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Curvilinear Motion: Rectangular Components01:23

Curvilinear Motion: Rectangular Components

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.
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Related Experiment Video

Updated: May 16, 2026

Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics
14:14

Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics

Published on: April 16, 2017

Novel approaches to the parametric cubic-spline interpolation.

Shao-Hua Hong1, Lin Wang, Trieu-Kien Truong

  • 1Department of Communication Engineering, Xiamen University, Fujian 361005, China. hongsh@xmu.edu.cn

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|November 30, 2012
PubMed
Summary

A new method uses opportunity costs to find the best parameter for cubic-spline interpolation (CSI) to improve image reconstruction quality. This approach enhances image processing performance without increasing computational load.

Related Experiment Videos

Last Updated: May 16, 2026

Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics
14:14

Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics

Published on: April 16, 2017

Area of Science:

  • Image Processing
  • Computer Vision
  • Numerical Analysis

Background:

  • Cubic-spline interpolation (CSI) enhances reconstructed image quality.
  • CSI relies on the least-squares method and cubic convolution interpolation (CCI) functions.
  • Determining optimal parameters for CSI in varied image types is challenging.

Purpose of the Study:

  • To propose a novel method for identifying optimal parameters for the CCI function within the CSI scheme.
  • To improve the performance of image reconstruction using CSI.

Main Methods:

  • A novel method based on opportunity costs is introduced to select the optimal parameter for the CCI function.
  • The proposed method is evaluated using an optimal four-point CCI function with the least-squares method.
  • An optimal six-point CSI scheme combined with a cross-zonal filter is also investigated.

Main Results:

  • The optimal four-point CCI function with the least-squares method demonstrates improved performance over existing CSI algorithms using the same computational resources.
  • The optimal six-point CSI scheme with a cross-zonal filter shows superior performance compared to the optimal four-point CSI scheme.
  • Computational complexity is not increased with the enhanced six-point CSI scheme.

Conclusions:

  • The proposed opportunity cost-based method effectively identifies optimal parameters for CSI, leading to enhanced image reconstruction.
  • The optimal six-point CSI scheme offers a significant performance improvement without additional computational cost, advancing image processing techniques.