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

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 extract 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.

Malaria parasite detection and cell counting for human and mouse using thin blood smear microscopy

“…Different techniques have been proposed including Otsu thresholding 12 , 38 – 40 and watershed algorithms 41 – 43 that are usually combined with morphology operations to improve segmentation results and address texture complexities; however, improper clump splitting and over-segmentation are the main drawbacks of the methods based on histogram thresholding and watershed transform 7 . To address the splitting of overlapping cells and avoid over segmentation marker-controlled watershed algorithms, 25 , 44 , 45 template matching, 32 , 46 Ada-boost, 31 distance transform, 47 and active contour models 21 , 48 – 50 have been presented, which perform poor to segment highly overlapping cells.…”