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A wavelet-based coarse-to-fine image matching scheme in a parallel virtual machine environment

机译:并行虚拟机环境中基于小波的粗糙到精细图像匹配方案

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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.
机译:我们提出一种基于小波的,高性能的图像匹配分层方案,该方案包括(1)通过小波变换动态检测感兴趣点作为子带图像不同级别上的特征点,(2)基于压缩量的自适应阈值选择图像特征空间中的模糊集,以及(3)从粗糙级别到精细级别的最佳匹配的指导搜索策略。与依赖于专用并行机的传统并行方法相反,我们探索了分布式系统实现并行的潜力。所提出的图像匹配算法是使用并行虚拟机(PVM)在工作站集群的网络上实现的。结果表明,我们的基于小波的分层图像匹配方案是有效的和有效的目标识别。

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