Teresa L. P. Olson

Advanced Targeting Systems (United States)

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Co-Authors

Haiwen Chen

Air Force Medical University

Leon H. Sibul

Applied Research Laboratory at the University of Hawai‘i

Harry C. Lee

Lockheed Martin (United States)

Surachai Sutha

Rajalakshmi Engineering College

Carl W. Sanford

Lockheed Martin (United States)

Harry Lee

Purdue University West Lafayette

Jason A. Sefcik

Lockheed Martin (United States)

Robert Stanfill

Lockheed Martin (United States)

Harry C. Lee

Lockheed Martin (United States)

Tim E. Olson

University of Florida

Cooperative Institutions

Lockheed Martin (United States)

Advanced Targeting Systems (United States)

Sensors (United States)

Johns Hopkins University

Applied Research Laboratory at the University of Hawai‘i

Applied Research (United States)

Pennsylvania State University

Signal Processing (United States)

American University of Nigeria

University of Georgia

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<title>Image-based tracker performance metric</title>

Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (2002)

Teresa L. P. Olson Robert Stanfill

Currently, there is no standard quantitative measure of the performance of an image-based tracker algorithm. It is usually described as remaining in track for the entire run or losing track at some point during a run. This is purely a qualitative evaluation. Without a quantitative measure it becomes difficult to accurately evaluate the performance of a tracker algorithm. We have developed the Tracker Performance Metric (TPM) specifically for this purpose. It was designed to measure the output performance, on a frame-by-frame basis, using its output position and quality (sometimes referred to as confidence) state. The TPM can also be used as a measure of algorithm performance to compare against the Trackability Metric. The Trackability Metric was developed by AMCOM to determine how trackable a set of data should be. The TPM will be described and results presented.

Performance metric

Position (finance)

10.1117/12.472596

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<title>Detection and classification of acoustic transients using wavelet transforms</title>

Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (1996)

Teresa L. P. Olson Leon H. Sibul

The search for an optimum mother wavelet for the detection of transient signals can be made tractable if one realizes that all transients are generated by a physical system. The canonical model for many transients can be derived from a simple parallel resonant circuit. It is shown that the impulse response from this circuit acts as a mother wavelet and parameterizes the circuit via its resonant frequency and quality factor. Once the family of transients is defined, a wavelet-based estimator correlator is used to detect and classify transient signals with unknown frequency and arrival times.

Impulse response

Transient (computer programming)

Transient analysis

10.1117/12.255245

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Citations (1)

Infrared Autonomous Acquisition and Tracking

Teresa L. P. Olson Harry C. Lee

Abstract : In order to demonstrate lock-on-after-launch (LOAL) capability, imaging infrared missile systems of the future require the ability to autonomously identify and track targets of interest. A robust algorithm architecture must have the flexibility to accommodate fluid system requirements driving its design. This paper describes a method to autonomously acquire and track an extended range target through its whole flight scenario. A proven ATR approach is used to detect and identify targets of interest, separating them into non-targets and clutter. The methodology uses a down selection strategic to nominate targets for terminal track. Once nominated, a Weighted Edge Tracker is employed. The tracker relies upon correlations of appropriately weighted edge directions from frame-to-frame images and reference templates. This combination of automatic target acquisition and terminal tracking provides a sophisticated yet simple approach to many challenging long range tracking problems.

Tracking (education)

Target acquisition

Template

10.21236/ada384928

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Image analysis and understanding using super resolution

Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (2007)

Harry Lee Teresa L. P. Olson Drew Manville Gene Cloud

Through the trade-off temporal information, a significant increase in spatial resolution is obtainable. This improvement is quantifiable by using Airy's disc analysis against camera sensor pitch. Integrate the use of Airy's disc to quantify the image improvement in resolvability and ultimately system range. It this comparison that sets the ground works for realistic expectation. Our SR system is a natural tracker of moving vehicles with the addition of improved target resolvability. Super Resolution can capitalize on camera platforms instability. A by product of SR is digitally stabilize imagery to a fraction of a sub-pixel. Investigation in the sub-pixel remapping has lead to the developed of improved super resolve images. Another, approach has lead to the development of a window management scheme for further improvement. The cleaner, from a noise and structural point-of-view, the composite SR image is the more favorable it is to high-sharpening. Mapping into a transform space greatly reduces the correlation complexity which makes it easier to realize the complete algorithm into hardware. We have implemented this system into a real-time architecture. The hardware configuration is composed of an FPGA and supporting processor.

Sharpening

10.1117/12.719851

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Citations (1)

<title>A chopper-free measurement-based parametric-fitting nonuniformity correction system</title>

Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (2004)

Haiwen Chen Felix Fontan Teresa L. P. Olson

In traditional designs of a NUC (non-uniformity correction) system, a rotating chopper-wheel (or a blurring/deform lens) is used to separate the outside scene and the inside FPN (fixed pattern noise) on the FPA (focal plane array). To design a NUC system removing the chopper-wheel (chopper-free) and its control electronics and hardware will not only considerably reduce the cost, but also require less space to fit the NUC system. In this paper, we describe a recently developed CF (chopper-free) NUC system. This system is simpler to build, costs less, and requires less space, as compared with traditional designs.

