Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
