An image smoothing operator for fast and accurate scale space approximation

Abstract

Gussian image smoothing is a fundamental operation in the extraction of scale-invariant feature points. Its computation, however, can be too expensive in some resource-constrained scenarios. Alternative solutions such as the box filter can be computed more efficiently, at the cost of a loss in feature repeatibility under some conditions. In this paper we propose a fast and accurate image smoothing operator based on integral images. It has the same order of computational complexity as the box filter, but provides much more accurate visual results and improved keypoint repeatability, which is confirmed in a feature detection scenario using SIFT features.