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Author |
Cristhian A. Aguilera; Angel D. Sappa; R. Toledo |
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Title |
LGHD: A feature descriptor for matching across non-linear intensity variations |
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Conference Article |
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Year |
2015 |
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IEEE International Conference on, Quebec City, QC, 2015 |
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178 - 181 |
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Feature descriptor, multi-modal, multispectral, NIR, LWIR |
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Abstract |
This paper presents a new feature descriptor suitable to the task of matching features points between images with nonlinear intensity variations. This includes image pairs with significant illuminations changes, multi-modal image pairs and multi-spectral image pairs. The proposed method describes the neighbourhood of feature points combining frequency and spatial information using multi-scale and multi-oriented Log- Gabor filters. Experimental results show the validity of the proposed approach and also the improvements with respect to the state of the art. |
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IEEE |
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Quebec City, QC, Canada |
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English |
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English |
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2015 IEEE International Conference on Image Processing (ICIP) |
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Approved |
no |
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Call Number |
cidis @ cidis @ |
Serial |
40 |
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Author |
Cristhian A. Aguilera; Francisco J. Aguilera; Angel D. Sappa; Ricardo Toledo |
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Title |
Learning crossspectral similarity measures with deep convolutional neural networks |
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Conference Article |
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Year |
2016 |
Publication |
IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) Workshops |
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267-275 |
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Abstract |
The simultaneous use of images from different spectra can be helpful to improve the performance of many com- puter vision tasks. The core idea behind the usage of cross- spectral approaches is to take advantage of the strengths of each spectral band providing a richer representation of a scene, which cannot be obtained with just images from one spectral band. In this work we tackle the cross-spectral image similarity problem by using Convolutional Neural Networks (CNNs). We explore three different CNN archi- tectures to compare the similarity of cross-spectral image patches. Specifically, we train each network with images from the visible and the near-infrared spectrum, and then test the result with two public cross-spectral datasets. Ex- perimental results show that CNN approaches outperform the current state-of-art on both cross-spectral datasets. Ad- ditionally, our experiments show that some CNN architec- tures are capable of generalizing between different cross- spectral domains. |
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English |
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English |
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Approved |
no |
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Call Number |
cidis @ cidis @ |
Serial |
48 |
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