DocumentCode
298438
Title
Segmentation driven by an iterative pairwise mutually best merge criterion
Author
Baraldi, A. ; Parmiggiani, F.
Author_Institution
IMGA, CNR, Modina, Italy
Volume
1
fYear
34881
fDate
10-14 Jul1995
Firstpage
89
Abstract
The iterative pairwise mutually best merge (IPMBM) segmentation algorithm extracts image regions characterized by low within-segment variance, IPMBM employs a new metric, called normalized vector distance (NVD), to perform a normalized comparison between a pair of multivalued vectors. IPMBM is robust and easy to use since only two parameters, both having an intuitive physical meaning, must be user-defined. Experimental results demonstrate that IPMBM is effective in real applications
Keywords
feature extraction; image segmentation; iterative methods; IPMBM; image regions; iterative pairwise mutually best merge criterion; multivalued vectors; normalized vector distance; segmentation; within-segment variance; Algorithm design and analysis; Convergence; Equations; Image segmentation; Iterative algorithms; Layout; Merging; Reactive power; Robustness;
fLanguage
English
Publisher
ieee
Conference_Titel
Geoscience and Remote Sensing Symposium, 1995. IGARSS '95. 'Quantitative Remote Sensing for Science and Applications', International
Conference_Location
Firenze
Print_ISBN
0-7803-2567-2
Type
conf
DOI
10.1109/IGARSS.1995.519656
Filename
519656
Link To Document