Combustion Images in Incinerator by Robust PCA with Intra-sample Outlier Process

Abstract

Combustion images in incinerators usually involve various noise elements in each of its images, since diverse wastes cause turbulent burning. This paper proposes a fuzzy membership approach basedon the squared Mahalanobis distances and the noise clustering (NC) by Dave for robustizing PCA to intra-sample outliers. The clustering prototypes of the images by the robust approach clearly represent the combustion statuses of an incinerator.