Adaptive Background Subtraction

Method

Unlike conventional background subtraction methods, ABS evaluates the local area around each shape and its context within the lane to determine the background value. This patented, intelligent algorithm combines the advantages of shape-based and lane-based methods in a single calculation. User bias and the impact of subjective choices are minimized or eliminated, allowing different users to generate consistently reproducible results.

Reproducibility and Error

• By evaluating a broader context for each shape, Empiria Studio uses a larger volume of data to evaluate and estimate image background. A typical region of interest may contain a few hundred pixels, and a conventional background subtraction method uses a few hundred pixels to estimate image background. In contrast, Empiria Studio analyzes several thousand pixels to estimate the background for that shape.

• If examined at the pixel level, image background is actually quite uniform. The ABS algorithm looks for this redundancy and differentiates it from “non-context” areas like spots, smears, or other artifacts adjacent to the band or in the sample lane. With a large data set for comparison, the algorithm can easily identify and disregard these anomalies. It adapts to the individual image context, calculating image background with unmatched precision and reproducibility.