Computationally efficient histogram extraction for rectangular image regions
Fatih Porikli · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
In this contribution, we propose a computationally fast algorithm to compute local feature histograms of an image. existing histogram extraction is done by evaluating the distribution of image features such as color, edge, etc. within a local image windows centered each pixels. This approach is computationally very demanding since it requires evaluation of the feature distributions for every possible local window in the image. We develop an accumulated histogram propagation method that takes advantage of the fact that the local windows are overlaps and their feature histograms are highly correlated. Instead of evaluating the distributions independently, we propagate the distribution information in a 2D sweeping fashion. Our simulations prove that the proposed algorithm significantly accelarates histogram extraction and enables computation of e.g. posterier propabilities and likelihood values, which are frequently used for object detection, and tracking, as well as in other vision applications such as calibration and recognition.