Dr. Natalia A. Schmid Profile

Dr. Natalia A. Schmid

Professor at West Virginia University

SPIE Involvement:

Author

Publications (24)

SPIE Journal Paper | 29 January 2020

Application of the Radon transform for search of candidate pulses in radio astronomy

Marwan Alkhweldi, Natalia Schmid

JATIS, Vol. 6, Issue 01, 018003, (January 2020) https://doi.org/10.1117/12.10.1117/1.JATIS.6.1.018003

KEYWORDS: Radon transform, Detection and tracking algorithms, Astronomy, Signal to noise ratio, Computer simulations, Algorithm development, Radon, Electromagnetic coupling, Radio astronomy, Telescopes

Read Abstract +

Proceedings Article | 1 May 2017 Presentation + Paper

Heterogeneous sharpness for cross-spectral face recognition

Zhicheng Cao, Natalia Schmid

Proceedings Volume 10202, 102020Q (2017) https://doi.org/10.1117/12.2261984

KEYWORDS: Short wave infrared radiation, Near infrared, Biometrics, Image quality, Facial recognition systems, Infrared imaging, Infrared radiation, Neural networks, Quality measurement, Image processing, Algorithm development

Read Abstract +

Matching images acquired in different electromagnetic bands remains a challenging problem. An example of this type of comparison is matching active or passive infrared (IR) against a gallery of visible face images, known as cross-spectral face recognition. Among many unsolved issues is the one of quality disparity of the heterogeneous images. Images acquired in different spectral bands are of unequal image quality due to distinct imaging mechanism, standoff distances, or imaging environment, etc. To reduce the effect of quality disparity on the recognition performance, one can manipulate images to either improve the quality of poor-quality images or to degrade the high-quality images to the level of the quality of their heterogeneous counterparts. To estimate the level of discrepancy in quality of two heterogeneous images a quality metric such as image sharpness is needed. It provides a guidance in how much quality improvement or degradation is appropriate. In this work we consider sharpness as a relative measure of heterogeneous image quality. We propose a generalized definition of sharpness by first achieving image quality parity and then finding and building a relationship between the image quality of two heterogeneous images. Therefore, the new sharpness metric is named heterogeneous sharpness. Image quality parity is achieved by experimentally finding the optimal cross-spectral face recognition performance where quality of the heterogeneous images is varied using a Gaussian smoothing function with different standard deviation. This relationship is established using two models; one of them involves a regression model and the other involves a neural network. To train, test and validate the model, we use composite operators developed in our lab to extract features from heterogeneous face images and use the sharpness metric to evaluate the face image quality within each band. Images from three different spectral bands visible light, near infrared, and short-wave infrared are considered in this work. Both error of a regression model and validation error of a neural network are analyzed.

SPIE Journal Paper | 19 August 2016

Composite multilobe descriptors for cross-spectral recognition of full and partial face

Zhicheng Cao, Natalia Schmid, Thirimachos Bourlai

OE, Vol. 55, Issue 08, 083107, (August 2016) https://doi.org/10.1117/12.10.1117/1.OE.55.8.083107

KEYWORDS: Visible radiation, Short wave infrared radiation, Composites, Near infrared, Infrared imaging, Eye, Facial recognition systems, Mid-IR, Long wavelength infrared, Computer programming

Read Abstract +

Proceedings Article | 12 May 2016 Paper

Image disparity in cross-spectral face recognition: mitigating camera and atmospheric effects

Zhicheng Cao, Natalia Schmid, Xin Li

Proceedings Volume 9844, 98440Z (2016) https://doi.org/10.1117/12.2225149

KEYWORDS: Infrared imaging, Image quality, Visible radiation, Facial recognition systems, Cameras, Quality measurement, Denoising, Short wave infrared radiation, Near infrared, Image enhancement

Read Abstract +

Proceedings Article | 22 May 2015 Paper

Inferential statistics for transient signal detection in radio astronomy phased arrays

Natalia Schmid, Richard Prestage, Marwan Alkhweldi

Proceedings Volume 9476, 94760S (2015) https://doi.org/10.1117/12.2184101

KEYWORDS: Signal detection, Interference (communication), Signal processing, Phased arrays, Radio astronomy, Astronomy, Data modeling, Signal to noise ratio, Statistical analysis, Receivers

