Template matching in the wild with weighted assembled similarity

Lingfeng Wang; Yan Ding; Peilin Li

doi:10.1117/12.3015901

30 April 2024 Template matching in the wild with weighted assembled similarity

Lingfeng Wang, Yan Ding, Peilin Li

Proceedings Volume 13156, Sixth Conference on Frontiers in Optical Imaging and Technology: Imaging Detection and Target Recognition; 131560M (2024) https://doi.org/10.1117/12.3015901
Event: Sixth Conference on Frontiers in Optical Imaging Technology and Applications (FOI2023), 2023, Nanjing, JS, China

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Lingfeng Wang, Yan Ding, and Peilin Li "Template matching in the wild with weighted assembled similarity", Proc. SPIE 13156, Sixth Conference on Frontiers in Optical Imaging and Technology: Imaging Detection and Target Recognition, 131560M (30 April 2024); https://doi.org/10.1117/12.3015901

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