Temperature imaging of sub-millimeter-thick water using a near-infrared camera

Kakuta, Naoto; Kondo, Katsuya; Ozaki, Atsushi; Arimoto, Hidenobu; Yamada, Yukio

doi:10.1016/j.ijheatmasstransfer.2009.04.024

Cited by 24 publications

(20 citation statements)

References 38 publications

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“…9,14,22,23,[47][48][49] On the path toward a more widespread implementation of SWIR tissue optics measurements, one important factor to consider is the temperature dependence of tissue absorption features in the SWIR regime. Water is a primary SWIR absorber in tissue, and it has been shown 53,54 that the spectral location of SWIR water absorption peaks can vary with temperature. Jansen et al 53 reported a water absorption coefficient that decreased with increasing temperature and a water absorption peak that shifted from 1940 nm at 22°C to 1920 nm at 70°C.…”

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confidence: 99%

“…Jansen et al 53 reported a water absorption coefficient that decreased with increasing temperature and a water absorption peak that shifted from 1940 nm at 22°C to 1920 nm at 70°C. Kakuta et al 54 reported a blue shift in the 1450 nm water absorption peak when the temperature was varied between 24°C and 40°C. Furthermore, temperature-dependent changes in the measured optical properties of skin have been reported in the VIS-NIR, 55,56 a finding that suggests that such changes are likely expected in the SWIR as well.…”

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confidence: 99%

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Review of short-wave infrared spectroscopy and imaging methods for biological tissue characterization

et al. 2015

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Review of short-wave infrared spectroscopy and imaging methods for biological tissue characterization

et al. 2015

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“…Most infrared cameras support unicolor implementation, but some support multicolors. Generally, the infrared camera is capable of obtaining the same quality images at night as in the day and can obtain images at a long distance, but it is rather expensive [16].…”

Section: Related Workmentioning

confidence: 99%

A Multiple Feature-Based Image-Switching Strategy in Visual Sensor Networks

Jang

Kim

2015

International Journal of Distributed Sensor Networks

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Generally, one fixed camera is used to take still or dynamic images and extract proper information from the captured images. However, the process of analyzing images through the use of one camera is very sensitive to neighboring environmental factors, such as illumination, background, and noise; thus, it is hard to guarantee precision. To extract proper information from images more precisely in visual sensor networks, this paper proposes an image-switching strategy where, among different types of installed cameras, the one camera best suited to neighboring circumstances is chosen. The proposed strategy is to first receive initial images as input data and then extract multiple features representing neighboring circumstances from the input images. Subsequently, it is to define the neighboring circumstances metric, which is the weighted sum of the extracted features, and to dynamically switch cameras to obtain images in accordance with the neighboring circumstances. The results of the experiment show that the proposed dynamic switching strategy reliably chooses, from among different cameras, the one camera that is best suited to the neighboring circumstances.

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“…A temperature imaging method based on the temperature dependence of the NIR absorption of water has been presented by Kakuta et al [54]. The apparatus consists of a microscope, NIR camera, and narrow-band pass filter for measuring the absorbance at 1412 nm.…”

Section: Temperature Monitoring Of Small Amounts Of Liquidmentioning

confidence: 99%