Abstract
Image comparison is a fundamental step for monitoring images and has wide applications in many disciplines of sciences, including satellite imaging, medical research, quality control and so forth. This problem, however, is complicated because (i) the observed images often contain noise, (ii) the image intensity functions are discontinuous and have spatial structures. In the literature, a vast majority of the methods are intensity-based. However, such an approach is often questionable in real life situations where small changes in the background may not indicate an actual meaningful change in the images as long as the boundaries of the image objects remain the same. In this article, we propose a flexible and effective image comparison method based on local pixel clustering and construct a test statistic based on the Variation of Information metric. This is a feature based image comparison technique where edges or the jump points are considered as the primary features. Numerical examples and statistical properties show that the proposed image comparison method performs well in various real life scenarios.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.