CoMIC: Good features for detection and matching at object boundaries
This addresses a specific problem in computer vision for applications like stereo matching and structure from motion, offering incremental improvements over existing methods.
The paper tackles the instability of traditional feature points at object boundaries by detecting corners on stable iso-intensity curves, which improves matching accuracy in boundary regions while maintaining comparable performance in interior regions.
Feature or interest points typically use information aggregation in 2D patches which does not remain stable at object boundaries when there is object motion against a significantly varying background. Level or iso-intensity curves are much more stable under such conditions, especially the longer ones. In this paper, we identify stable portions on long iso-curves and detect corners on them. Further, the iso-curve associated with a corner is used to discard portions from the background and improve matching. Such CoMIC (Corners on Maximally-stable Iso-intensity Curves) points yield superior results at the object boundary regions compared to state-of-the-art detectors while performing comparably at the interior regions as well. This is illustrated in exhaustive matching experiments for both boundary and non-boundary regions in applications such as stereo and point tracking for structure from motion in video sequences.