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

Area Computation by the Alternative Coordinate Method01:24

Area Computation by the Alternative Coordinate Method

The alternative coordinate method, also known as the Shoelace Formula, is a technique for determining the area of a traverse using Cartesian coordinates. This method relies on the sequential arrangement of x and y coordinates for each point of the shape, ensuring accuracy and ease of application.In this approach, each corner's x and y coordinates are listed as fractions, with the x-coordinate as the numerator and the y-coordinate as the denominator. These coordinates are arranged sequentially...
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an organic...
State Function, Exact and Inexact Differentials01:27

State Function, Exact and Inexact Differentials

A state function is a thermodynamic property that depends solely on the current state of a system, irrespective of its history or how it arrived at that state. These functions are represented by capital letters, such as U, H, and S, which stand for internal energy, enthalpy, and entropy, respectively.For instance, the value of internal energy depends on the system's state variables and remains unaffected by the process path. This means that whether the system underwent a linear process or a...
Transmission-Line Differential Equations01:26

Transmission-Line Differential Equations

Transmission lines are essential components of electrical power systems. They are characterized by the distributed nature of resistance (R), inductance (L), and capacitance (C) per unit length. To analyze these lines, differential equations are employed to model the variations in voltage and current along the line.
Line Section Model
A circuit representing a line section of length Δx helps in understanding the transmission line parameters. The voltage V(x) and current i(x) are measured from the...
Area Between Curves: Integrating With Respect to y01:29

Area Between Curves: Integrating With Respect to y

Consider a planar region bounded by two curves that are both written as functions of the vertical variable, y. The left and right boundary curves are continuous between y = c and y = d, and these two horizontal lines define the vertical limits of the region. Because the boundaries depend on y rather than x, the area is most appropriately evaluated using horizontal slices.The area is obtained using the Riemann sum method. The region is divided into many thin horizontal strips, each having an...
Differential Form of Maxwell's Equations01:17

Differential Form of Maxwell's Equations

James Clerk Maxwell (1831–1879) was one of the significant contributors to physics in the nineteenth century. He is probably best known for having combined existing knowledge of the laws of electricity and the laws of magnetism with his insights to form a complete overarching electromagnetic theory, represented by Maxwell's equations. The four basic laws of electricity and magnetism were discovered experimentally through the work of physicists such as Oersted, Coulomb, Gauss, and Faraday.

You might also read

Related Articles

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

Sort by
Same author

GHS-OBAT: Global, open building attribute data reporting age, function, height and compactness at footprint level.

Data in brief·2025
Same author

The Multi-temporal and Multi-dimensional Global Urban Centre Database to Delineate and Analyse World Cities.

Scientific data·2024
Same author

Land use efficiency of functional urban areas: Global pattern and evolution of development trajectories.

Habitat international·2022
Same author

A crowdsourced global data set for validating built-up surface layers.

Scientific data·2022
Same author

Downscaling SSP-consistent global spatial urban land projections from 1/8-degree to 1-km resolution 2000-2100.

Scientific data·2021
Same author

Generalized Vertical Components of built-up areas from global Digital Elevation Models by multi-scale linear regression modelling.

PloS one·2021

Related Experiment Video

Updated: May 26, 2026

Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy
06:37

Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy

Published on: June 15, 2022

Differential area profiles: decomposition properties and efficient computation.

Georgios K Ouzounis1, Martino Pesaresi, Pierre Soille

  • 1Global Security and Crisis Management Unit, Institute for the Protection and Security of the Citizen, Joint Research Center of the European Commission, Ispra (VA), Italy. georgios.ouzounis@jrc.ec.europa.eu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|December 21, 2011
PubMed
Summary
This summary is machine-generated.

Differential area profiles (DAPs) decompose images using area zones for efficient analysis. This method speeds up pattern analysis and image segmentation in remote sensing and medical imaging.

More Related Videos

Exfoliation and Analysis of Large-area, Air-Sensitive Two-Dimensional Materials
10:18

Exfoliation and Analysis of Large-area, Air-Sensitive Two-Dimensional Materials

Published on: January 5, 2019

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

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

Published on: August 30, 2013

Related Experiment Videos

Last Updated: May 26, 2026

Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy
06:37

Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy

Published on: June 15, 2022

Exfoliation and Analysis of Large-area, Air-Sensitive Two-Dimensional Materials
10:18

Exfoliation and Analysis of Large-area, Air-Sensitive Two-Dimensional Materials

Published on: January 5, 2019

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

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

Published on: August 30, 2013

Area of Science:

  • Image analysis and computer vision.
  • Morphological image processing.
  • Pattern recognition.

Background:

  • Differential area profiles (DAPs) are multiscale image descriptors.
  • DAPs utilize connected morphological filters for scale-space representation.
  • Existing methods for DAP computation can be computationally intensive.

Purpose of the Study:

  • To explore the properties of image decomposition using area zones.
  • To develop an efficient method for computing operations on the DAP vector field.
  • To demonstrate the utility of area zone decomposition in practical applications.

Main Methods:

  • Image decomposition into area zones based on attribute extrema.
  • Efficient computation of area zones from hierarchical image structures.
  • Development of a one-pass method for DAP vector field operations using Max-Tree.

Main Results:

  • Area zones provide a novel way to analyze DAP vector fields.
  • The one-pass method offers computational advantages over conventional techniques.
  • Demonstrated effectiveness in convex/concave segmentation, remote sensing, and medical image analysis.

Conclusions:

  • Area zone decomposition offers an efficient approach to image analysis.
  • The one-pass method significantly improves computational performance for DAP operations.
  • This decomposition method has broad applicability in image segmentation and analysis.