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Related Concept Videos

Temperature Measurement Sites01:14

Temperature Measurement Sites

1.6K
A thermometer measures body temperature. The common sites for measuring body temperature are the oral cavity, axillary region, temporal artery, and skin surface, such as the forehead, abdomen, and axilla. True core body temperature is assessed in the rectum, tympanic membrane, pulmonary artery, esophagus, and urinary bladder.
Oral: When assessing oral temperature, the thermometer tip should be placed under the tongue in the posterior sublingual pocket. It offers accurate readings and can be...
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Distance Corrections01:15

Distance Corrections

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To achieve precise distance measurements, especially in surveying and construction, certain corrections must be applied to account for potential sources of error like the standardization errors, temperature variations, and slope adjustments.Standardization error emerges when measurement equipment undergoes changes, such as wear, repairs, or weather impacts. To address this, surveyors compare the equipment’s readings to a standard. This process identifies any deviation that might lead to...
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Equipments Used to Measure Body Temperature01:13

Equipments Used to Measure Body Temperature

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Body temperature can be assessed using various devices and measured in Celsius or Fahrenheit.
Glass-bulb Thermometer:
Glass-bulb thermometers are hollow glass tubes with a bulb tip containing liquid such as ethanol or mercury. Historically, glass bulb mercury thermometers were the standard device to measure body temperature. Today, mercury thermometers are prohibited in many countries due to the hazardous effects of mercury and the risk of exposure if the glass bulb breaks. In general,...
978

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Related Experiment Video

Updated: Jun 12, 2025

In Situ Surface Temperature Measurement in a Conveyor Belt Furnace via Inline Infrared Thermography
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Simultaneous Temperature Estimation and Nonuniformity Correction From Multiple Frames.

Navot Oz, Omri Berman, Nir Sochen

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    |September 17, 2024
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    Summary
    This summary is machine-generated.

    This study introduces a new deep learning method for accurate temperature estimation and nonuniformity correction in low-cost infrared cameras. The approach significantly improves usability in practical applications by leveraging multiple frames and ambient temperature data.

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    Area of Science:

    • Optics and Photonics
    • Computer Vision
    • Machine Learning

    Background:

    • Infrared (IR) cameras are vital for temperature measurement across diverse fields like agriculture, medicine, and security.
    • Low-cost microbolometer-based IR cameras offer potential but suffer from nonuniformity and temperature drift, limiting their practical use.
    • Existing methods often struggle with imperfect frame registration and accurate temperature offset estimation.

    Purpose of the Study:

    • To develop a novel approach for simultaneous temperature estimation and nonuniformity correction (NUC) in low-cost IR cameras.
    • To address the limitations of drift and spatial nonuniformity in microbolometer-based IR imaging.
    • To improve the accuracy and reliability of temperature measurements from affordable IR cameras.

    Main Methods:

    • A deep learning architecture, Kernel Prediction Network (KPN), was developed, integrating the camera's physical image-acquisition model.
    • The method processes multiple IR frames, accommodating imperfect registration, to perform simultaneous temperature estimation and NUC.
    • A novel offset block was introduced to incorporate ambient temperature for improved camera offset estimation.

    Main Results:

    • The accuracy of temperature estimation and NUC was found to be significantly influenced by the number of frames used.
    • The proposed offset block substantially enhanced performance compared to a standard KPN.
    • Testing on real-world data from an unmanned aerial vehicle (UAV) showed minimal average errors (0.27-0.54°C) compared to scientific-grade cameras.

    Conclusions:

    • The developed method offers an accurate and efficient solution for simultaneous temperature estimation and NUC in low-cost IR cameras.
    • This technique overcomes key limitations of microbolometer-based IR cameras, broadening their applicability.
    • The findings have significant implications for enhancing temperature monitoring in various practical applications.