@inproceedings{1c341072108140aeab5e5473761f6aa8,
title = "Template matching in the wild with weighted assembled similarity",
abstract = "In this paper, we propose a template matching algorithm that is robust to deformations and background clutters. A weighted assembled similarity measure is constructed to discover the similarity between two different distributions, and a two-step nearest neighbor searching algorithm is designed to provide the feature points with different weights, which makes it more distinctive when calculating the similarity between the candidate image and the template. A local feature descriptor named Progressive Gradient Descriptor is also put forward to encode the input image to a high-dimensional feature map. Experiments on real-scene data prove that the proposed algorithm is competitive in terms of matching accuracy.",
keywords = "feature descriptor, nearest neighbor, template matching",
author = "Lingfeng Wang and Yan Ding and Peilin Li",
note = "Publisher Copyright: {\textcopyright} 2024 SPIE. All rights reserved.; 6th Conference on Frontiers in Optical Imaging and Technology: Imaging Detection and Target Recognition ; Conference date: 22-10-2023 Through 24-10-2023",
year = "2024",
doi = "10.1117/12.3015901",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Chao Zuo and Jiangtao Xu",
booktitle = "Sixth Conference on Frontiers in Optical Imaging and Technology",
address = "United States",
}