A Robust Method for Image Stitching

We propose a novel method for image stitching that is robust against repetitive patterns and featureless regions in the imaginary. In such cases, typical image stitching methods easily produce stitching artifacts, since they may produce false pairwise image registrations that are in conflict within the global connectivity graph. By contrast, our method collects all the plausible pairwise image registration candidates, among which globally consistent candidates are chosen. This enables the method to determine the correct pairwise registrations by utilizing all the available information from the whole imaginary, such as unambiguous registrations outside the repeating pattern and featureless regions. We formalize the method as a weighted multigraph whose nodes represent the individual image transformations from the composite image, and whose sets of multiple edges between two nodes represent all the plausible transformations between the pixel coordinates of the two images. The edge weights represent the plausibility of the transformations. The image transformations and the edge weights are solved from a non-linear minimization problem with linear constraints, for which a projection method is used. As an example, we apply the method in a scanning application where the transformations are primarily translations with only slight rotation and scaling component.

A statistical model of tristimulus measurements within and between OLED displays

We present an empirical model for noises in color measurements from OLED displays. According to measured data the noise is not isotropic in the XYZ space, instead most of the noise is along an axis that is parallel to a vector from origin to measured XYZ vector. The presented empirical model is simple and depends only on the measured XYZ values. Our tests show that the variations between multiple panels of the same type have similar distribution as the temporal noise in measurements from a single panel, but a larger magnitude.