Object Contour Extraction Based on Intensity and Texture Information

In this paper, we propose a new method to extract object contour in a given gray-level image, whose foreground and background are statistically homogeneous and different. Firstly, the image for contour extraction is decomposed by discrete wavelet transform, and the high-pass and low-pass components...

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Bibliographic Details
Main Authors:	Qizhi Xu, Lei Hu, Bo Li, Yangke Liu
Format:	Conference Proceeding
Language:	eng
Subjects:	Active contours Data mining Discrete wavelet transforms Dynamic range Image edge detection Level set Noise level Object detection Remote sensing Solid modeling
Online Access:	Get full text
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Summary:	In this paper, we propose a new method to extract object contour in a given gray-level image, whose foreground and background are statistically homogeneous and different. Firstly, the image for contour extraction is decomposed by discrete wavelet transform, and the high-pass and low-pass components are used to form intensity and texture energy respectively. Secondly, a minimal partition function, which combines intensity, texture and contour length energy, is made to model the contour extraction problem. Finally, the model is formulated in terms of level set function to obtain a numerical solution. Experiments have been performed on synthetic and remote-sensing images, and the results demonstrated that our method can adaptively use intensity and texture information to accurately extract object contour.