Title :
Segmentation driven by an iterative pairwise mutually best merge criterion
Author :
Baraldi, A. ; Parmiggiani, F.
Author_Institution :
IMGA, CNR, Modina, Italy
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;
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
DOI :
10.1109/IGARSS.1995.519656
