Noise Control Boundary Image Matching Using Time-Series Moving Average Transform

摘要

To achieve the noise reduction effect in boundary image matching, we exploit the moving average transform of time-series matching. Our motivation is that using the moving average transform we may reduce noise in boundary image matching as in time-series matching. We first propose a new notion of k-order image matching, which applies the moving average transform to boundary image matching. A boundary image can be represented as a sequence in the time-series domain, and our k-order image matching identifies similar boundary images in this time-series domain by comparing the k-moving average transformed sequences. Next, we propose an index-based method that efficiently performs k-order image matching on a large image database, and prove its correctness. Moreover, we present its index building and k-order image matching algorithms. Experimental results show that our k-order image matching exploits the noise reduction effect, and our index-based method outperforms the sequential scan by one or two orders of magnitude.