ISIFT: extracting incremental results from SIFT.

In computer vision, scale-invariant feature transform (SIFT) remains one of the most commonly used algorithms for feature extraction, but its high computational cost makes it hard to deploy in real-time applications. In this paper, we introduce a novel technique to restructure the inter-octave and intra-octave dependencies of SIFT's keypoint detection and description processes, allowing it to be stopped early and produce approximate results in proportion to the time for which it was allowed to run. If our algorithm is run to completion (about 0.7% longer than traditional SIFT), its results and SIFT's converge. Unlike previous approaches to real-time SIFT, we require no special hardware and make no compromises in keypoint quality, making our technique ideal for real-time and near-real-time applications on resource-constrained systems. We use standard data sets and metrics to analyze the performance of our algorithm and the quality of the generated keypoints.