A Novel Point-Based Indexing Strategy for Biospeckle Sensors

Abstract

In this letter, a novel point-based algorithm is presented for biospeckle sensors by combining random temporal history, robust normalization, and absolute value difference technique. Performance of the proposed strategy was evaluated and compared with existing strategies in simulation domain in terms of different analysis parameters (viz. frame invariance, correlation linearity, characterization of heterogeneity, and efficient computation). For experimental verification, biospeckle frames corresponding to coffee seeds ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">coffea arabica</i> ) and maize seeds ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">zea maize</i> ) were utilized. From obtained results it can be inferred that, the proposed strategy possesses high accuracy in terms of all the analysis parameters. In future, the strategy can be widely utilized for industrial automation and quality inspection.