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The Uncertainty Principle04:08

The Uncertainty Principle

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Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...
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Uncertainty in Measurement: Reading Instruments02:46

Uncertainty in Measurement: Reading Instruments

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Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
53.9K
Non-Verbal Cues01:29

Non-Verbal Cues

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Non-verbal communication extends beyond gestures and facial expressions to include vocal elements known as paralanguage. Paralanguage consists of non-verbal vocal cues such as pitch, loudness, speech rate, pauses, and non-verbal vocalizations like laughter, sighs, and moans. These elements not only accompany speech but also provide critical emotional and contextual information.The Role of Paralanguage in CommunicationParalanguage adds depth to spoken language by conveying emotions and...
343
Uncertainty: Overview00:59

Uncertainty: Overview

1.8K
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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Uncertainty in Measurement: Significant Figures03:34

Uncertainty in Measurement: Significant Figures

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All the digits in a measurement, including the uncertain last digit, are called significant figures or significant digits. Note that zero may be a measured value; for example, if a scale that shows weight to the nearest pound reads “140,” then the 1 (hundreds), 4 (tens), and 0 (ones) are all significant (measured) values.
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Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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Related Experiment Video

Updated: Feb 12, 2026

Video Imaging and Spatiotemporal Maps to Analyze Gastrointestinal Motility in Mice
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Video Imaging and Spatiotemporal Maps to Analyze Gastrointestinal Motility in Mice

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Unsupervised Uncertainty Estimation Using Spatiotemporal Cues in Video Saliency Detection.

Tariq Alshawi, Zhiling Long, Ghassan AlRegib

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |March 24, 2018
    PubMed
    Summary
    This summary is machine-generated.

    This study quantifies computational video saliency reliability by analyzing spatial and spatiotemporal correlations. The developed algorithm estimates pixel-wise uncertainty, improving saliency-based video processing and risk assessment.

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    Area of Science:

    • Computer Vision
    • Human-Computer Interaction
    • Signal Processing

    Background:

    • Computational saliency models predict visual attention but lack reliability quantification.
    • Assessing the trustworthiness of saliency maps is crucial for video processing applications.
    • Existing methods do not adequately address the inherent uncertainty in saliency predictions.

    Purpose of the Study:

    • To develop a method for quantifying the reliability of computational video saliency maps.
    • To enable more robust saliency-based video processing algorithms and objective risk assessment.
    • To introduce a pixel-wise uncertainty estimation technique based on local saliency correlations.

    Main Methods:

    • Explored spatial and spatiotemporal correlations in saliency and human eye-fixation data.
    • Developed an unsupervised algorithm to estimate pixel-wise uncertainty maps based on local neighborhood divergence.
    • Proposed a systematic procedure for evaluating uncertainty estimation performance using ground truth data.

    Main Results:

    • The proposed algorithm effectively estimates uncertainty by measuring pixel divergence from its local neighborhood.
    • Experiments demonstrated significant improvements in accuracy (up to 63%) over state-of-the-art methods.
    • The algorithm is unsupervised, computationally efficient, and flexible for various video content.

    Conclusions:

    • The developed method provides a reliable way to quantify computational video saliency.
    • The uncertainty estimation technique enhances the practical utility of saliency-based video processing.
    • This approach facilitates more objective risk assessment in applications relying on visual attention prediction.