| Records |
| Author |
Cristhian A. Aguilera; Angel D. Sappa; R. Toledo |
| Title |
LGHD: A feature descriptor for matching across non-linear intensity variations |
Type |
Conference Article |
| Year |
2015 |
Publication |
IEEE International Conference on, Quebec City, QC, 2015 |
Abbreviated Journal |
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Volume  |
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Issue |
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Pages |
178 - 181 |
| Keywords |
Feature descriptor, multi-modal, multispectral, NIR, LWIR |
| 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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Thesis |
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| Publisher |
IEEE |
Place of Publication |
Quebec City, QC, Canada |
Editor |
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| Language |
English |
Summary Language |
English |
Original Title |
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Conference |
2015 IEEE International Conference on Image Processing (ICIP) |
| Notes |
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Approved |
no |
| Call Number |
cidis @ cidis @ |
Serial |
40 |
| Permanent link to this record |
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| Author |
Cristhian A. Aguilera; Francisco J. Aguilera; Angel D. Sappa; Ricardo Toledo |
| Title |
Learning crossspectral similarity measures with deep convolutional neural networks |
Type |
Conference Article |
| Year |
2016 |
Publication |
IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) Workshops |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
267-275 |
| Keywords |
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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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Thesis |
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Place of Publication |
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Editor |
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| Language |
English |
Summary Language |
English |
Original Title |
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Abbreviated Series Title |
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Edition |
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Approved |
no |
| Call Number |
cidis @ cidis @ |
Serial |
48 |
| Permanent link to this record |
