Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Median01:08

Median

Besides mean, the median is a widely used measure of central tendency. Typically, median is defined as the central or middle value of a data set, measured by arranging the data elements in an increasing or decreasing order. Since this middle value is not affected by the precise numerical values of the outliers or fluctuations, it is insensitive to them. Hence, in cases where a data set may have outliers or the extreme values are not known, the median is a better measure of the central tendency...
Local Maximum and Minimum Values01:31

Local Maximum and Minimum Values

In multivariable calculus, a function of two variables can exhibit local maximum or minimum values at certain points on its surface. A local maximum occurs when the function's value at a point is greater than at all nearby points, while a local minimum occurs when the function’s value is less than at all nearby locations. These points are referred to as local extrema and are of central importance in optimization problems.Local extrema are found at critical points, where the surface becomes...
Sign Test for Median of Single Population01:20

Sign Test for Median of Single Population

In general, the sign test serves as a nonparametric method to test hypotheses about the median of a single population when the data does not follow a known distribution. This simplicity makes it particularly useful for small sample sizes or when the assumptions of parametric tests cannot be met. The process begins with identifying a null hypothesis, typically stating that the population median equals a specific value. The alternative hypothesis could be that the median is either not equal to,...
Midrange01:07

Midrange

A somewhat easy to compute quantitative estimate of a data set’s central tendency is its midrange, which is defined as the mean of the minimum and maximum values of an ordered data set.
Simply put, the midrange is half of the data set’s range. Similar to the mean, the midrange is sensitive to the extreme values and hence the prospective outliers. However, unlike the mean, the midrange is not sensitive to all the values of the data set that lie in the middle. Thus, it is prone to outliers and...
Measures of Central Tendency02:16

Measures of Central Tendency

The "center" of a data set is also a way of describing location. The two most widely used measures of the "center" of the data are the mean (average) and the median. The words "mean" and "average" are often used interchangeably. The substitution of one word for the other is common practice. The technical term is "arithmetic mean" and "average" is technically a center location. However, in practice among non-statisticians, "average" is commonly accepted for "arithmetic mean."
Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Incubating artificial intelligence initiatives and careers in dermatology.

The Journal of investigative dermatology·2026
Same author

Altered Interictal Bed Nucleus of Stria Terminalis Connectivity in Patients With Temporal Lobe Epilepsy.

Neurology·2025
Same author

Neuronal Decoding of Decisions in Multidimensional Feature Space Using a Gated Recurrent Variational Autoencoder.

bioRxiv : the preprint server for biology·2025
Same author

Distinguishing benign and malignant myxoid soft tissue tumors: Performance of radiomics vs. radiologists.

PloS one·2025
Same author

The relationship between channel interaction, electrode placement, and speech perception in adult cochlear implant users.

The Journal of the Acoustical Society of America·2024
Same author

Frequency-to-Place Mismatch and Cochlear Implant Outcomes by Electrode Type.

JAMA otolaryngology-- head & neck surgery·2024
Same journal

Through the Looking Glass: A Dual Perspective on Weakly-Supervised Few-Shot Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Mask-guided Asymmetric Contrastive and Semantic Alignment for Unsupervised Person Re-Identification.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Hyperbolic Cycle Alignment for Infrared-Visible Image Fusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Learning Gaze Synthesizer via 3D-eye Controlled Diffusion and Cross-domain Feature Alignment.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Underlying Semantic Diffusion for Effective and Efficient In-Context Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

DiffRES: Unleashing Text-to-Image Diffusion Models for Generative Referring Expression Segmentation without Information Leakage.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
See all related articles

Related Experiment Video

Updated: Jul 7, 2026

Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters
15:25

Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters

Published on: February 4, 2018

Topological median filters.

Hakan Güray Senel1, Richard Alan Peters, Benoit Dawant

  • 1Electrical Engineering Department, Anadolu University, Eskisehir, 26470, Turkey. senel@mmf.mm.anadolu.edu.tr

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 5, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a novel topological median filter for image processing. This new filter enhances edge detection in noisy images by improving upon conventional median filters using fuzzy connectedness.

Related Experiment Videos

Last Updated: Jul 7, 2026

Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters
15:25

Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters

Published on: February 4, 2018

Area of Science:

  • Image processing
  • Computer vision
  • Digital signal processing

Background:

  • Conventional median filters are widely used for noise reduction in images.
  • However, they can struggle with accurate edge detection in the presence of noise and complex image features.
  • Existing methods for fuzzy connectedness offer potential for improved image analysis.

Purpose of the Study:

  • To define and test a new topological median filter for image processing.
  • To improve edge extraction in noisy images compared to conventional median filters.
  • To leverage fuzzy connectedness for enhanced image analysis.

Main Methods:

  • Definition of alpha-connectivity for pixel neighborhood analysis.
  • Development of an algorithm to compute pixel connectedness.
  • Creation of a connectivity map to identify image topology.
  • Median calculation on the connectivity map for pixel value estimation.
  • Implementation and testing of four distinct topological median filters.

Main Results:

  • The topological median filter effectively disconnects image features separated by brightness valleys.
  • It provides an improved estimate of the median pixel value within connected regions.
  • The filter is less susceptible to noise from disconnected features in the neighborhood.
  • Qualitative and statistical analyses demonstrate superior performance over conventional median filters.
  • Edge detection accuracy is significantly enhanced on topologically median filtered images.

Conclusions:

  • The topological median filter offers a robust approach to noise reduction and edge detection in digital images.
  • Its foundation in fuzzy connectedness and alpha-connectivity provides a novel way to analyze image topology.
  • This method presents a significant advancement for applications requiring precise edge extraction in noisy environments.