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Full-Text Articles in Engineering
Semi-Automatic Road Extraction From Aerial Images, Somkait Udomhunsakul, Samuel Peter Kozaitis, Uthai Thai Sritheeravirojana
Semi-Automatic Road Extraction From Aerial Images, Somkait Udomhunsakul, Samuel Peter Kozaitis, Uthai Thai Sritheeravirojana
Electrical Engineering and Computer Science Faculty Publications
In this work, we proposed a method to detect roads in aerial imagery. In our approach, we applied the wavelet transform in a multiresolution sense by forming the products of wavelet coefficients at the different scales to locate and identify roads. After detecting possible road pixels, we used a graph searching algorithm to identify roads. We found that our approach leads to an effective method to form the basis of a road extraction approach.
Line Detection Using Wavelet Filters, Somkait Udomhunsakul, Samuel Peter Kozaitis, Uthai Thai Sritheeravirojana, A. Khempila
Line Detection Using Wavelet Filters, Somkait Udomhunsakul, Samuel Peter Kozaitis, Uthai Thai Sritheeravirojana, A. Khempila
Electrical Engineering and Computer Science Faculty Publications
We proposed a new line detection method in noisy images using Mexican hat wavelet filters. In our approach, we applied the wavelet transform in a multiresolution sense by forming the products of wavelet coefficients at the different scales to locate and identify lines at different scales. In addition, we also considered shifting line locations through multiple scales for robust line detection in the presence of noise. We found that our approach leads to an effective method to form the basis of a line detection approach.
Linear Feature Detection Using Multiresolution Wavelet Filters, Samuel Peter Kozaitis, Somkait Udomhunsakul, Rufus H. Cofer, A. Agarawal, Shuwu Song
Linear Feature Detection Using Multiresolution Wavelet Filters, Samuel Peter Kozaitis, Somkait Udomhunsakul, Rufus H. Cofer, A. Agarawal, Shuwu Song
Electrical Engineering and Computer Science Faculty Publications
We detected roads in aerial imagery based on multiresolution linear feature detection. Our method used the products of wavelet coefficients at several scales to identify and locate linear features. After detecting possible road pixels, we used a shortest-path algorithm to identify roads. The multiresolution approach effectively increased the size of the region we examined when looking for possible road pixels and reduced the effect of noise. We found that our approach leads to an effective method for detecting roads in aerial imagery.
Improved Wavelet-Based Multiresolution Edge Detection In Noisy Images, Yunwoo Lee, Samuel Peter Kozaitis
Improved Wavelet-Based Multiresolution Edge Detection In Noisy Images, Yunwoo Lee, Samuel Peter Kozaitis
Electrical Engineering and Computer Science Faculty Publications
We used the multiresolution property of the discrete wavelet transform to detect edges in noisy images. In our approach, we used wavelets corresponding to 1 and 2 derivatives to generate noisy wavelet coefficients. Then, we compared the wavelet COeffiCientS as a function of scale to reduce the effects of noise. In addition, our approach considered the change in edge position as a function of scale. We analyzed l..D experimental results and compar1 2..D results ofnoisy images to a more common edge detection method. Our results lead to improved edge detection resutts in noisy images.