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In geometry, measuring the direct distance between two points on a plane is essential in various practical and theoretical applications. Whether in navigation, engineering, or computer graphics, determining the shortest path between two locations involves using the distance formula. This formula is derived from the Pythagorean Theorem, which relates the lengths of the sides of a right triangle. On a coordinate plane, the horizontal and vertical distances between two points serve as the legs of...
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Updated: May 29, 2026

Quantifying Intermembrane Distances with Serial Image Dilations
07:45

Quantifying Intermembrane Distances with Serial Image Dilations

Published on: September 28, 2018

A dynamic programming algorithm for the distance between two finite areas.

R K Moore1

  • 1Department of Phonetics and Linguistics, University College London, London, England.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a novel two-dimensional dynamic programming algorithm for calculating the distance between finite areas, extending prior sequence alignment methods. This advancement offers new solutions for complex pattern matching and data analysis challenges.

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Area of Science:

  • Computer Science
  • Algorithms
  • Computational Linguistics

Background:

  • Dynamic programming effectively solves sequence alignment problems, crucial for speech recognition and text correction.
  • Existing methods primarily focus on one-dimensional sequence comparisons.

Purpose of the Study:

  • To extend dynamic programming techniques into two dimensions.
  • To present an algorithm for calculating the distance between two finite areas.

Main Methods:

  • Development of a novel two-dimensional dynamic programming algorithm.
  • Adaptation of sequence alignment principles to spatial data.

Main Results:

  • Successful formulation of an algorithm to compute the distance between finite areas.
  • Demonstration of the technique's applicability beyond one-dimensional sequences.

Conclusions:

  • The two-dimensional algorithm offers a powerful new tool for analyzing spatial data.
  • Potential applications exist in image processing, bioinformatics, and other fields requiring area comparison.