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Related Concept Videos

Curvilinear Motion: Rectangular Components01:23

Curvilinear Motion: Rectangular Components

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Curvilinear motion characterizes the movement of a particle or object along a curved path, notably evident when envisioning a car navigating a winding road. If the car starts at point A, its position vector is established within a fixed frame of reference, where the ratio of the position vector to its magnitude signifies the unit vector pointing in the position vector's direction.
As the car advances, its position evolves over time. Quantifying the car's velocity involves computing the...
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Deconvolution01:20

Deconvolution

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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
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Deformation in a Circular Shaft01:10

Deformation in a Circular Shaft

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One of the distinctive characteristics of circular shafts is their ability to maintain their cross-sectional integrity under torsion. In other words, each cross-section continues to exist as a flat, unaltered entity, simply rotating like a solid, rigid slab. To understand the distribution of shearing stress within such a shaft, consider a cylindrical section inside this circular shaft. This section has a length of L and a radius of R, with one end fixed. The radius of the cylindrical section is...
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Curvilinear Motion: Polar Coordinates01:27

Curvilinear Motion: Polar Coordinates

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In polar coordinates, the motion of a particle follows a curvilinear path. The radial coordinate symbolized as 'r,' extends outward from a fixed origin to the particle, while the angular coordinate, 'θ,' measured in radians, represents the counterclockwise angle between a fixed reference line and the radial line connecting the origin to the particle.
The particle's location is described using a unit vector along the radial direction. Deriving the particle's position...
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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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Plastic Deformation in Circular Shafts01:20

Plastic Deformation in Circular Shafts

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When materials are subjected to forces that surpass their yield strength, they undergo a process known as plastic deformation. This results in a permanent alteration or strain in their structure. This concept can be specifically applied to circular shafts, where the deformation leads to a change in its shape. The precise evaluation of this plastic deformation requires understanding the stress distribution within the circular shaft, which is achieved by calculating the maximum shearing stress in...
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Related Experiment Video

Updated: Aug 10, 2025

Quantifying Intermembrane Distances with Serial Image Dilations
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Image Vignetting Correction Using a Deformable Radial Polynomial Model.

Artur Bal1,2, Henryk Palus1

  • 1Department of Data Science and Engineering, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland.

Sensors (Basel, Switzerland)
|February 11, 2023
PubMed
Summary

A new Deformable Radial Polynomial (DRP) model corrects image vignetting, a common radiometric error. This model offers improved accuracy over the standard Radial Polynomial (RP) model for lens-camera systems.

Keywords:
approximation functionflat-field correctionimage shadingimage vignettinglow-level visionsingle-image vignetting correctionvignetting correctionvignetting modeling

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Area of Science:

  • Computer Vision
  • Image Processing
  • Optical Engineering

Background:

  • Image vignetting is a significant radiometric error in lens-camera systems.
  • Vignetting correction is crucial for accurate image analysis and applications.
  • Existing methods like flat-field correction rely on accurate vignetting models.

Purpose of the Study:

  • To introduce a novel vignetting model, the Deformable Radial Polynomial (DRP) model.
  • To address the limitations of the standard Radial Polynomial (RP) model for non-radial vignetting.
  • To provide a computationally simple yet universal approach to vignetting correction.

Main Methods:

  • Development of the Deformable Radial Polynomial (DRP) model.
  • Utilizing distance transformation and minimization techniques.
  • Matching radial models to non-radial vignetting in lens-camera systems.

Main Results:

  • The DRP model demonstrates superior performance compared to the RP model.
  • Experimental results show significant improvements in vignetting correction accuracy (up to 50%).
  • The DRP model balances simplicity with the universality of complex models.

Conclusions:

  • The DRP model offers an effective solution for correcting non-radial image vignetting.
  • This new model enhances the accuracy of radiometric measurements in imaging systems.
  • The DRP model provides a practical advancement for lens-camera system calibration.