Partial denoising boundary image matching using time-series matching techniques

Removing noise, called denoising, is an essential factor for achieving the intuitive and accurate results in boundary image matching. This paper deals with a partial denoising problem that tries to allow a limited amount of noise embedded in boundary images. To solve this problem, we first define partial denoising time-series that can be generated from an original image time-series by removing a variety of partial noises. We then propose an efficient mechanism that quickly obtains those partial denoising time-series in the time-series domain rather than the image domain. Next, we present the partial denoising distance, which is the minimum distance from a query time-series to all possible partial denoising time-series generated from a data time-series. We then use this partial denoising distance as a similarity measure in boundary image matching. Using the partial denoising distance, however, incurs a severe computational overhead since there are a large number of partial denoising time-series to be considered. To solve this problem, we derive a tight lower bound for the partial denoising distance and formally prove its correctness. We also propose partial denoising boundary image matching exploiting the partial denoising distance in boundary image matching. Through extensive experiments, we finally show that our lower bound-based approach improves search performance by up to an order of magnitude in partial denoising-based boundary image matching.