FEATURE-DRIVEN MAXIMALLY STABLE EXTREMAL REGIONS
Pedro Martins, Carlo Gatta, P. Carvalho · 2012
The high repeatability of Maximally Stable Extremal Regions (MSERs) on structured images along with their suitability to be combined with either photometric or shape descriptors to solve image matching problems have contributed to establish the MSER detector as one of the most prominent affine covariant detectors. However, the so-called affine covariance that characterizes MSERs relies on the assumption that objects possess smooth boundaries, a premiss that is not always valid. We introduce an alternative domain for MSER detection in which boundary-related features are highlighted and simultaneously delineated under smooth transitions. Detection results on common benchmarks show improvements that are discussed.