Journal
COMPUTER VISION - ECCV 2016, PT VII
Volume 9911, Issue -, Pages 101-117Publisher
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-319-46478-7_7
Keywords
Image matching; Correspondence analysis; Statistical optical flow; Guided matching; Ground truth for feature matching
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In this paper, we present a novel algorithm for reliable and fast feature matching. Inspired by recent efforts in optimizing the matching process using geometric and statistical properties, we developed an approach which constrains the search space by utilizing spatial statistics from a small subset of matched and filtered correspondences. We call this method Guided Matching based on Statistical Optical Flow (GMbSOF). To ensure broad applicability, our approach works on high dimensional descriptors like SIFT but also on binary descriptors like FREAK. To evaluate our algorithm, we developed a novel method for determining ground truth matches, including true negatives, using spatial ground truth information of well known datasets. Therefore, we evaluate not only with precision and recall but also with accuracy and fall-out. We compare our approach in detail to several relevant state-of-the-art algorithms using these metrics. Our experiments show that our method outperforms all other tested solutions in terms of processing time while retaining a comparable level of matching quality.
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