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Proceedings Paper

Computationally efficient histogram extraction for rectangular image regions
Author(s): Fatih Porikli
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Paper Abstract

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.

Paper Details

Date Published: 25 February 2005
PDF: 8 pages
Proc. SPIE 5671, Real-Time Imaging IX, (25 February 2005); doi: 10.1117/12.588109
Show Author Affiliations
Fatih Porikli, Mitsubishi Electric Research Labs. (United States)


Published in SPIE Proceedings Vol. 5671:
Real-Time Imaging IX
Nasser Kehtarnavaz; Phillip A. Laplante, Editor(s)

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