2008 IEEE Conference on Computer Vision and Pattern Recognition (2008)
Anchorage, AK, USA
June 23, 2008 to June 28, 2008
Mohamed Hussein , Dept. of Computer Science, University of Maryland, USA
Fatih Porikli , Mitsubishi Electric Research Labs, Cambridge, MA 02139, USA
Larry Davis , Dept. of Computer Science, University of Maryland, USA
Integral images are commonly used in computer vision and computer graphics applications. Evaluation of box filters via integral images can be performed in constant time, regardless of the filter size. Although Heckbert  extended the integral image approach for more complex filters, its usage has been very limited, in practice. In this paper, we present an extension to integral images that allows for application of a wide class of non-uniform filters. Our approach is superior to Heckbert’s in terms of precision requirements and suitability for parallelization. We explain the theoretical basis of the approach and instantiate two concrete examples: filtering with bilinear interpolation, and filtering with approximated Gaussian weighting. Our experiments show the significant speedups we achieve, and the higher accuracy of our approach compared to Heckbert’s.
F. Porikli, M. Hussein and L. Davis, "Kernel integral images: A framework for fast non-uniform filtering," 2008 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Anchorage, AK, USA, 2008, pp. 1-8.