Multi-Scale Spatially Weighted Local Histograms in O(1)

TL;DR

This paper introduces a novel algorithm for efficiently computing multi-scale spatially weighted local histograms in constant time using a weighted integral histogram, enhancing speed and accuracy in image processing tasks like tracking.

Contribution

It presents a new method to compute spatially weighted local histograms in constant time, improving performance in image analysis applications.

Findings

Enhanced target localization accuracy in tracking

Achieved faster computation with constant-time algorithm

Improved robustness over plain histograms

Abstract

Weighting pixel contribution considering its location is a key feature in many fundamental image processing tasks including filtering, object modeling and distance matching. Several techniques have been proposed that incorporate Spatial information to increase the accuracy and boost the performance of detection, tracking and recognition systems at the cost of speed. But, it is still not clear how to efficiently ex- tract weighted local histograms in constant time using integral histogram. This paper presents a novel algorithm to compute accurately multi-scale Spatially weighted local histograms in constant time using Weighted Integral Histogram (SWIH) for fast search. We applied our spatially weighted integral histogram approach for fast tracking and obtained more accurate and robust target localization result in comparison with using plain histogram.