LGHD: A feature descriptor for matching across non-linear intensity variations

Aguilera, Cristhian; Sappa, Ángel D.; Toledo, Ricardo

doi:10.1109/icip.2015.7350783

Cited by 100 publications

(77 citation statements)

References 10 publications

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“…We have taken pairs of images from the visible-LWIR dataset introduced in [38]. Each pair has been adjusted in order to make viewpoints and resolutions of both images (infrared and visible) exactly the same.…”

Section: Methodsmentioning

confidence: 99%

PHROG: A Multimodal Feature for Place Recognition

Bonardi

Aïnouz

Boutteau

et al. 2017

Sensors

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Long-term place recognition in outdoor environments remains a challenge due to high appearance changes in the environment. The problem becomes even more difficult when the matching between two scenes has to be made with information coming from different visual sources, particularly with different spectral ranges. For instance, an infrared camera is helpful for night vision in combination with a visible camera. In this paper, we emphasize our work on testing usual feature point extractors under both constraints: repeatability across spectral ranges and long-term appearance. We develop a new feature extraction method dedicated to improve the repeatability across spectral ranges. We conduct an evaluation of feature robustness on long-term datasets coming from different imaging sources (optics, sensors size and spectral ranges) with a Bag-of-Words approach. The tests we perform demonstrate that our method brings a significant improvement on the image retrieval issue in a visual place recognition context, particularly when there is a need to associate images from various spectral ranges such as infrared and visible: we have evaluated our approach using visible, Near InfraRed (NIR), Short Wavelength InfraRed (SWIR) and Long Wavelength InfraRed (LWIR).

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Section: Methodsmentioning

confidence: 99%

PHROG: A Multimodal Feature for Place Recognition

Bonardi

Aïnouz

Boutteau

et al. 2017

Sensors

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“…A group of works was focused on the specific task of visible to Near-Infrared (NIR) registration [4,11]. [1] solves registration by FAST features [22] and unique descriptors for non linear intensity variations. [2] was the first to measure cross-spectral similarity by Convolutional-Neural-Network (CNN).…”

Section: Previous Workmentioning

confidence: 99%

Deep Multi-Spectral Registration Using Invariant Descriptor Learning

Ofir

Silberstein

Levi

et al. 2018

2018 25th IEEE International Conference on Image Processing (ICIP)

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In this paper, we introduce a novel deep-learning method to align cross-spectral images. Our approach relies on a learned descriptor which is invariant to different spectra. Multi-modal images of the same scene capture different signals and therefore their registration is challenging and it is not solved by classic approaches. To that end, we developed a feature-based approach that solves the visible (VIS) to Near-Infra-Red (NIR) registration problem. Our algorithm detects corners by Harris [9] and matches them by a patchmetric learned on top of a network trained on CIFAR-10 [12] dataset. As our experiments demonstrate we achieve a high-quality alignment of cross-spectral images with a subpixel accuracy. Comparing to other existing methods, our approach is more accurate in the task of VIS to NIR registration.

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“…When spectral ranges are closer, the captured images are more similar and therefore easier to align. [1] solves registration by FAST features [26] and unique descriptors for non linear intensity variations. [2] measures cross spectral similarity by Convolutional-Neural-Network (CNN).…”

Section: Previous Workmentioning

confidence: 99%

Registration and Fusion of Multi-Spectral Images Using a Novel Edge Descriptor

Ofir

Silberstein

Rozenbaum

et al. 2018

2018 25th IEEE International Conference on Image Processing (ICIP)

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In this paper we introduce a fully end-to-end approach for multi-spectral image registration and fusion. Our method for fusion combines images from different spectral channels into a single fused image by different approaches for low and high frequency signals. A prerequisite of fusion is a stage of geometric alignment between the spectral bands, commonly referred to as registration. Unfortunately, common methods for image registration of a single spectral channel do not yield reasonable results on images from different modalities. For that end, we introduce a new algorithm for multi-spectral image registration, based on a novel edge descriptor of feature points. Our method achieves an accurate alignment of a level that allows us to further fuse the images. As our experiments show, we produce a high quality of multi-spectral image registration and fusion under many challenging scenarios.

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LGHD: A feature descriptor for matching across non-linear intensity variations

Cited by 100 publications

References 10 publications

PHROG: A Multimodal Feature for Place Recognition

PHROG: A Multimodal Feature for Place Recognition

Deep Multi-Spectral Registration Using Invariant Descriptor Learning

Registration and Fusion of Multi-Spectral Images Using a Novel Edge Descriptor

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