An End-to-End Deep Convolutional Neural Network for a Multi-Scale Image Matching and Localization Problem

Abstract

Diverse imaging techniques are utilized in many scientific domains to acquire a rich description of the subject under study and to further discover a variety of its properties. Especially, in sample systems, by probing with optical, electron or x-ray beams, the captured images describe the sample in an extremely large range of length scales. This makes the correlation from one image to another very difficult in addition to the intrinsic appearance complexity of those scientific images. In this paper, we aim to tackle this multi-scale image matching and localization problem by proposing an end-to-end deep convolutional neural network. Our proposed network is designed to first generate different filters according to the two queried images originated from different length scales. Then, to compute the correlation map, we use these filters to predict the correspondence between the two images. For the training and evaluation, we collect a number of electron microscopy experiments to form a multi-scaled image patch dataset comprised of various material structures. We observe about 90% accuracy for multi-scale image matching and localization while a triplet-based network shows about 78% accuracy.