Information-Theoretic Signal and Image Processing

Professor Joseph A. O’Sullivan Electrical and Systems Engineering

Associate Director, Center for Security Technologies

Science and Technology Intellectual Thrusts

• Information-Theoretic Signal and Image Processing:Snyder, Byrnes, Fuhrmann, Ghosh, Isidori,

Mukai, O’Sullivan, Preza, Wickerhauser

• Recognition Theory and Systems:O’Sullivan, Byrnes, Chamberlain, Grimm,

Indeck, Martin, Pless, Smith, Wickerhauser

Outline: Signal and Image Processing

• Fundamentals of Imaging Science• Application: Recognition System Design and Analysis

– Open problem: fundamental performance bounds as a function of system complexity

– Systems integration issues for biometrics• Application: X-Ray Imaging for Luggage Inspection

– Open problem: implement efficiently, combine recognition– Systems integration for combined imaging, threat detection

• Application: Spectral Analysis– Open problem: performance for spectral unmixing– Systems integration for rapid analysis, interpretation

Technology Expertise and Enablersdevices, design, analysis, computation, electronics, systems ...

• Cheap, even disposable sensors• Inexpensive mass data storage• Fundamental advances in imaging science• Reconfigurable, fast electronics • Affordable, rapid computational processing• Economical, pervasive networking, including

wireless

Imaging Science at Washington University

• Leadership in Medical Imaging— Development of First PET Machines— Brain Mapping— Deformable Anatomy

• Astronomical Imaging, Hubble Telescope• Center for Imaging Science, 1995-2000

— Fundamentals of Imaging Science, Recognition Theory— Performance Bounds for ATR— ATR System Design and Analysis

CST: applying analogous methodology to security technologies

Fundamental Approach

SceneModel

PropagationModel

SensorModel

AlgorithmDevelopment

AbstractionBarrier

PerformanceAnalysis

Physics of Scenesand Spectra

PropagationPhysics

Physics of Sensorsand FPAs

Signal and ImageProcessors

• Biometrics-based recognition including face,fingerprint, voice, retinal, DNA, etc.

• Physical signature recognition including magnetic signatures

• Fast database searching and recognition system implementation

• Data mining, intelligence extraction, situational awareness

Target Target ClassifierClassifier ââ=T72=T72

Recognition Theory and Systems

Recognition Theory and Systems

• System Design – Data models system outline– Training data, physics model parameters– Implementation constraints on architecture,

computational platform, time– Performance goals and constraints

• Range of Applications– Object recognition– Biometric recognition– Biochemical contaminants

Example: Radar ATR Theory and Performance

• Publicly available SAR images from MSTAR program; over 15,000 images in data set

• Compare performance and database complexity for a variety of data models

2S12S1 T62T62 BTR 60BTR 60 D7D7 ZIL 131ZIL 131 ZSU 23/4ZSU 23/4

• Derive statistical models from data, physics, constraintspR|Θ,a(r|θ,a) - conditional data modelpΘ|a(θ|a) – prior probability on orientationp(a) – prior probability on target class

• Recognition: select most likely target• Evaluate performance over test set• Repeat for different models and complexity constraints

Target Target ClassifierClassifier

ââ=T72=T72Given a SAR image r, determine a corresponding target class â∈A and pose θ

Target Recognition ProblemStatistical Likelihood Approach

Recognition Performance

•Model correctness: validity of statistical likelihood•Model segmentation: parts of data modeled

•Approximation complexity: number of poses

BMP2 Variance Image at 6 Sizes

Fine model of a T72 tank (1/7 relative scale),72 templates totaling 1.1M floats

Coarse model of aT62 tank, 1 template with 16K floats

Example Results•240 variations per model families•10 model families (6 reported)

Performance—Complexity Analysis

• Open Problems– Given finite size database, finite

representation of data, determine fundamental limit on performance

– Computational limit

• Systems Integration– Design of biometric systems– Dynamic resource allocation

Target Target ClassifierClassifier

ââ=T72=T72

CT Imaging: Luggage Inspection

• Mandate that every checked bag be screened as of Jan. 1, 2003 (by the Transportation Security Administration (TSA))

• Goals: accurate images, good display, automatic detection of potential threats, speed

• WU Basis: medical CT imaging, nondestructive testing

CT Imaging

• Physics-based models• Novel algorithm development to

mitigate effects of metal• Medical imaging for prostheses,

brachytherapy applicators, dental implants

• Industrial imaging nondestructive testing

CT Imaging

• Challenges – efficient implementation– unknown objects in scene, unknown

orientations, shapes– spectral modeling

• Security Problem: Screening of Luggage– accurate artifact-free images– automatic threat detection– speed of processing

Spectral Analysis

• Security Applications– Detection of biological and chemical contaminants

• Security of food and water supply, mail, currency• Emergency room detection, pattern detection

– Remote spectral sensing• Agricultural classification

– Mass spectrometry• Systems Integration Issues

– Rapid analysis, extraction of signatures– Rapid search of databases– Coupling to sensors of interest

Spectral Unmixing

• Component Spectra– Database– Extracted from data

• Quantify Variability ofSpectra

• Quantify Relative Concentrations inNew Data

Conclusions

• Fundamental engineering and scientific approach to signal and image processing

• Critical link in many security systems• Tight coupling between

• Information-Theoretic Signal and Image Processing

• Recognition Theory and Systems

• Unique expertise at Washington University