A wavelet-based coarse-to-fine image matching scheme in a parallel virtual machine environment
We present a wavelet-based, high performance, hierarchical scheme for image matching which includes I) dynamic detection of interesting points as feature points at different levels of subband images via 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.
image matching wavelet transform parallel virtual machine (pvm) feature detection guided matching hausdorff distance image pyramid parallel algorithms