Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Imaging Biological Samples with Optical Microscopy

Imaging Biological Samples with Optical Microscopy

Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...

You might also read

Related Articles

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

Sort by

Same author

Using explainable AI to align pre-university profiles with bachelor's degree success.

Scientific reports·2025

Same author

AI edge cloud service provisioning for knowledge management smart applications.

Scientific reports·2025

Same author

Effect of COVID-19 on the cultural identity of the Amazonian indigenous Waorani.

Heliyon·2025

Same author

Study of image sensors for enhanced face recognition at a distance in the Smart City context.

Scientific reports·2023

Same author

Multidimensional evaluation of teaching strategies adopted in the COVID-19 pandemic.

Universal access in the information society·2022

Same author

Evaluating Impact on Motivation and Academic Performance of a Game-Based Learning Experience Using Kahoot.

Frontiers in psychology·2020

Same journal

Solvent Extraction of Metals in the Circular Economy: Enhancing Resource Efficiency and Sustainability.

TheScientificWorldJournal·2026

Same journal

Agronomic Performance and Nutritive Value Evaluation of Desho Grass Varieties Under Supplementary Irrigation in Western Oromia, Ethiopia.

TheScientificWorldJournal·2026

Same journal

Physicians' and Hospital Administrators' Perspectives of Diagnosis-Related Groups (DRGs) in High-Income Countries: A Systematic Review.

TheScientificWorldJournal·2026

Same journal

The Eco-Friendly Preparation of Se, Zn, and Ag MONPs and Their Current Medical Applications and Drug Delivery for AD Diseases.

TheScientificWorldJournal·2026

Same journal

Fear of COVID-19: A Comparative Study Among University Students in Peru.

TheScientificWorldJournal·2026

Same journal

Opportunities and Challenges of Integrating Ethiopian Traditional Medicine System Into Modern Medicine: A Narrative Review.

TheScientificWorldJournal·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 28, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Trajectory-based morphological operators: a model for efficient image processing.

Antonio Jimeno-Morenilla¹, Francisco A Pujol¹, Rafael Molina-Carmona²

¹Departamento de Tecnología Informática y Computación, Universidad de Alicante, P.O. Box 99, E-03080 Alicante, Spain.

Thescientificworldjournal

|June 4, 2014

Summary

This summary is machine-generated.

We introduce the morphological trajectory model (MTM) for faster mathematical morphology operations. This new method accelerates image processing for real-time applications, independent of structuring element size.

More Related Videos

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline

Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline

Published on: January 30, 2019

Related Experiment Videos

Last Updated: Apr 28, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline

Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline

Published on: January 30, 2019

Area of Science:

Computer Vision
Image Processing
Computational Mathematics

Background:

Mathematical morphology operations are crucial in image processing.
Existing methods face challenges in real-time implementation due to computational demands.
There is a need for accelerated morphological operations for high-resolution images and industrial systems.

Purpose of the Study:

To present a novel model for accelerating mathematical morphology operations.
To introduce the morphological trajectory model (MTM) for efficient computation.
To enable real-time implementation of morphological filters.

Main Methods:

Deconstructing morphological filters into a sequence of basic operations.
Defining trajectory-based operations (e.g., dilation, erosion) via ordered application of basic operations.
Utilizing the morphological trajectory model (MTM) with various structuring elements, such as disks.

Main Results:

The proposed MTM enables efficient computation of morphological operations.
Experiments demonstrate the method's independence from structuring element size.
The model facilitates easy application to industrial systems and high-resolution image processing.

Conclusions:

The morphological trajectory model (MTM) offers a significant advancement in accelerating mathematical morphology.
This approach is suitable for real-time systems and diverse applications.
The method's robustness to structuring element size enhances its practical utility.