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Title: Auto-adaptive Harris corner detection algorithm based on entropy-improved block processing

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.

Authors:
 [1];  [1]; ORCiD logo [2]
  1. Rochester Institute of Technology (RIT), New York
  2. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1459293
DOE Contract Number:
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV - Orlando, Florida, United States of America - 4/17/2018 8:00:00 AM-4/19/2018 8:00:00 AM
Country of Publication:
United States
Language:
English

Citation Formats

Sun, Yihang, Ientilucci, Emmett, and Voisin, Sophie. Auto-adaptive Harris corner detection algorithm based on entropy-improved block processing. United States: N. p., 2018. Web. doi:10.1117/12.2305733.
Sun, Yihang, Ientilucci, Emmett, & Voisin, Sophie. Auto-adaptive Harris corner detection algorithm based on entropy-improved block processing. United States. doi:10.1117/12.2305733.
Sun, Yihang, Ientilucci, Emmett, and Voisin, Sophie. Tue . "Auto-adaptive Harris corner detection algorithm based on entropy-improved block processing". United States. doi:10.1117/12.2305733. https://www.osti.gov/servlets/purl/1459293.
@article{osti_1459293,
title = {Auto-adaptive Harris corner detection algorithm based on entropy-improved block processing},
author = {Sun, Yihang and Ientilucci, Emmett and Voisin, Sophie},
abstractNote = {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.},
doi = {10.1117/12.2305733},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue May 01 00:00:00 EDT 2018},
month = {Tue May 01 00:00:00 EDT 2018}
}

Conference:
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