Jove
Visualize
Contact Us

Related Concept Videos

Gradient and Del Operator01:14

Gradient and Del Operator

In mathematics and physics, the gradient and del operator are fundamental concepts used to describe the behavior of functions and fields in space. The gradient is a mathematical operator that gives both the magnitude and direction of the maximum spatial rate of change. Consider a person standing on a mountain. The slope of the mountain at any given point is not defined unless it is quantified in a particular direction. For this reason, a "directional derivative" is defined, which is a vector...
Gradient Vectors and Their Applications01:19

Gradient Vectors and Their Applications

Every point on a topographical map corresponds to a particular elevation, so the landscape can be modeled as a surface whose height depends on horizontal position. From any given location, a hiker may face infinitely many directions, but only one direction produces the fastest possible increase in elevation. This unique route is called the direction of steepest ascent, and in multivariable calculus, it is represented by the gradient vector of the elevation function.The gradient vector points...
Gradient Fields01:27

Gradient Fields

A gradient field is a vector field derived from a scalar field. A scalar field assigns a single numerical value to every point in space, such as temperature, pressure, or electric potential. The gradient field describes how that value changes from point to point. It gives both the direction of the fastest increase and the rate of change in that direction.For a scalar field f(x, y), the gradient is written as\begin{equation*}\nabla f=\left\langle \jfrac{\partial f}{\partial x},\jfrac{\partial...
Significance of the Gradient Vector01:27

Significance of the Gradient Vector

A surface defined by a function of two variables can be understood by examining how it changes along specific directions. When one variable is held constant, the surface reduces to a curve that reflects variation in the other variable. For example, fixing one variable and moving parallel to a coordinate axis produces a cross-sectional curve. The slope of this curve at a given point represents how the function changes in that particular direction, providing a measure of local steepness.By...
Second Derivatives and Laplace Operator01:22

Second Derivatives and Laplace Operator

The first order operators using the del operator include the gradient, divergence and curl. Certain combinations of first order operators on a scalar or vector function yield second order expressions. Second-order expressions play a very important role in mathematics and physics. Some second order expressions include the divergence and curl of a gradient function, the divergence and curl of a curl function, and the gradient of a divergence function.
Consider a scalar function. The curl of its...
Maximizing the Directional Derivative01:25

Maximizing the Directional Derivative

The directional derivative is a central concept in multivariable calculus that describes how a function changes at a given point when moving in a specified direction. This direction is represented by a unit vector, ensuring that only the orientation influences the rate of change. By varying the direction, different rates of change can be observed, demonstrating that the directional derivative depends strongly on the chosen direction.The directional derivative is computed using the gradient...

You might also read

Related Articles

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

Sort by
Same author

Lower limb prosthetic considerations in persons with coagulopathies.

Haemophilia : the official journal of the World Federation of Hemophilia·2016
Same author

Multidimensional gain control in image representation and processing in vision.

Biological cybernetics·2014
Same author

Bihemifield visual stimulation reveals reduced lateral bias in dyslexia.

Annals of dyslexia·2013
Same author

Applications of limited-extent waves: an introduction.

Applied optics·2010
Same author

Natural signal classification by neural cliques and phase-locked attractors.

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference·2007
Same author

Radiological case of the month. Denouement and discussion: synovial chondromatosis.

Archives of pediatrics & adolescent medicine·2001
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 Experiment Video

Updated: Jul 7, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

The design of two-dimensional gradient estimators based on one-dimensional operators.

M Azaria1, I Vitsnudel, Y Y Zeevi

  • 1Dept. of Electr. Eng., Technion-Israel Inst. of Technol., Haifa.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1996
PubMed
Summary

A new computational method extends one-dimensional (1-D) gradient estimators to two dimensions (2-D) for image processing. This simpler 1-D design approach also allows for higher-order derivative estimators.

Related Experiment Videos

Last Updated: Jul 7, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Area of Science:

  • Image Processing
  • Computational Mathematics

Background:

  • Two-dimensional (2-D) gradient estimators are essential tools in image processing.
  • Existing methods for 2-D gradient estimation can be complex in design.

Purpose of the Study:

  • To present a computational procedure for extending one-dimensional (1-D) gradient estimators to two dimensions (2-D).
  • To offer a simpler alternative to existing 2-D surface fitting methods.

Main Methods:

  • A computational procedure is detailed for adapting 1-D gradient estimators to a 2-D context.
  • The proposed method is shown to be equivalent to surface fitting but with a simplified 1-D design.

Main Results:

  • The procedure successfully extends 1-D gradient estimators to 2-D.
  • The method's design simplicity, rooted in 1-D principles, is highlighted.
  • The procedure's applicability to constructing higher-order derivative estimators is demonstrated.

Conclusions:

  • The presented computational procedure offers a simplified and effective way to implement 2-D gradient estimation.
  • This approach facilitates the development of more advanced derivative estimators in image processing.