Read Abstract +

Proceedings Article | 22 May 2015 Paper

Distributed estimation of a parametric field with random sensor placements

Marwan Alkhweldi, Zhicheng Cao, Natalia Schmid

Proceedings Volume 9476, 94760R (2015) https://doi.org/10.1117/12.2177277

KEYWORDS: Sensors, Sensor networks, Signal processing, Error analysis, Interference (communication), Signal to noise ratio, Sensor performance, Numerical analysis, Algorithm development, Signal generators

Read Abstract +

Proceedings Article | 22 May 2015 Paper

Composite multi-lobe descriptor for cross spectral face recognition: matching active IR to visible light images

Zhicheng Cao, Natalia Schmid

Proceedings Volume 9476, 94760T (2015) https://doi.org/10.1117/12.2176462

KEYWORDS: Near infrared, Short wave infrared radiation, Visible radiation, Infrared imaging, Feature extraction, Composites, Facial recognition systems, Thermography, Computer programming, Infrared radiation

Read Abstract +

Proceedings Article | 10 June 2013 Paper

Target localization and function estimation in sparse sensor networks

Natalia Schmid

Proceedings Volume 8744, 87440V (2013) https://doi.org/10.1117/12.2016656

KEYWORDS: Sensors, Signal to noise ratio, Signal processing, Sensor networks, Quantization, Signal generators, Data modeling, Interference (communication), Analog electronics, Data fusion

Read Abstract +

Proceedings Article | 31 May 2013 Paper

Encoding and selecting features for boosted multispectral face recognition: matching SWIR versus color

Sirisha Boothapati, Natalia Schmid

Proceedings Volume 8712, 87120A (2013) https://doi.org/10.1117/12.2015929

KEYWORDS: Short wave infrared radiation, Feature selection, Computer programming, Facial recognition systems, Image segmentation, Near infrared, Visible radiation, Binary data, Long wavelength infrared, Infrared radiation

Read Abstract +

Proceedings Article | 18 May 2012 Paper

Near real-time face detection and recognition using a wireless camera network

Francesco Nicolo, Srikanth Parupati, Vinod Kulathumani, Natalia Schmid

Proceedings Volume 8392, 83921I (2012) https://doi.org/10.1117/12.920696

KEYWORDS: Cameras, Image fusion, Facial recognition systems, Image resolution, Image processing, Image filtering, Detection and tracking algorithms, Eye, Optical filters, Imaging systems

Read Abstract +

Proceedings Article | 9 May 2012 Paper

Distributed parameter estimation in wireless sensor networks using fused local observations

Mohammad Fanaei, Matthew Valenti, Natalia Schmid, Marwan Alkhweldi

Proceedings Volume 8404, 840404 (2012) https://doi.org/10.1117/12.919664

KEYWORDS: Sensors, Signal to noise ratio, Analog electronics, Expectation maximization algorithms, Quantization, Sensor networks, Error analysis, Statistical analysis, Sensor fusion, Environmental sensing

Read Abstract +

Proceedings Article | 5 May 2011 Paper

Channel-aware distributed classification in wireless sensor networks using binary local decisions

Mohammad Fanaei, Matthew Valenti, Natalia Schmid, Vinodkrishnan Kulathumani

Proceedings Volume 8050, 80501L (2011) https://doi.org/10.1117/12.884397

KEYWORDS: Sensors, Binary data, Sensor networks, Signal to noise ratio, Classification systems, Sensor fusion, Environmental sensing, Magnetic sensors, Magnetism, Solids

Read Abstract +

SPIE Journal Paper | 1 April 2010

Automatic target recognition performance losses in the presence of atmospheric and camera effects

Xiaohan Chen, Natalia Schmid

JEI, Vol. 19, Issue 02, 023016, (April 2010) https://doi.org/10.1117/12.10.1117/1.3435348

KEYWORDS: Databases, Automatic target recognition, Sensors, Target detection, Cameras, Image segmentation, Image filtering, Target recognition, Detection and tracking algorithms, 3D acquisition

