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|Title:||Improved techniques for automatic image segmentation|
|Source:||IEEE transactions on circuits and systems for video technology, Dec. 2001, v. 11, no. 12, p.1273-1280 How to cite?|
|Journal:||IEEE transactions on circuits and systems for video technology|
|Abstract:||Mathematical morphology is very attractive for automatic image segmentation because it efficiently deals with geometrical descriptions such as size, area, shape, or connectivity that can be considered as segmentation-oriented features. This paper presents an image-segmentation system based on some well-known strategies. The segmentation process is divided into three basic steps, namely: simplification, marker extraction, and boundary decision. Simplification, which makes use of area morphology, removes unnecessary information from the image to make it easy to segment. Marker extraction identifies the presence of homogeneous regions. A new marker-extraction design is proposed in this paper. It is based on both luminance and color information. The goal of boundary decision is to precisely locate the boundary of regions detected by the marker extraction. This decision is based on a region-growing algorithm which is a modified watershed algorithm. A new color distance is also defined for this algorithm. In both marker extraction and boundary decision, color measurement is used to replace grayscale measurement and L*a*b* color space is used to replace the more straightforward spaces such as the RGB color space and YUV color space.|
|Rights:||© 2001 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
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