Measuring colorant fading within raster regions of printed scanned customer content using a novel unsupervised clustering method

Abstract

Colorant fading defects in raster region of interest (ROI) are among the most common printing issues in electrophotographic printers. Colorant fading manifests as faint print or customer content and is usually caused by low-level ink/cartridge. This paper presents an accurate method to detect the colorant fading defects and classify the defects based on their severity. There are two modules for this method. The first module uses the SLIC super-pixel method to separate the raster ROI and extract the smooth super-pixels. We design a novel unsupervised clustering algorithm to automatically extract the three or four main colors from the smooth super-pixels. Unsupervised data clustering is an important problem that arises in many image processing applications. We propose a new approach to unsupervised data clustering called Data Inter-Distance MEdiated Clustering (DIDMEC). Our new method is based on analyzing the matrix of Euclidean distances between each pair of points in the data set. Based on three simple properties, we devise an approach that effectively yields the same accuracy as the K-Means algorithm but at a much lower computational cost for a moderate number of sample points. The second module extracts feature vectors for each main color clustered by DIDMEC to classify the colorant fading defects based on their severity.