michael Miller

Proceedings Volume 3370, (1998) https://doi.org/10.1117/12.321866

KEYWORDS: Thermodynamics, Sensors, Automatic target recognition, Solid modeling, Video, Forward looking infrared, Signal to noise ratio, Computer aided design, Sensor fusion, Image sensors

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Proceedings Article | 28 July 1997 Paper

Accommodating geometric and thermodynamic variability for forward-looking infrared sensors

Matthew Cooper, Ulf Grenander, Michael Miller, Anuj Srivastava

Proceedings Volume 3070, (1997) https://doi.org/10.1117/12.281553

KEYWORDS: Thermodynamics, Databases, Forward looking infrared, Error analysis, Automatic target recognition, Monte Carlo methods, Statistical analysis, Sensors, Computer aided design, Solid modeling

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Showing 5 of 11 publications

Conference Committee Involvement (5)

Mobile Devices and Multimedia: Enabling Technologies, Algorithms, and Applications 2015

10 February 2015 | San Francisco, California, United States

Mobile Devices and Multimedia: Enabling Technologies, Algorithms, and Applications 2014

3 February 2014 | San Francisco, California, United States

Multimedia Content Access: Algorithms and Systems VII

4 February 2013 | Burlingame, California, United States

Multimedia Content Access: Algorithms and Systems VI

23 January 2012 | Burlingame, California, United States

Multimedia Content Access: Algorithms and Systems V

25 January 2011 | San Francisco Airport, California, United States

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