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Polar Coordinate System01:30

Polar Coordinate System

The polar coordinate system provides a natural way to describe points in the plane when distances and directions are more meaningful than horizontal and vertical displacements. It is especially useful for modeling non-rectangular regions such as circles and spirals, where symmetry about a center point is easier to express than it is in a rectangular grid. A familiar example is a ship’s plan position indicator, which marks detected targets as dots positioned relative to the ship at the display’s...
Polar Coordinates01:24

Polar Coordinates

The polar coordinate system offers an alternative to the Cartesian coordinate system for specifying points in a plane, using a distance and an angle instead of x and y coordinates. This system is particularly advantageous in situations involving circular or rotational symmetry, such as in physics or engineering problems involving waves, oscillations, or orbital paths.Defining Polar CoordinatesIn polar coordinates, a point is represented as P(r, ��), where r is the radial distance from a fixed...
Integration Applied to Polar Coordinates to Find Areas01:15

Integration Applied to Polar Coordinates to Find Areas

A rotating lawn sprinkler with an uneven spray pattern produces a variable reach as it distributes water in different directions. This directional variation in spray distance can be effectively described using polar coordinates, where the distance from the center is represented as a function of the angle of rotation. The path traced by the spray then forms a polar curve, which captures the irregularities in the sprinkler’s reach across the full rotation.To calculate the total area watered by...
Integration Applied to Polar Coordinates to Find Arc Lengths01:26

Integration Applied to Polar Coordinates to Find Arc Lengths

In polar coordinates, a plane curve is described by a radial distance r from a fixed point, called the pole, and an angle θ measured from a reference direction. This system is especially useful for paths that naturally involve rotation, such as an expanding spiral followed by a search drone. If the hiker’s last known position is treated as the pole, then the drone’s location at any instant can be represented by the polar equation r = f(θ), where the distance from the pole changes as the drone...

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Related Experiment Video

Updated: Jun 22, 2026

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities
07:13

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

Published on: October 27, 2023

Image registration using adaptive polar transform.

Rittavee Matungka1, Yuan F Zheng, Robert L Ewing

  • 1Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43210, USA. matungkr@ece.osu.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|June 13, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive polar transform (APT) for robust image registration, overcoming limitations of the log-polar transform (LPT). The new method achieves accurate and efficient registration, even with occluded or altered images.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Geometric Transformations

Background:

  • Image registration is crucial for comparing and integrating visual data from multiple images.
  • Log-polar transform (LPT) offers rotation and scale invariance but suffers from nonuniform sampling, limiting its use with altered or occluded images.

Purpose of the Study:

  • To develop a novel image registration algorithm that overcomes LPT's limitations while preserving robustness to scale and rotation.
  • To improve accuracy and reduce computational load compared to conventional LPT methods.
  • To enable effective registration of images subjected to occlusion and alteration.

Main Methods:

  • Introduced a novel adaptive polar transform (APT) for uniform image sampling in Cartesian coordinates.
  • Combined APT with a projection transform and matching mechanism for efficient registration.
  • Employed Gabor feature extraction and a new search scheme to recover translation and accelerate localization.
  • Developed an image comparison scheme to identify differing image areas.

Main Results:

  • The proposed APT-based method demonstrates superior accuracy and reduced computational load compared to conventional LPT.
  • Achieved robust registration for images with scale, rotation, translation, occlusion, and alteration.
  • Successfully localized image differences using the proposed comparison scheme.

Conclusions:

  • The novel adaptive polar transform (APT) offers a robust and effective solution for image registration, particularly for challenging scenarios involving image alteration and occlusion.
  • The method provides a significant advancement over traditional log-polar transform techniques in image processing applications.