Localization accuracy of interest point detectors with different scale space representations

The detection of scale invariant image features is a fundamental task for computer vision applications like object recognition or re-identification. Features are localized by computing extrema of the gradients in the Laplacian of Gaussian (LoG) scale space. The most popular detector for scale invariant features is the SIFT detector which uses the Difference of Gaussians (DoG) pyramid as an approximation of the LoG. Recently, the alternative interest point (ALP) detector demonstrated its strength in fast computation on highly parallel architectures like the GPU. It uses the LoG scale space representation for the localization of interest points. This paper evaluates the localization accuracy of ALP in comparison to SIFT. By using synthetic images, it is demonstrated that both localization approaches show a systematic error which is dependent on the subpixel position of the feature. The error increases with the scale of the detected feature. However, using the LoG instead of the DoG representation reduces the maximum systematic error by 77 %. For the evaluation with natural images, benchmark data sets are used. The repeatability criterion evaluates the accuracy of the detectors. The LoG based detector results in up to 16 % higher repeatability. The comparisons are completed with a reference feature localization which uses a signal based approach for the gradient approximation. Based on this approach, a new feature selection criterion is proposed.