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Introduction to Scalers01:21

Introduction to Scalers

Many familiar physical quantities can be specified completely by giving a single number and the appropriate unit. For example, "a class period lasts 50 min," or "the gas tank in my car holds 65 L," or "the distance between the two posts is 100 m." A physical quantity that can be specified completely in this manner is called a scalar quantity. The word "scalar" is a synonym for "number." Time, mass, distance, length, volume, temperature, and energy are some examples of scalar quantities.
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Related Experiment Video

Updated: Jul 7, 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

Multiscale recursive medians, scale-space, and transforms with applications to image processing.

J A Bangham1, P Ling, R Young

  • 1Sch. of Inf. Syst., East Anglia Univ., Norwich.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1996
PubMed
Summary
This summary is machine-generated.

A novel recursive median filter cascade, the R-sieve, simplifies signals while preserving scale-space and offering robustness. This image processing technique enables pattern recognition through a granularity domain transform, proving efficient for machine vision.

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Last Updated: Jul 7, 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

Area of Science:

  • Image Processing
  • Computer Vision
  • Signal Processing

Background:

  • Scale-space representation is crucial for signal simplification.
  • Gaussian filters preserve scale-space but lack robustness.
  • Recursive median filters offer potential for robust signal processing.

Purpose of the Study:

  • Introduce and characterize the R-sieve, a novel image processing tool.
  • Demonstrate the R-sieve's ability to preserve scale-space and enhance robustness.
  • Explore the R-sieve's application in pattern recognition via a granularity domain transform.

Main Methods:

  • A cascade of 1-D recursive median filters of increasing scale was employed.
  • The R-sieve was analyzed for its scale-space preservation properties.
  • A transform to the granularity domain was derived from successive R-sieve stages.
  • Idempotent matched sieves were used for pattern recognition in the granularity domain.

Main Results:

  • The R-sieve simplifies signals without introducing new extrema or edges, preserving scale-space.
  • The R-sieve demonstrates superior robustness compared to Gaussian filters.
  • The granularity domain transform allows for effective pattern and shape recognition.
  • The R-sieve is computationally efficient and related to alternating sequential filters.

Conclusions:

  • The R-sieve is a robust and efficient image processing tool that preserves scale-space.
  • The granularity domain transform facilitates pattern recognition in machine vision.
  • The R-sieve offers a valuable alternative to traditional filtering methods for specific applications.