@inbook{b839fb885e8c4b138fef8bb4f978b847,
title = "Fast and Robust Image Matching Based on Depth-Wise Convolution Features and Unique Nearest Neighbour Similarity",
abstract = "Finding a template patch in a target image, including object detection and tracking, is a core component in computer vision applications. This paper introduces a fast and robust image matching based on depth-wise convolution features and unique nearest neighbour similarity. Experiments with its results show that our algorithm can effectively solve the problem of image mapping with complex deformation, and the matching performance is better than the existing optimal feature matching algorithms by improving the matching accuracy about 10.68\%.",
keywords = "Deep learning, Depth-wise convolution, Image matching",
author = "Li Ruan and Yuanjie Jiang and Chang Yang and Yiyang Xing and Limin Xiao and Xiangwen Qu",
note = "Publisher Copyright: {\textcopyright} 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.",
year = "2021",
doi = "10.1007/978-3-030-70665-4\_100",
language = "英语",
series = "Lecture Notes on Data Engineering and Communications Technologies",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "933--940",
booktitle = "Lecture Notes on Data Engineering and Communications Technologies",
address = "德国",
}