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Full-Text Articles in Engineering
Spectrally Sensitive Wavelet Analysis Of Multispectral Imagery For Object Detection, Samuel Peter Kozaitis, Ty Olmstead
Spectrally Sensitive Wavelet Analysis Of Multispectral Imagery For Object Detection, Samuel Peter Kozaitis, Ty Olmstead
Electrical Engineering and Computer Science Faculty Publications
We used a 3D wavelet denoising method to reduce noise from multispectral imagery so that small objects may be more readily detected. Our approach exploits the correlation between bands typically present in multispectral imagery. Using our approach, the resulting image generally consists of a weighted sum of both spectral bands and spatial frequencies. We found that we could generally increase the SNR of a multispectral image more than if the spectral bands were processed independently.
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 …