Fast Image Alignment Using Anytime Algorithms

Abstract

Image alignment refers to finding the best transformation from a fixed reference image to a new image of a scene. This process is often guided by similarity measures between images, computed based on the image data. However, in time-critical applications state-of-the-art methods for computing similarity are too slow. Instead of using all the image data to compute similarity, one can use a subset of pixels to improve the speed, but often this comes at the cost of reduced accuracy. This makes the problem of image alignment a natural application domain for deliberation control using anytime algorithms. However, almost no research has been done in this direction. In this paper, we present anytime versions for the computation of two common image similarity measures: mean squared difference and mutual information. Off-line, we learn a performance profile specific to each measure, which is then used on-line to select the appropriate amount of pixels to process at each optimization step. When tested against existing techniques, our method achieves comparable quality and robustness with significantly less computation.