@inproceedings{c7abc8bdec1e4aa69586ebf6725da888,
title = "Improvement of the Harris corner detector using an entropy-block-based strategy",
abstract = "Extracting well distributed control points (CPs) is a very challenging task for remote sensing image registration, particularly for large high-resolution images over heterogeneous landscape. Based on image analysis such as edge detection, corner detection, and information theory, a new CP detection approach is proposed to select high-quality, evenly distributed CPs. The Entropy-Block-Based variant of the Harris Corner Detector (EBB-HCD) is achieved by dividing the image into blocks and by allocating the number of CP's based upon the entropy of each block. While the block-based strategy improves the CP balance problem, a factor calculated from entropy avoids overdetection. We conducted a comparison study utilizing the well-known Harris Corner Detector (HCD) and an implementation of the Block-Based Harris Corner Detector (BB-HCD). Experimental results indicate that using EBB-HCD to find the CPs improves the overall alignment accuracy during registration compared with HCD or BB-HCD.",
keywords = "Entropy Based Strategy, Evenly Distributed Features, Harris Corner Detector, Image Registration",
author = "Yihang Sun and Emmett Ientilucci and Sophie Voisin",
note = "Publisher Copyright: {\textcopyright} COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.; Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV 2018 ; Conference date: 17-04-2018 Through 19-04-2018",
year = "2018",
doi = "10.1117/12.2305733",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Messinger, {David W.} and Miguel Velez-Reyes",
booktitle = "Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV",
}