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Feature point descriptors: infrared and visible spectra.

Ricaurte P, Chilán C, Aguilera-Carrasco CA, Vintimilla BX, Sappa AD - Sensors (Basel) (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: CIDIS-FIEC, Escuela Superior Politécnica del Litoral (ESPOL), Campus Gustavo Galindo, Km 30.5 vía Perimetral, P.O. Box 09-01-5863, Guayaquil, Ecuador. paricaur@espol.edu.ec.

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.

No MeSH data available.


Performance to image degradation (blur): (a) visible spectrum; (b) LWIR spectrum.
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f5-sensors-14-03690: Performance to image degradation (blur): (a) visible spectrum; (b) LWIR spectrum.

Mentions: Figure 5 presents the study of robustness of the different algorithms when the given images are degraded using a Gaussian filter of increasing size. In general all the algorithms in both spectrums are equally affected showing a decrease in performance with the increase of kernel size. In the particular case of LWIR, ORB shows the worst performance; in other words it seems to be the most sensitive to blur. This fact can be appreciated in the fast decrease in performance.


Feature point descriptors: infrared and visible spectra.

Ricaurte P, Chilán C, Aguilera-Carrasco CA, Vintimilla BX, Sappa AD - Sensors (Basel) (2014)

Performance to image degradation (blur): (a) visible spectrum; (b) LWIR spectrum.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3958214&req=5

f5-sensors-14-03690: Performance to image degradation (blur): (a) visible spectrum; (b) LWIR spectrum.
Mentions: Figure 5 presents the study of robustness of the different algorithms when the given images are degraded using a Gaussian filter of increasing size. In general all the algorithms in both spectrums are equally affected showing a decrease in performance with the increase of kernel size. In the particular case of LWIR, ORB shows the worst performance; in other words it seems to be the most sensitive to blur. This fact can be appreciated in the fast decrease in performance.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: CIDIS-FIEC, Escuela Superior Politécnica del Litoral (ESPOL), Campus Gustavo Galindo, Km 30.5 vía Perimetral, P.O. Box 09-01-5863, Guayaquil, Ecuador. paricaur@espol.edu.ec.

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.

No MeSH data available.