Read Abstract +

Proceedings Article | 29 January 2010 Paper

IT and SLT characterizations of secured biometric authentication systems

Natalia Schmid, Harry Wechsler

Proceedings Volume 7541, 75410M (2010) https://doi.org/10.1117/12.839129

KEYWORDS: Biometrics, Probability theory, Binary data, Error analysis, Information technology, Distortion, Biological research, Sensors, Statistical analysis, Telecommunications

Read Abstract +

Proceedings Article | 17 April 2008 Paper

On the limits of target recognition in the presence of atmospheric effects

Xiaohan Chen, Natalia Schmid

Proceedings Volume 6968, 69680J (2008) https://doi.org/10.1117/12.777850

KEYWORDS: Sensors, Automatic target recognition, Image filtering, Target detection, Image segmentation, Cameras, Target recognition, Detection and tracking algorithms, Databases, Image classification

Read Abstract +

The importance of Networked Automatic Target Recognition systems for surveillance applications is continuously increasing. Because of the requirement of a low cost and limited payload these networks are traditionally equipped with lightweight, low-cost sensors such as Electro Optical or Infrared sensors. The quality of imagery acquired by these sensors critically depends on the environmental conditions, type and characteristics of sensors, and absence of occluding or concealing objects. In the past a large number of efficient detection, tracking, and recognition algorithms have been designed to operate on imagery of good quality. However, detection and recognition limits under non-ideal environmental and/or sensor based distortions have not been carefully evaluated. This work describes a real image dataset formed by imaging 10 die cast models of military vehicles at different elevation and orientation angles. The dataset contains imagery acquired both indoors and outdoors. The indoors dataset is composed of clear and distorted images. The distortions include defocus blur, sided illumination, low contrast, shadows and occlusions. All images in this dataset, however, have a uniform blue background. The indoors dataset is applied to evaluate the degradations of recognition performance due to camera and illumination effects. The recognition method is based on Bessel K forms. The dataset collected outdoors includes real background and is much more complex to process. This dataset is used to evaluate performance of a fully automatic target recognition system that involves a Haar-based detector to select potential regions of interest within images; performs adjustment and fusion of detected regions; segments potential targets using a region based approach; identifies targets using Bessel K form-based encoding; and performs clutter rejection. The numerical results demonstrate that the complexity of the background and the presence of occlusions lead to substantial detection and recognition performance degradations.

Proceedings Article | 9 May 2007 Paper

UAV-based distributed ATR under realistic simulated environmental effects

Xiaohan Chen, Shanshan Gong, Natalia Schmid, Matthew Valenti

Proceedings Volume 6567, 65670E (2007) https://doi.org/10.1117/12.719849

KEYWORDS: Image fusion, Sensors, Detection and tracking algorithms, Data fusion, Image filtering, Unmanned aerial vehicles, Cameras, Automatic target recognition, Lawrencium, Target detection

Read Abstract +

Proceedings Article | 12 April 2007 Paper

Super-resolution for high magnification face images

Yi Yao, Besma Abidi, Nathan Kalka, Natalia Schmid, Mongi Abidi

Proceedings Volume 6539, 65390G (2007) https://doi.org/10.1117/12.720113

KEYWORDS: Image processing, Image resolution, Facial recognition systems, Super resolution, Detection and tracking algorithms, Zoom lenses, Databases, Wavelets, Image registration, Surveillance

Read Abstract +

Proceedings Article | 17 April 2006 Paper

Image quality assessment for iris biometric

Nathan Kalka, Jinyu Zuo, Natalia Schmid, Bojan Cukic

Proceedings Volume 6202, 62020D (2006) https://doi.org/10.1117/12.666448

KEYWORDS: Image quality, Iris, Iris recognition, Image segmentation, Image fusion, Motion estimation, Biometrics, Factor analysis, Light sources and illumination, Detection and tracking algorithms

Read Abstract +

Proceedings Article | 28 March 2005 Paper

Performance evaluation of iris-based recognition system implementing PCA and ICA encoding techniques

Vivekanand Dorairaj, Natalia Schmid, Gamal Fahmy

Proceedings Volume 5779, (2005) https://doi.org/10.1117/12.604201

KEYWORDS: Iris, Independent component analysis, Iris recognition, Principal component analysis, Computer programming, Image resolution, Distance measurement, Biometrics, Image processing, Image segmentation

