We describe a method for automatically classifying image-quality defects on printed documents. The proposed approach accepts a scanned image where the defect has been localized a priori and performs several appropriate image processing steps to reveal the region of interest. A mask is then created from the exposed region to identify bright outliers. Morphological reconstruction techniques are then applied to emphasize relevant local attributes. The classification of the defects is accomplished via a customized tree classifier that utilizes size or shape attributes at corresponding nodes to yield appropriate binary decisions. Applications of this process include automated/assisted diagnosis and repair of printers/copiers in the field in a timely fashion. The proposed technique was tested on a database of 276 images of synthetic and real-life defects with 94.95% accuracy.
Department, Program, or Center
Microelectronic Engineering (KGCOE)
Onome Augustine Ugbeme, Eli Saber, Wencheng Wu, Kartheek Chandu, "Automated algorithm for the identification of artifacts in mottled and noisy images," Journal of Electronic Imaging 16(3), 033015 (1 July 2007). https://doi.org/10.1117/1.2761920
RIT – Main Campus