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Related Concept Videos

Calibration Curves: Linear Least Squares01:20

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A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
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In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the...
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Accurate calibration of glassware, such as volumetric flasks, pipettes, and burettes, is essential to ensure accurate measurements in the analytical laboratory. Calibration helps maintain consistency across measurements and prevents errors arising from inaccurate volumes.
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Updated: Oct 4, 2025

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Towards High Performance Low Complexity Calibration in Appearance Based Gaze Estimation.

Zhaokang Chen, Bertram E Shi

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    This summary is machine-generated.

    This study improves appearance-based gaze estimation by analyzing calibration data complexity. A novel estimator (GEDDNet) achieves high accuracy with minimal calibration, even outperforming models requiring extensive training data.

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    Area of Science:

    • Computer Vision
    • Human-Computer Interaction
    • Machine Learning

    Background:

    • Appearance-based gaze estimation from RGB images offers unconstrained gaze tracking using common hardware.
    • Subject-independent models face accuracy limitations due to intra- and inter-subject appearance variations and subject-dependent bias.
    • Previous work proposed gaze decomposition to separate image-based estimates from subject-specific bias, outperforming calibration algorithms with large datasets.

    Purpose of the Study:

    • To comprehensively analyze the relationship between calibration dataset complexity and gaze estimation accuracy.
    • To investigate the impact of varying numbers of gaze targets, images per target, and head positions on calibration effectiveness.
    • To develop a high-performance, low-complexity gaze estimation method.

    Main Methods:

    • Utilized the new NISLGaze dataset, featuring diverse head positions and orientations for detailed analysis.
    • Evaluated the influence of calibration data parameters: number of targets, images per target, and head positions.
    • Developed and tested the Gaze Estimation with Decomposition and Debiasing Network (GEDDNet) based on these findings.

    Main Results:

    • High-quality gaze calibration can be achieved with minimal data, specifically a single gaze target and head position.
    • Incorporating head orientation variability during calibration enhances performance.
    • GEDDNet achieved over 6.3% improvement compared to state-of-the-art methods and demonstrated robust performance with or without calibration.

    Conclusions:

    • Minimal calibration data (single target, single head position) is sufficient for high-quality gaze estimation.
    • Varied head orientation during calibration is beneficial.
    • GEDDNet offers a versatile solution, performing well "out of the box" and improving with calibration, supported by new theoretical analysis.