Abstract
This manuscript evaluates the behavior of classical feature point descriptors when they are used in images from long-wave infrared spectral band and compare them with the results obtained in the visible spectrum. Robustness to changes in rotation, scaling, blur, and additive noise are analyzed using a state of the art framework. Experimental results using a cross-spectral outdoor image data set are presented and conclusions from these experiments are given.
Highlights
Recent advances in imaging technologies have increased the usage of cameras working at different spectral bands
The current work is focused on the LWIR domain, which corresponds to the infrared spectral band farthest from the visible spectrum
The visible domain (VS) images were obtained with an ACE camera, from Basler, with a resolution of 658 492 pixels; while the LWIR images were obtained with a Gobi-640-GigE camera, from Xenixs
Summary
Recent advances in imaging technologies have increased the usage of cameras working at different spectral bands. Infrared imaging represents one of the examples of such novel technologies. These images cover the spectral band from 0.75 m to 15 m, which is split up into the following categories: Near-Infrared (NIR: 0.75–1.4 m), Short-Wave Infrared (SWIR: 1.4–3 m), Mid-Wave Infrared (MWIR: 3–8 m) or. Images from each one of these categories have a particular advantage for a given application; for instance, NIR images are generally used in gaze detection and eye tracking applications [3]; the SWIR spectral band has shown its usage in heavy fog environments [4]; MWIR is generally used to detect temperatures somehow above body temperature in military applications; LWIR images have been used in video surveillance and driver assistance (e.g., [5,6]). The current work is focused on the LWIR domain, which corresponds to the infrared spectral band farthest from the visible spectrum
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