@inproceedings{a8fc0b6725ea45f98d86688e89a15dda,
title = "Iterative template matching with rotation invariant best-buddies pairs",
abstract = "In this paper, we propose a new method for template matching method with rotation invariance. Our template matching can not only find the location of the object, but also annotate its rotation angle. The key idea is to firstly rectify the local rotation patches according to their intensity centroids, and then to find the corresponding patch-features between template and target images under an iterative matching framework. We adopt the coarse-to-fine search ways, so the patch size should be updated accordingly, which is time-consuming. To tackle this problem, we use the integral image to update the intensity centroid to accelerate the computing speed. The corresponding feature matching is based on the Best-Buddies Pairs (BBPs), which is robust to the non-rigid transform of local range and outliers. Experimental results demonstrate the effectiveness and robustness of the proposed algorithm.",
keywords = "Integral image, Intensity centroid, Rotation invariance, Template matching",
author = "Zhuo Chen and Yang Yang and Weile Chen and Qian Kou and Dexing Zhong",
note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2017.; 2nd Chinese Conference on Computer Vision, CCCV 2017 ; Conference date: 11-10-2017 Through 14-10-2017",
year = "2017",
doi = "10.1007/978-981-10-7299-4\_38",
language = "英语",
isbn = "9789811072987",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "461--471",
editor = "Xiang Bai and Qinghua Hu and Liang Wang and Qingshan Liu and Jinfeng Yang and Ming-Ming Cheng and Deyu Meng",
booktitle = "Computer Vision - 2nd CCF Chinese Conference, CCCV 2017, Proceedings",
}