@inproceedings{7731248c2a0c403594aaee206a51101f,
title = "SIFT in GMS-selected areas: A method for feature point matching between photographs and rendered images",
abstract = "Pose estimation with rendered key images often enables more effective and efficient performance for on-line tasks. However, it is difficult to establish correspondences between photographs and rendered images because of their different sources and features. This paper proposes SIGMA (SIFT in GMS-selected Areas), a method combining high quality detector and descriptor with effective method of removing mismatches. It preforms SIFT locally in the areas selected by GMS, which provides high precision and good distribution of points. Experiments show the precision of SIGMA is 0.7638, higher than SIFT(0.5037) and GMS(0.7206).",
keywords = "Computer vision, Feature point matching, GMS, Image processing, SIFT",
author = "Junfu Zhou and Zhenzhong Wei",
note = "Publisher Copyright: {\textcopyright} 2020 SPIE.; 6th Symposium on Novel Optoelectronic Detection Technology and Applications ; Conference date: 03-12-2019 Through 05-12-2019",
year = "2020",
doi = "10.1117/12.2565221",
language = "英语",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Junhao Chu and Huilin Jiang",
booktitle = "Sixth Symposium on Novel Optoelectronic Detection Technology and Applications",
address = "美国",
}