Multi-Scale Spatially Weighted Local Histograms in O(1)
This addresses the speed-accuracy trade-off in detection, tracking, and recognition systems for computer vision applications, though it is incremental as it builds on existing integral histogram methods.
The paper tackled the problem of efficiently computing spatially weighted local histograms for image processing tasks, and presented a novel algorithm that achieves this in constant time, resulting in more accurate and robust target localization compared to plain histograms.
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.