Chopper

Free space

10.1117/12.541363

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TBB (true best base) searching method and its applications

Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (2003)

Haiwen Chen Teresa L. P. Olson

The binary-tree best base (BTBB) searching method developed by Coifman and Wickerhauser is well known and widely used in wavelet packet applications. However, the requirement that the base vectors be chosen from either a parent or its directly related children in the binary-tree structure is a limitation because it doesn't search all possible orthogonal bases and therefore may not provide a optimal result. We have recently found that the set of all possible orthogonal bases in a wavelet packet is much larger than the set searched by the BTBB method. Based on this observation, we have developed the true best base (TBB) searching method - a new way to search the best base among a much larger set of orthogonal bases. In this paper, we show that considerable improvements in signal compression, de-noising, and time-frequency analysis can be achieved using the new TBB method. Furthermore, we show that the TBB method can be used as a searching engine to extract the local discriminant base (LDB) for feature extraction and signal/object classification, and we compare the performances of the LDBs extracted by the TBB and BTBB.

Base (topology)

Tree (set theory)

Feature (linguistics)

10.1117/12.502397

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Citations (1)

Wavelet detector for model-based imaging

Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (1997)

Teresa L. P. Olson Leon H. Sibul

This paper investigates the model-based aspects of the estimator correlator (EC) detector in the wavelet domain. Ideas from group theory are used to develop and describe underlying properties. By applying group representation theory to the detector development, insight into the optimal processing structure of the EC is gained. In the absence of a priori model information, the EC detector reduces to the wavelet domain or matched filter detector. With a priori information incorporated into the model, the EC becomes a weighted wavelet detector. Implementing the EC in the wavelet domain provides range-Doppler (wavelet) images at different stages of processing. This allows the opportunity to simultaneously exploit the vast body of knowledge of wavelets, scattering function theory, and range-Doppler processing techniques. Ambiguity function theory is used to evaluate performance capabilities of these wavelet-based detectors using various narrowband and wideband transmit signals. This paper shows that the weighted wavelet detector serves as a classifier as well by using the scattering function model as a basis for pattern recognition. An example of the effectiveness of the weighted wavelet detector with narrowband and wideband signals for a multi- highlight image model is presented and results are discussed.

Wideband

Narrowband

10.1117/12.280792

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Citations (0)

A Real-Time Multistage IR Image-Based Tracker

Teresa L. P. Olson Carl W. Sanford

10.21236/ada385504

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Citations (6)

New aggressive way to search for the best base in wavelet packets

IEE Proceedings - Vision Image and Signal Processing (2005)

H.-W. Chen Teresa L. P. Olson

The binary-tree best base (BTBB) searching method developed by Coifman and Wickerhauser is well known and widely used in wavelet packet applications. However, the requirement that the base vectors be chosen from either a parent or its directly related children in the binary-tree structure is a limitation because it does not search all possible orthogonal bases and therefore may not provide an optimal result. We have recently found that the set of all possible orthogonal bases in a wavelet packet is much larger than the set searched by the BTBB method. Based on this observation, we have developed a Tree-elimination based Best orthogonal Base (TBB) searching method: a new way to search the best base among a much larger set of orthogonal bases. In this paper, we show that considerable improvements in signal compression, time-frequency analysis, and feature extraction may be achieved using the newly developed TBB method. Similar to the matching pursuit method, TBB uses an aggressive searching method. However, its computation is faster than that of the orthogonal matching pursuit (MP) searching method.

Base (topology)

Tree (set theory)

Orthogonal wavelet

Feature (linguistics)

10.1049/ip-vis:20045136

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Citations (2)

<title>Target recognition and tracking in an infrared system</title>

Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (2001)

Harry C. Lee Teresa L. P. Olson Robert Stanfill

In order to demonstrate lock-on-after-launch (LOAL) capability, imaging infrared missile systems of the future require the ability to autonomously identify and track targets of interest. A robust algorithm architecture must have the flexibility to accommodate the fluid system requirements that drive its design. This paper describes a method to autonomously acquire and track an extended range target through its entire flight scenario. A proven automatic target recognition (ATR) approach is used to detect and identify targets of interest, separating them into non-targets and clutter. The methodology uses a down selection strategy to nominate targets for terminal track. Once nominated, a Weighted Edge Tracker (WET) is employed. The tracker relies upon correlations of appropriately weighted edge directions from frame-to-frame images and reference templates. This combination of automatic target recognition and terminal tracking provides a sophisticated yet simple approach to many challenging long range tracking problems.

Automatic Target Recognition

Tracking (education)

Template

10.1117/12.438036

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Citations (2)