A feature-based technique for joint, linear estimation of high-order image-to-mosaic transformations: mosaicing the curved human retina

An algorithm for constructing image mosaics from multiple, uncalibrated, weak-perspective views of the human retina is presented and analyzed. It builds on an algorithm for registering pairs of retinal images using a noninvertible, 12-parameter, quadratic image transformation model and hierarchical, robust estimation. The major innovation presented is a linear, feature-based, noniterative method for jointly estimating consistent transformations of all images onto the mosaic "anchor image." Constraints for this estimation are derived from pairwise registration both directly with the anchor image and indirectly between pairs of nonanchor images. An incremental, graph-based technique constructs the set of registered image pairs used in the solution. The estimation technique allows images that do not overlap the anchor frame to be successfully mosaiced, a valuable capability for mosaicing images of the retinal periphery. Experimental analysis on data sets from 16 eyes shows the average overall median transformation error in final mosaic to be 0.76 pixels. The technique is simpler, more accurate, and offers broader coverage than previously published methods