Using Local, Contextual, and Deep Convolutional Neural Network Features in Image Registration

Abstract

Image registration is a well-known problem that arises in many applications in the fields of computer vision, remote sensing, and medical imaging. Many registration methods have been proposed in the literature. However, no single method works well in all kinds of images. In this work, local features and context-based augmented features are used in order to improve the accuracy of the image registration. Furthermore, an attempt has been made to use deep convolutional neural network features on top of those features for further improvement. The paper presents comparative results on image registration with and without feature augmentation and the deep convolutional neural network features. The results from the methods on a widely used benchmark dataset from the University of Oxford confirm improvement in the accuracy of image registration when local and augmented features are used.