Fast vision-based object recognition using combined integral map

Abstract

Integral images or integral map (IMap) is one of the major techniques that has been used to improve the speed of computer vision systems. It has been used to compute Haar features and histograms of oriented gradient features. Some modifications have been proposed to the original IMap algorithm, but most proposed systems use IMap as it was first introduced. The IMap may be further improved by reducing its computational cost in multi-dementional feature domain. In this paper, a combined integral map (CIMap) technique is proposed to efficiently build and use multiple IMaps using a single concatenated map. Implementations show that using CIMap can signifficantly improve system speed while maintaining the accuracy. © 2009 Springer-Verlag Berlin Heidelberg.