DocumentCode
834707
Title
Automatic multilevel thresholding for image segmentation by the growing time adaptive self-organizing map
Author
Shah-Hosseini, Hamed ; Safabakhsh, Reza
Author_Institution
Comput. Eng. Dept., Amirkabir Univ. of Technol., Tehran, Iran
Volume
24
Issue
10
fYear
2002
fDate
10/1/2002 12:00:00 AM
Firstpage
1388
Lastpage
1393
Abstract
In this paper, a Growing TASOM (Time Adaptive Self-Organizing Map) network called "GTASOM" along with a peak finding process is proposed for automatic multilevel thresholding. The proposed GTASOM is tested for image segmentation. Experimental results demonstrate that the GTASOM is a reliable and accurate tool for image segmentation and its results outperform other thresholding methods.
Keywords
image segmentation; self-organising feature maps; GTASOM; Growing TASOM; automatic multilevel thresholding; growing time adaptive self-organizing map; image segmentation; peak finding process; Adaptive systems; Clustering algorithms; Histograms; Image segmentation; Lattices; Neurons; Principal component analysis; Process design; Quantization; Testing;
fLanguage
English
Journal_Title
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher
ieee
ISSN
0162-8828
Type
jour
DOI
10.1109/TPAMI.2002.1039209
Filename
1039209
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