Dr. Anuj Srivastava Profile

Dr. Anuj Srivastava

Professor at Florida State Univ

SPIE Involvement:

Author

Publications (15)

Proceedings Article | 13 March 2013 Paper

Morphological changes in the corpus callosum: a study using joint Riemannian feature spaces

Meena Mani, Anuj Srivastava, Christian Barillot

Proceedings Volume 8669, 866908 (2013) https://doi.org/10.1117/12.2007226

KEYWORDS: Shape analysis, Diffusion tensor imaging, Brain, Principal component analysis, Analytical research, Optical spheres, Visualization, Alzheimer's disease, Anisotropy, Magnetism

Read Abstract +

Proceedings Article | 23 May 2011 Paper

Statistical analysis and classification of acoustic color functions

J. Derek Tucker, Anuj Srivastava

Proceedings Volume 8017, 80170O (2011) https://doi.org/10.1117/12.884537

KEYWORDS: Acoustics, Statistical analysis, Aluminum, Target detection, Solids, Data analysis, Diffusion, Principal component analysis, Data modeling, Pattern recognition

Read Abstract +

Proceedings Article | 15 March 2011 Paper

A joint model for boundaries of multiple anatomical parts

Grégoire Kerr, Sebastian Kurtek, Anuj Srivastava

Proceedings Volume 7962, 796246 (2011) https://doi.org/10.1117/12.877694

KEYWORDS: Shape analysis, Statistical modeling, Brain, Statistical analysis, Thalamus, Adaptive optics, Stochastic processes, Medical research, Medical diagnostics, Medical imaging

Read Abstract +

Proceedings Article | 15 March 2011 Paper

Classification of mathematics deficiency using shape and scale analysis of 3D brain structures

Sebastian Kurtek, Eric Klassen, John Gore, Zhaohua Ding, Anuj Srivastava

Proceedings Volume 7962, 796244 (2011) https://doi.org/10.1117/12.877363

KEYWORDS: Brain, Shape analysis, Mathematics, Thalamus, Neuroimaging, Medical imaging, Control systems, Optimization (mathematics), 3D image processing, Spherical lenses

Read Abstract +

Proceedings Article | 11 March 2011 Paper

A novel Riemannian metric for analyzing HARDI data

Sentibaleng Ncube, Anuj Srivastava

Proceedings Volume 7962, 79620Q (2011) https://doi.org/10.1117/12.878100

KEYWORDS: Statistical analysis, Denoising, Optical spheres, Diffusion, Shape analysis, Magnetic resonance imaging, Data analysis, Brain, Scanners, High angular resolution diffusion imaging

Read Abstract +

Proceedings Article | 22 October 2001 Paper

Regression model for prediction of IR images

Anuj Srivastava, Brian Thomasson, S. Richard Sims

Proceedings Volume 4379, (2001) https://doi.org/10.1117/12.445364

KEYWORDS: Infrared imaging, Thermography, Infrared cameras, Cameras, Thermal modeling, Volume rendering, Temperature metrology, Databases, Error analysis, Statistical analysis

Read Abstract +

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

Read Abstract +

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 | 4 August 2000 Paper

Bayesian filtering for tracking pose and location of rigid targets

Anuj Srivastava

Proceedings Volume 4052, (2000) https://doi.org/10.1117/12.395067

KEYWORDS: Motion models, Statistical analysis, Error analysis, Nonlinear filtering, Detection and tracking algorithms, Monte Carlo methods, 3D modeling, Sensors, Target recognition, Electronic filtering

Read Abstract +

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

Read Abstract +

Proceedings Article | 27 July 1999 Paper

Estimation of pose and location of ground targets for ATR

Marc Loizeaux, Anuj Srivastava, Michael Miller

Proceedings Volume 3720, (1999) https://doi.org/10.1117/12.357153

KEYWORDS: Sensors, Error analysis, Automatic target recognition, Statistical analysis, Target recognition, Cameras, Detection and tracking algorithms, Ions, Target detection, Image sensors

Read Abstract +

Proceedings Article | 28 May 1999 Paper

Automatic battle damage assessment based on laser radar imagery

Ssu-Hsin Yu, Anuj Srivastava, Raman Mehra

Proceedings Volume 3707, (1999) https://doi.org/10.1117/12.351346

KEYWORDS: LIDAR, Sensors, Fractal analysis, Image processing, Image registration, Distortion, Algorithm development, Expectation maximization algorithms, Distance measurement, Missiles

Read Abstract +

Proceedings Article | 15 September 1998 Paper

MSTAR target classification using Bayesian pattern theory

Raman Mehra, Ravi Ravichandran, Anuj Srivastava

Proceedings Volume 3370, (1998) https://doi.org/10.1117/12.321869

KEYWORDS: Signal to noise ratio, Detection and tracking algorithms, Sensors, Automatic target recognition, Error analysis, Data modeling, Stochastic processes, Image processing, Image classification, Synthetic aperture radar

Read Abstract +

Proceedings Article | 1 April 1998 Paper

Metrics for target recognition

Anuj Srivastava, Ulf Grenander

Proceedings Volume 3307, (1998) https://doi.org/10.1117/12.304657

KEYWORDS: Sensors, Target recognition, Automatic target recognition, Cameras, Remote sensing, Video, Error analysis, Synthetic aperture radar, Chemical elements, 3D acquisition

Read Abstract +

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

Read Abstract +

Proceedings Article | 18 August 1995 Paper

Detection of multiple airborne targets from multisensor data

Mark Foltz, Anuj Srivastava, Michael Miller, Ulf Grenander

Proceedings Volume 2562, (1995) https://doi.org/10.1117/12.216952

KEYWORDS: Target detection, Sensors, Detection and tracking algorithms, Data modeling, Imaging systems, Target recognition, Diffusion, Image resolution, Statistical analysis, Optical resolution

Read Abstract +

Showing 5 of 15 publications

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years