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We present a wavelet-based, high performance, hierarchical scheme for image matching which includes (1) dynamic detection of interesting points as feature points at different levels of subband images via the wavelet transform, (2) adaptive thresholding selection based on compactness measures of fuzzy sets in image feature space, and (3) a guided searching strategy for the best matching from coarse level to fine level. In contrast to the traditional parallel approaches which rely on specialized parallel machines, we explored the potential of distributed systems for parallelism. The proposed image matching algorithms were implemented on a network of workstation clusters using parallel virtual machine (PVM). The results show that our wavelet-based hierarchical image matching scheme is efficient and effective for object recognition
Published in:
Image Processing, IEEE Transactions on
(Volume:9
,
Issue:
9
)
Date of Publication: Sep 2000
- Page(s):
- 1547 - 1559
- ISSN :
- 1057-7149
- INSPEC Accession Number:
- 6701651
- Digital Object Identifier :
- 10.1109/83.862635
- Product Type:
- Journals & Magazines
- Date of Current Version :
- 06 August 2002
- Issue Date :
- Sep 2000
- Sponsored by :
- IEEE Signal Processing Society
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