A Convolutional Neural Network for Learning Local Feature Descriptors on Multispectral Images

Authors

Keywords:

Local feature descriptor, Multispectral images, Log-Gabor filters, Convolutional neural networks

Abstract

This work presents a novel convolutional neural network, termed multispectral features network (MF-Net), for learning local feature descriptors in multispectral images. Unlike most existing solutions, which primarily handle images from the visible light spectrum, we propose a learning-based method that deals with image data acquired from different spectrum bands. To design our convolutional neural network, we introduce a new layer that incorporates Log-Gabor filters to enhance the network capability to work with nonlinear intensity variations in images captured from different electromagnetic frequencies spectrum. This layer, entitled mapping layer, can be easily integrated into different network architectures. To demonstrate the efficacy and limitations of our method, we went on experiments with two distinct datasets extensively used in previous works composed of image pairs from the visible spectrum and the infrared spectrum. Experimental results with datasets containing images obtained from visible light and infrared spectrum show that our method can accurately match features, outperforming some state-of-the-art learning-based algorithms.

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Author Biographies

Cristiano Nunes, Centro Federal de Educação Tecnológica de Minas Gerais

Cristiano Nunes received the BS degree in computation engineering from the Centro Federal de Educação Tecnológica de Minas Gerais (CEFET-MG), Brazil, in 2014. He received the MS degree in Mathematical and Computational Modeling from the Centro Federal de Educação Tecnológica de Minas Gerais (CEFET-MG), Brazil, in 2017. He's a doctoral degree student and has been a system analyst at the same institution. His research interests include computer vision, pattern classification and content-based image and video retrieval.

Flávio Pádua, Centro Federal de Educação Tecnológica de Minas Gerais (CEFET-MG)

Flávio Pádua received the BS degree in electrical engineering and the MS and PhD degrees in computer science from the Universidade Federal de Minas Gerais (UFMG), Brazil, in 1999, 2002, and 2005, respectively. He has been an associate professor of computer engineering at the Centro Federal de Educação Tecnológica de Minas Gerais (CEFET-MG) since 2005. His research interests include computer vision, pattern classification and content-based image, and video retrieval.

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Published

2021-06-10

How to Cite

Nunes, C., & Pádua, F. (2021). A Convolutional Neural Network for Learning Local Feature Descriptors on Multispectral Images. IEEE Latin America Transactions, 20(2), 215–222. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/5432