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Uncertainty in Measurement: Accuracy and Precision03:37

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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. 
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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.  Highly accurate...
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Consider two point charges, each exerting Coulomb force on the other. It is possible to describe the Coulomb interaction via an intermediate step by defining a new physical quantity called the electric field.
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A moving charge or a current creates a magnetic field in the surrounding space, in addition to its electric field. The magnetic field exerts a force on any other moving charge or current that is present in the field. Like an electric field, the magnetic field is also a vector field. At any position, the direction of the magnetic field is defined as the direction in which the north pole of a compass needle points.
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Electric fields generated by static charges, often referred to as electrostatic fields, are characteristically different from electric fields created by time-varying magnetic fields. While the former is a conservative field, implying that no net work is done on a test charge if it goes around in a complete loop in the field, the latter is, by definition, not a conservative field; net work is done, and it is proportional to the rate of change of magnetic flux.
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Detecting the Water-soluble Chloride Distribution of Cement Paste in a High-precision Way
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High-Precision Edge Detection Guided byFlow Fields.

Bing Li, Yuchen Han, Shiyin Zhang

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    We introduce FFED, a novel flow field-guided edge detection model. FFED enhances complex target extraction and background information capture for improved visual tasks.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Artificial Intelligence

    Background:

    • Edge detection is crucial for visual tasks but struggles with complex textures and backgrounds.
    • Existing methods face challenges in extracting detailed information from intricate scenes.

    Purpose of the Study:

    • To propose FFED, a flow field-guided edge detection model.
    • To address limitations in current edge detection techniques for complex targets and backgrounds.

    Main Methods:

    • FFED utilizes a Feature Broadcast Module (FBM) to transfer semantic features to high-resolution details.
    • An Antagonistic Bio-inspired Spatial Attention Module (ABSAM) optimizes spatial modeling for complex backgrounds.
    • A novel pixel difference convolution, ALS, enhances gradient and subtle texture extraction.

    Main Results:

    • FFED achieved competitive results on benchmark datasets (NYUD, BSDS500, BIPED) and industrial data.
    • The model demonstrated effectiveness in extracting complex textured targets and background information.
    • FFED showed a positive auxiliary effect on downstream visual tasks.

    Conclusions:

    • FFED offers a robust solution for edge detection in complex visual scenes.
    • The proposed modules (FBM, ABSAM, ALS) significantly improve edge detection performance.
    • FFED advances the capabilities of edge detection for various computer vision applications.