Wide line detector and its applications

Pao Yue-kong Library Electronic Theses Database

Wide line detector and its applications


Author: Liu, Li
Title: Wide line detector and its applications
Degree: Ph.D.
Year: 2007
Subject: Hong Kong Polytechnic University -- Dissertations.
Image processing.
Pattern recognition systems.
Department: Dept. of Computing
Pages: ix, 145 p. : ill. ; 30 cm.
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b2189813
URI: http://theses.lib.polyu.edu.hk/handle/200/1580
Abstract: Lines provide important information in images and line detection is crucial in many applications. However, most of the existing algorithms focus only on the extraction of line positions, ignoring line thickness. In this thesis, we aim to address this issue. We describe a general method for wide line detection and apply this method to solve two practical line detection problems. In the first section, we propose a novel wide line detector to extract the line entirely. In contrast with the traditional directional-derivative-based edge and line extraction method, our wide line detector is based on isotropic nonlinear filtering without any derivatives and consequently is more robust to noise. We develop an approach for dynamic selection of parameters of the proposed wide line detector. We also introduce a general scheme for analyses of this wide line detector further. A sequence of tests is conducted on a variety of image samples. The experimental results demonstrate that the proposed wide line detector works very well for a range of images containing lines of different widths, especially for those where the width of lines varies greatly and where the lines run close together or cross each other. We then address the first application of our wide line detector: palm-line based palmprint recognition. We present a novel palm-line feature extraction method for personal identification. As compared to previous work on palm line extraction, the proposed wide line detector-based method extracts not only structure features but also strength features of palm lines. We introduce a translation-invariant scheme for palm-line feature matching. We also develop an experimental scheme to find out the optimal combination of parameters for the proposed palm-line feature extraction method. An extensive test is conducted on a public palmprint database. Experimental results show that the performance of the proposed palm-line feature extraction method is comparable with the state-of-the-art algorithms of palmprint identification and thereby palm-line features can be used to recognize palmprints. Finally, we for the first time attempt to extract tongue cracks, one of pathological features in tongue diagnosis. We propose a framework for automatic tongue crack extraction. We derive a tongue crack detection scheme based on the wide line detector which extracts the whole of the line by employing an isotropic nonlinear filter. The wide line detector describes the relationship between the size of the isotropic filter, i.e. the scale of this detector, and the width of detected lines. Due to the large range of widths of tongue cracks, the maximum widths of cracks vary greatly with different tongue images and consequently the sizes of the isotropic filters should be very different. To implement the proposed scheme totally automatically, we design an adaptive algorithm of line width estimation. The proposed scheme has been tested on a set of typical cracked tongue samples and our experimental results show the promising performance of our automatic tongue crack extraction scheme.

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