DocumentCode :
2145580
Title :
Watershed Segmentation Using Curvelet and Morphological Filtering
Author :
Chen, Dongfang ; Xu, Tao
Author_Institution :
Coll. of Comput. Sci. & Technol., Wuhan Univ. of Sci. & Technol., Wuhan, China
fYear :
2009
fDate :
17-19 Oct. 2009
Firstpage :
1
Lastpage :
5
Abstract :
An improved segmentation method is proposed in this paper for metallographic images, especially those objects surrounded with complex texture. If only watershed algorithm is used for the segmentation of an image, the over-segmentation problem will be serious. To solve this, we proposed a new approach. In the method, we take the curvelet transform to denoise initial images by thresholding the different scales of coefficients firstly. Afterward the improved morphological Top-Bottom filter is used for filtering the produced images, and then watershed algorithm is applied lastly. After segmenting, we do some simple progresses to remove some separated holes. The results demonstrate that combining curvelet and improved top-bottom filter can help us to get more accurate segmentation.
Keywords :
filtering theory; image denoising; image segmentation; complex texture; curvelet transform; filtering curvelet; image denoising; metallographic images; morphological filtering; top-bottom filter; watershed segmentation; Computer science; Costs; Educational institutions; Filtering algorithms; Image segmentation; Merging; Morphology; Noise reduction; Wiener filter; Wrapping;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image and Signal Processing, 2009. CISP '09. 2nd International Congress on
Conference_Location :
Tianjin
Print_ISBN :
978-1-4244-4129-7
Electronic_ISBN :
978-1-4244-4131-0
Type :
conf
DOI :
10.1109/CISP.2009.5303723
Filename :
5303723
Link To Document :
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