The text fading defect is one of the most common defects in electrophotographic printers; and it dramatically affects print quality. It usually appears in a significant symbol Region of Interest (ROI), easily noticed by a user on his or her print. We can detect text fading by the density reduction for the black and white printed symbol ROI. It is difficult to detect the color text fading only by density reduction, because the depleted cartridge may only cause the color distortion without density reduction in the color printed symbol ROI. In our previous work with print quality defects analysis, the text fading detection method only works for black text fading defect detection [1]. Our new text fading method can detect the color text fading defect and predict the depleted cartridge. In this new text fading detection method, we use whole page image registration and the median threshold bitmap (MTB) matching method to align the text characters between the master and test symbol ROIs, because with the aligned text characters, it is easy to extract the difference between the master and the test text characters to detect the text fading defect. We use a support vector machine classifier to assign a rank to the overall quality of the printed page. We also use the gap statistic method with the K-means clustering algorithm to extract the different text characters’ different colors to predict the depleted cartridge.
This paper presents a novel method for 3D scene modeling using stereo vision, with an application to image registration. The method constists of two steps. First, disparity estimates are refined, by filling gaps of invalid disparity and removing halos of incorrectly assigned disparity. A coarse segmentation is obtained by identifying depth slices, after which objects are clustered based on color and texture information using Gabor filters. The second step consists of reconstructing the resulting objects in 3D for scene alignment by fitting a planar region. A 2D triangle mesh is generated, and a 3D mesh model is obtained by projecting each triangle onto the fitted plane. Both of these extensions result in improved alignment quality with respect to the state of the art, and operate in near real time using multi-threading. As a bonus, the refined disparity map can also be used in combination with the existing method.