Read Abstract +

Proceedings Article | 25 October 2004 Paper

Nonfrontal/nonideal iris localization and acquisition

Gamal Fahmy, Lawrence Hornak, Natalia Schmid, Xin Li, Stephanie Schuckers

Proceedings Volume 5601, (2004) https://doi.org/10.1117/12.570269

KEYWORDS: Iris recognition, Video, Biometrics, Image processing, Electronic filtering, Detection and tracking algorithms, Image filtering, Video processing, Gaussian filters, Sclera

Read Abstract +

Proceedings Article | 22 October 2001 Paper

Classification of Gaussian data with sieve-regularized estimates

Natalia Schmid, Joseph O'Sullivan

Proceedings Volume 4379, (2001) https://doi.org/10.1117/12.445405

KEYWORDS: Error analysis, Statistical analysis, Analytical research, Expectation maximization algorithms, Data analysis, Stochastic processes, Classification systems, Data modeling, Image classification, Binary data

Read Abstract +

Proceedings Article | 27 August 2001 Paper

Probabilistic approach to model extraction from training data

Michael DeVore, Joseph O'Sullivan, Sushil Anand, Natalia Schmid

Proceedings Volume 4382, (2001) https://doi.org/10.1117/12.438229

KEYWORDS: Data modeling, Automatic target recognition, Target recognition, Model-based design, Statistical modeling, Synthetic aperture radar, Radar, Solid modeling, Image segmentation, Systems modeling

Read Abstract +

Many of the approaches to automatic target recognition (ATR) for synthetic aperture radar (SAR) images that have been proposed in the literature fall into one of two broad classes, those based on prediction of images from models (CAD or otherwise) of the targets and those based on templates describing typical received images which are often estimated from sample data. Systems utilizing model-based prediction typically synthesize an expected SAR image given some target class and pose and then search for the combination of class and pose which maximizes some match metric between the synthesized and observed images. This approach has the advantage of being robust with respect to target pose and articulation not previously encountered but does require detailed models of the targets of interest. On the other hand, template-based systems typically do not require detailed target models but instead store expected images for a range of targets and poses based on previous observations (training data) and then search for the template which most closely represents the observed image. We consider the design and use of probabilistic models for targets developed from training data which do not require CAD models of the targets but which can be used in a hypothesize-and-predict manner similar to other model-based approaches. The construction of such models requires the extraction from training data of functions which characterize the target radar cross section in terms of target class, pose, articulation, and other sources of variability. We demonstrate this approach using a conditionally Gaussian model for SAR image data and under that model develop the tools required to determine target models and to use those models to solve inference problems from an image of an unknown target. The conditionally Gaussian model is applied in a target-centered reference frame resulting in a probabilistic model on the surface of the target. The model is segmented based on the information content in regions of the target space. Modeling radar power variability and target positional uncertainty results in improved accuracy. Performance results are presented for both target classification and orientation estimation using the publicly available MSTAR dataset.

Proceedings Article | 17 August 2000 Paper

Method for reducing dimensionality in ATR systems

Joseph O'Sullivan, Natalia Schmid

Proceedings Volume 4050, (2000) https://doi.org/10.1117/12.395582

KEYWORDS: Error analysis, Data modeling, Statistical analysis, Stochastic processes, Monte Carlo methods, Pattern recognition, Automatic target recognition, Feature selection, Samarium, Fourier transforms

Read Abstract +

Proceedings Article | 13 August 1999 Paper

Dependence of ATR system performance on size of training sets

Joseph O'Sullivan, Natalia Schmid

Proceedings Volume 3721, (1999) https://doi.org/10.1117/12.357688

KEYWORDS: Probability theory, Automatic target recognition, Data modeling, Systems modeling, Statistical analysis, Binary data, Information operations, Receivers, Radar, Target detection

Read Abstract +

Showing 5 of 24 publications

Conference Committee Involvement (3)

Biometric and Surveillance Technology for Human and Activity Identification XII

22 April 2015 | Baltimore, MD, United States

Biometric and Surveillance Technology for Human and Activity Identification XI

8 May 2014 | Baltimore, MD, United States

Biometric and Surveillance Technology for Human and Activity Identification X

2 May 2013 | Baltimore, Maryland, United States

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