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Proceedings Paper

A modified wavelet-transformation-based-method of linear object extraction
Author(s): Tieling Chen
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Paper Abstract

Edges can be characterized through the evolution of a wavelet transformation at different scale levels. A two-dimensional wavelet transformation of a given image is proportional to the gradient of a corresponding smoothed image. Each component of a normal two-dimensional wavelet transformation is in fact a one-dimensional wavelet transformation in one variable followed by a smoothing process in the other variable. The modified wavelet transformation of the given image gets rid of the smoothing process in each component since the magnitude of the wavelet transformation in the center part of a linear object may be increased by the big magnitudes of the wavelet transformation along the edges if the smoothing process is adopted, which makes it hard to isolate the centerline of the linear object. The modified wavelet transformation gives high magnitudes along the edges and low magnitudes in the center part of the linear objects in the wavelet-transformed image. In the image showing the magnitude of the wavelet transformation, there are high ridges along the edges of the linear objects and low grey level valleys bounded by the ridges. A suitable threshold can be used to extract the low grey level part of the image, such that the center parts of the linear objects are included. Since they are separated from other objects, they can be easily extracted in a post-processing.

Paper Details

Date Published: 17 February 2006
PDF: 8 pages
Proc. SPIE 6064, Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning, 60640P (17 February 2006); doi: 10.1117/12.643612
Show Author Affiliations
Tieling Chen, Univ. of South Carolina (United States)

Published in SPIE Proceedings Vol. 6064:
Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning
Nasser M. Nasrabadi; Edward R. Dougherty; Jaakko T. Astola; Syed A. Rizvi; Karen O. Egiazarian, Editor(s)

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