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Full-Text Articles in Engineering
Novel Wavelength Diversity Technique For High-Speed Atmospheric Turbulence Compensation, William W. Arrasmith, Sean F. Sullivan
Novel Wavelength Diversity Technique For High-Speed Atmospheric Turbulence Compensation, William W. Arrasmith, Sean F. Sullivan
Electrical Engineering and Computer Science Faculty Publications
The defense, intelligence, and homeland security communities are driving a need for software dominant, real-time or near-real time atmospheric turbulence compensated imagery. The development of parallel processing capabilities are finding application in diverse areas including image processing, target tracking, pattern recognition, and image fusion to name a few. A novel approach to the computationally intensive case of software dominant optical and near infrared imaging through atmospheric turbulence is addressed in this paper. Previously, the somewhat conventional wavelength diversity method has been used to compensate for atmospheric turbulence with great success. We apply a new correlation based approach to the wavelength …
Parallel Implementation Of High-Speed, Phase Diverse Atmospheric Turbulence Compensation Method On A Neural Network-Based Architecture, William W. Arrasmith, Sean F. Sullivan
Parallel Implementation Of High-Speed, Phase Diverse Atmospheric Turbulence Compensation Method On A Neural Network-Based Architecture, William W. Arrasmith, Sean F. Sullivan
Electrical Engineering and Computer Science Faculty Publications
Phase diversity imaging methods work well in removing atmospheric turbulence and some system effects from predominantly near-field imaging systems. However, phase diversity approaches can be computationally intensive and slow. We present a recently adapted, high-speed phase diversity method using a conventional, software-based neural network paradigm. This phase-diversity method has the advantage of eliminating many time consuming, computationally heavy calculations and directly estimates the optical transfer function from the entrance pupil phases or phase differences. Additionally, this method is more accurate than conventional Zernike-based, phase diversity approaches and lends itself to implementation on parallel software or hardware architectures. We use computer …
Direct, Object Brightness Estimation From Atmospheric Turbulence Degraded Images Using A New, High-Speed, Modified Phase Diversity Method, William W. Arrasmith
Direct, Object Brightness Estimation From Atmospheric Turbulence Degraded Images Using A New, High-Speed, Modified Phase Diversity Method, William W. Arrasmith
Electrical Engineering and Computer Science Faculty Publications
The well known phase diversity technique has long been used as a premier passive imaging method to mitigate the degrading effects of atmospheric turbulence on incoherent optical imagery. Typically, an iterative, slow method is applied that uses the Zernike basis set and 2-D Fourier transforms in the reconstruction process. In this paper, we demonstrate a direct method for estimating the un-aberrated object brightness from phase or phase difference estimates that 1) does not require the use of the Zernike basis set or the intermediate determination of the generalized pupil function, 2) directly determines the optical transfer function without the requirement …
Unconventional Optical Imaging Using A High Speed, Neural Network Based Smart Sensor, William W. Arrasmith
Unconventional Optical Imaging Using A High Speed, Neural Network Based Smart Sensor, William W. Arrasmith
Electrical Engineering and Computer Science Faculty Publications
The advancement of neural network methods and technologies is finding applications in many fields and disciplines of interest to the defense, intelligence, and homeland security communities. Rapidly reconfigurable sensors for real or near-real time signal or image processing can be used for multi-functional purposes such as image compression, target tracking, image fusion, edge detection, thresholding, pattern recognition, and atmospheric turbulence compensation to name a few. A neural network based smart sensor is described that can accomplish these tasks individually or in combination, in real-time or near real-time. As a computationally intensive example, the case of optical imaging through volume turbulence …
Joint-Transform Correlator Architecture For Wavelet Feature Extraction, Samuel Peter Kozaitis, Mark A. Getbehead, Wesley E. Foor
Joint-Transform Correlator Architecture For Wavelet Feature Extraction, Samuel Peter Kozaitis, Mark A. Getbehead, Wesley E. Foor
Electrical Engineering and Computer Science Faculty Publications
A version of an image consisting of multiple wavelet scales allows for more flexible feature extraction when compared to the use of one wavelet scale. We proposed an imaging system based on a multiple-input joint-transform correlator, that could be used for multiple wavelet-scale analysis of an input image. Given a single input image and wavelet, for m wavelet scales, m versions of the wavelet and m copies of the input image were generated using conventional optics that are used as inputs to a joint wavelet-transform correlator. The output consisted of 4 m - 1 correlation results, one of which is …
Multispectral Image Feature Extraction By The Joint Wavelet-Transform Correlator, Samuel Peter Kozaitis, Mark A. Getbehead, Wesley E. Foor
Multispectral Image Feature Extraction By The Joint Wavelet-Transform Correlator, Samuel Peter Kozaitis, Mark A. Getbehead, Wesley E. Foor
Electrical Engineering and Computer Science Faculty Publications
A multispectral version of an image consisting of multiple wavelet components allows for more flexible feature extraction when compared to the use of one wavelet component. We showed how a multiple-input joint wavelet- transform correlator could be used for multispectral analysis of an input image. For m wavelet scales, m versions of the wavelet and m copies of the input image were generated using conventional optics that are used as inputs to a joint wavelet-transform correlator. The output consisted of 4m-1 correlation results, one of which is the desired output. The space-bandwidth product of the system is the same as …
Optical Image Analysis Using Fractal Techniques, Samuel Peter Kozaitis, Harold Gregory Andrews, Wesley E. Foor
Optical Image Analysis Using Fractal Techniques, Samuel Peter Kozaitis, Harold Gregory Andrews, Wesley E. Foor
Electrical Engineering and Computer Science Faculty Publications
Using an optical technique, we classified images of natural terrain based on their fractal dimension. We calculated the fractal dimension from an optically generated power spectrum obtained with a magneto-optic spatial light modulator (SLM). By using the fractal dimension to classify images of natural terrain, our post processing was simpler that when a ring-wedge detector was used.
Feature-Based Correlation Filters For Object Recognition, Samuel Peter Kozaitis, Wesley E. Foor
Feature-Based Correlation Filters For Object Recognition, Samuel Peter Kozaitis, Wesley E. Foor
Electrical Engineering and Computer Science Faculty Publications
Using an optical correlator, we experimentally evaluated a binary phase-only filter (BPOF) designed to recognize objects not in the training set used to design the filter. Such a filter is essential for recognizing objects from actual sensors. We used an approach that is as descriptive as a BPOF yet robust to object and background variations of an unknown or nonrepeatable type. We generated our filter by comparing the values of spatial frequencies of a training set. Our filter was easily calculated and offered potentially superior performance to other correlation filters.
Feature-Based Correlation Filters For Distortion Invariance, Samuel Peter Kozaitis, Robert Petrilak, Wesley E. Foor
Feature-Based Correlation Filters For Distortion Invariance, Samuel Peter Kozaitis, Robert Petrilak, Wesley E. Foor
Electrical Engineering and Computer Science Faculty Publications
In an optical correlator, binary phase-only filters (BPOFs) that recognize objects that vary in a nonrepeatable way are essential for recognizing objects from actual sensors. An approach is required that is as descriptive as a BPOF yet robust to object and background variations of an unknown or nonrepeatable type. We developed a BPOF that was more robust than a synthetic discriminant function (SDF) filter. This was done by creating a filter that retained the invariant features of a training set. By simulation, our feature-based filter offered a range of performance by setting a parameter to different values. As the value …