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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

50.9K
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...
50.9K
Uncertainty: Overview00:59

Uncertainty: Overview

1.7K
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.
1.7K
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

100.5K
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
100.5K
Uncertainty in Measurement: Significant Figures03:34

Uncertainty in Measurement: Significant Figures

80.8K
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.
80.8K
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

10.4K
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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Updated: Jan 24, 2026

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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Creating Objects and Object Categories for Studying Perception and Perceptual Learning

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Unc-SOD: Un marco de aprendizaje de incertidumbre para la detección de objetos pequeños

Xiang Yuan, Gong Cheng, Jiacheng Cheng

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |January 22, 2026
    PubMed
    Resumen
    Este resumen es generado por máquina.

    Este estudio presenta Unc-SOD, un marco novedoso para la detección de objetos pequeños (SOD). Modela eficazmente la incertidumbre para mejorar la identificación de objetos pequeños, logrando resultados de vanguardia en puntos de referencia clave.

    Palabras clave:
    detección de objetos pequeñosaprendizaje de incertidumbreredes de propuestas de regionesvisión por computadorainteligencia artificialaprendizaje automático

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    Área de la Ciencia:

    • Visión por Computadora
    • Inteligencia Artificial
    • Aprendizaje Automático

    Sus antecedentes:

    • La detección de objetos pequeños (SOD) es un desafío debido a las regiones informativas limitadas y la ambigüedad inherente en las instancias pequeñas.
    • Los detectores de dos etapas existentes tienen dificultades con la incertidumbre y la inconsistencia de las características en SOD.
    • Los métodos de muestreo actuales pueden ser engañados por objetivos pequeños irreconocibles, desperdiciando recursos computacionales.

    Objetivo del estudio:

    • Desarrollar un marco de aprendizaje de incertidumbre para la detección de objetos pequeños (Unc-SOD).
    • Abordar los desafíos de la incertidumbre y la inconsistencia de las características en la detección de objetos pequeños.
    • Mejorar la precisión y la eficiencia de la detección de objetos pequeños en escenas complejas.

    Principales métodos:

    • Se incorporó una rama de incertidumbre auxiliar en la Red de Propuesta de Regiones (RPN) convencional para modelar la indeterminación a nivel de instancia.
    • Se utilizó la incertidumbre como un criterio de muestreo dinámico para las redes de propuestas, mejorando la selección de candidatos.
    • Se ideó una estrategia de Percepción e Interacción para capturar representaciones ricas y discriminatorias optimizando las características regionales.

    Principales resultados:

    • Unc-SOD logró un rendimiento de vanguardia en los puntos de referencia SODA-D y SODA-A.
    • El marco demostró mejoras significativas en los conjuntos de datos COCO, VisDrone y Tsinghua-Tencent 100K.
    • Los resultados indican un rendimiento superior en comparación con los detectores predominantes al manejar instancias pequeñas.

    Conclusiones:

    • Unc-SOD modela y aprovecha eficazmente la incertidumbre para mejorar la detección de objetos pequeños.
    • El marco propuesto ofrece una solución robusta para la desafiante tarea de SOD.
    • Este trabajo representa un intento seminal de incorporar el modelado de la incertidumbre en SOD, produciendo ganancias significativas de rendimiento.