An overview of deep learning methods for image registration with focus on feature-based approaches. (2nd April 2020)

• Record Type: Journal Article
• Authors: Kuppala, Kavitha; Banda, Sandhya; Barige, Thirumala Rao
• Is Part Of: International journal of image and data fusion. Volume 11:Number 2(2020)
• Page Start: 113
• Page End: 135
• Publication Date: 2020-04-02
• Subjects: convolution neural network -- area-based image registration -- feature-based image registration -- similarity; Image processing -- Periodicals; Multisensor data fusion -- Periodicals; Multisensor data fusion; Periodicals; 621.36705
• DOI: 10.1080/19479832.2019.1707720
• Languages: English
• ISSNs: 1947-9832

Abstract

ABSTRACT: Image registration is an essential pre-processing step for several computer vision problems like image reconstruction and image fusion. In this paper, we present a review on image registration approaches using deep learning. The focus of the survey presented is on how conventional image registration methods such as area-based and feature-based methods are addressed using deep net architectures. Registration approach adopted depends on type of images and type of transformation used to describe the deformation between the images in an application. We then present a comparative performance analysis of convolutional neural networks that have shown good performance across feature extraction, matching and transformation estimation in featured-based registration. Experimentation is done on each of these approaches using a dataset of aerial images generated by inducing deformations such as scale.