GPU computing with orientation maps for extracting local invariant features

Local invariant features have been widely used as fundamental elements for image matching and object recognition. Although dense sampling of local features is useful in achieving an improved performance in image matching and object recognition, it results in increased computational costs for feature extraction. The purpose of this paper is to develop fast computational techniques for extracting local invariant features through the use of a graphics processing unit (GPU). In particular, we consider an algorithm that uses multiresolutional orientation maps to calculate local descriptors consisting of the histograms of gradient orientations. By using multiresolutional orientation maps and applying Gaussian filters to them, we can obtain voting values for the histograms for all the pixels in a scale space pyramid. We point out that the use of orientation maps has two advantages in GPU computing. First, it improves the efficiency of parallel computing by reducing the number of memory access conflicts in the overlaps among local regions, and secondly it utilizes a fast implementation of Gaussian filters that permits the use of shared memory for the many convolution operations required for orientation maps. We conclude with experimental results that demonstrate the usefulness of multiresolutional orientation maps for fast feature extraction.