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Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

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Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
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A Novel Method for Motion Blur Detection and Quantification Using Signal Analysis on a Controlled Empirical Image

Woottichai Nonsakhoo1, Saiyan Saiyod1

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Summary

This study introduces a new framework to detect, locate, and quantify motion blur in single images. The method accurately estimates object velocity from blur measurements in controlled conditions.

Keywords:
controlled empirical datasetmotion blurmotion blur quantificationmovement artifactpolynomial regressionreference origin estimationscanline analysisself-similarityvelocity estimation

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Area of Science:

  • Computer Vision
  • Image Processing
  • Motion Analysis

Background:

  • Motion blur significantly degrades image quality in single-frame imaging.
  • Quantitative validation of motion blur is challenging due to the lack of ground-truth motion parameters in real-world images.

Purpose of the Study:

  • To present an interpretable, measure-first framework for detecting, localizing, and quantifying motion blur.
  • To enable quantitative validation of motion blur by estimating motion parameters from images.
  • To operate under a validated condition of one-dimensional horizontal uniform motion.

Main Methods:

  • The framework analyzes image rows as 1D spatial signals.
  • It employs Movement Artifact Position Estimation (MAPE) using scanline self-similarity.
  • Reference Origin Point Estimation (ROPE) and Movement Artifact Quantification (MAQ) are used for localization and blur magnitude summarization.

Main Results:

  • MAPE achieved 70-90% detection rates across velocities.
  • ROPE localized reference origins with 97-99% detection accuracy.
  • An empirical mapping from MAQ to velocity achieved R² = 0.9900, enabling calibrated velocity estimates.

Conclusions:

  • The developed framework provides a reliable method for quantifying motion blur and estimating velocity in controlled environments.
  • The study clarifies the empirical boundaries of the current controlled single-marker regime, especially concerning additive noise.
  • The approach is validated for one-dimensional horizontal uniform motion and does not claim generalization to complex scenarios.