A Sparse Curvature-Based Detector of Affine Invariant Blobs

Usually, state-of-the-art interest point detectors tend to over-represent the local image structures associating several interest points for each local image structure. This fact avoids interest points to clearly stand out against its neighborhood, losing the ability to clearly describe the global uniqueness of each local image structure. In order to solve this problem we propose a sparse affine invariant blob detector, which tries to describe each blob structure with a single interest point. The proposed detector is carried out in two stages: an initial stage, where a set of scale invariant interest points are located by means of the idea of blob movement and blob evolution (creation, annihilation and merging) along different scales by using a precise description of the image provided by the Gaussian curvature, providing a global bottom-up estimation of the image structure. During the second stage, the shape and location of each scale invariant interest point is refined by fitting an anisotropic Gaussian function, which minimizes the error with the underlying image and simultaneously estimates both the shape and location, by means of a non-linear least squares approach. A comparative evaluation of affine invariant detectors is presented, showing that our approach outperforms state-of-the-art affine invariant detectors in terms of precision and recall, and obtains a similar performance to that of the best ones in terms of repeatability and matching. In addition we demonstrate that our detector does not over-represent blob structures and provides a sparse detection that improves distinctiveness and reduces drastically the computational cost of matching tasks. In order to verify the accuracy and the reduction in the computational cost we have evaluated our detector in image registration tasks.