Unsupervised image segmentation using robust clustering
Unsupervised image segmentation is widely recognized as a difficult and challenging computer vision problem, in which, the fundamental issue is that of determination of the number and boundaries of different regions without a priori knowledge. There are two parts of work in this thesis. In the firs...
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sg-ntu-dr.10356-132542023-07-04T16:33:49Z Unsupervised image segmentation using robust clustering Pan, Hong Li, Stan Ziqing School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing Unsupervised image segmentation is widely recognized as a difficult and challenging computer vision problem, in which, the fundamental issue is that of determination of the number and boundaries of different regions without a priori knowledge. There are two parts of work in this thesis. In the first part, image segmentation is addressed as detecting the boundaries between different regions, where edge flow is successfully investigated and implemented in solving both texture and color image segmentation. Edge flow is used to identify and integrate the direction of change in various types of image attributes at each image location and segment the image using a heuristic post-processing. Master of Engineering 2008-08-08T00:45:27Z 2008-10-20T07:21:43Z 2008-08-08T00:45:27Z 2008-10-20T07:21:43Z 1999 1999 Thesis http://hdl.handle.net/10356/13254 en 115 p. application/pdf |
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Asia |
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Singapore Singapore |
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DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing |
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DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing Pan, Hong Unsupervised image segmentation using robust clustering |
| description |
Unsupervised image segmentation is widely recognized as a difficult and challenging computer vision problem, in which, the fundamental issue is that of determination of the number and boundaries of different regions without a priori knowledge.
There are two parts of work in this thesis. In the first part, image segmentation is addressed as detecting the boundaries between different regions, where edge flow is successfully investigated and implemented in solving both texture and color image segmentation. Edge flow is used to identify and integrate the direction of change in various types of image attributes at each image location and segment the image using a heuristic post-processing. |
| author2 |
Li, Stan Ziqing |
| author_facet |
Li, Stan Ziqing Pan, Hong |
| format |
Theses and Dissertations |
| author |
Pan, Hong |
| author_sort |
Pan, Hong |
| title |
Unsupervised image segmentation using robust clustering |
| title_short |
Unsupervised image segmentation using robust clustering |
| title_full |
Unsupervised image segmentation using robust clustering |
| title_fullStr |
Unsupervised image segmentation using robust clustering |
| title_full_unstemmed |
Unsupervised image segmentation using robust clustering |
| title_sort |
unsupervised image segmentation using robust clustering |
| publishDate |
2008 |
| url |
http://hdl.handle.net/10356/13254 |
| _version_ |
1772825259175575552 |