Partial to full image registration based on candidate positions and multiple correspondences

Abstract

In some image-registration based applications, it is more usual to detect a low quality and tiny partial image rather than a full sample (forensic palmprint recognition, satellite images, object detection in outdoor scenes …). In these cases, the usual registration methods fail due to the great amount of outliers that have to be detected while comparing a tiny image (object to be registered) to a full image (object in the database). In this paper, we present an image registration method that explicitly considers a great amount of outliers. In a first step, the method selects some candidate points to be the centres of the partial image. In a second step, these candidates are refined until selecting one through a multiple correspondence method. Experimental validation shows that the algorithm can outperform state of the art identification methods given the image to be identified a tiny and partial sample.