Optimization of Fecal Detection Using Hyperspectral Imaging and Kernel Density Estimation
Yoon, S.C. | Lawrence, K.C. | Park, B. | Windham, W.R.
This article addresses the development of an iterative search algorithm to find an optimal threshold to detect surface contaminants on poultry carcasses for a real-time multispectral imaging application. Previous studies showed that a band-ratio algorithm with a 517 nm band and a 565 nm band could detect contaminants on the surface of poultry carcasses. In this study, thresholding for the band-ratio algorithm was optimized in a statistical sense. A fundamental problem of the thresholding is that there is a theoretical performance bound from the standpoint of statistical hypothesis testing. In a Neyman-Pearson (NP) framework, a lower bound of detection accuracy can be determined for minimizing false positives. An iterative search algorithm was designed to obtain an optimal threshold in the NP framework. For the design of the search algorithm, statistical density distributions of fecal and non-fecal image data were estimated by kernel density estimation, and characterized by edge models on a projection axis perpendicular to a linear decision boundary. Three necessary criteria were investigated for the selection of the optimum threshold of the band-ratio algorithm. Numerical simulations with hyperspectral poultry images showed that the optimum threshold was 1.05.Show more [+] Less [-]