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

Relative Motion Analysis using Rotating Axes

Relative Motion Analysis using Rotating Axes

Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it instrumental in...

Relative Motion Analysis using Rotating Axes-Problem Solving

Relative Motion Analysis using Rotating Axes-Problem Solving

Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...

Relative Motion Analysis - Velocity

Relative Motion Analysis - Velocity

A stroke engine has a slider-crank mechanism that converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider.
When an external force is exerted, it sets the crank into a rotational movement. This, in turn, instigates the motion of the connecting rod, leading to what is referred to as a general plane motion. This process involves two key points - point A on the connecting rod...

Shape and Texture of Coarse Aggregate

Shape and Texture of Coarse Aggregate

Aggregate shape is classified based on the relative sharpness or roundness of the edges and corners. This classification includes categories like rounded, angular, elongated, and flaky, each with specific characteristics. Rounded aggregates, fully shaped by attrition, are typical of river or seashore gravel, while angular aggregates, such as crushed rock, have well-defined edges. Aggregates that are elongated and flaky are less desirable, as they can reduce the workability and strength of...

Linear Approximations

Linear Approximations

For a differentiable function of two variables, linear approximation estimates values near a known point by replacing the curved surface with its tangent plane. Consider the function\begin{equation*}f(x,y)=x^2+3y^2\end{equation*}near the point (2, 1). The exact value at this point is f(2, 1) = 22 + 3(1)2 = 4 + 3 = 7.The linear approximation of f(x, y)) near (a, b) is\begin{equation*}L(x,y)=f(a,b)+f_x(a,b)(x-a)+f_y(a,b)(y-b)\end{equation*}First, compute the partial derivatives: fx(x, y) = 2x and...

Relative Motion Analysis - Acceleration

Relative Motion Analysis - Acceleration

A slider-crank mechanism converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...

You might also read

Related Articles

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

Sort by

Same author

Long-correlation image models for textures with circular and elliptical correlation structures.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2008

Same author

Generalized circular autoregressive models for isotropic and anisotropic Gaussian textures.

Journal of the Optical Society of America. A, Optics, image science, and vision·2001

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 7, 2026

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

2-D moving average models for texture synthesis and analysis.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|February 16, 2008

Summary

This summary is machine-generated.

A novel random field model using moving average time-series analysis is introduced for creating stochastic and structured textures. This method enables accurate texture synthesis and parameter estimation for realistic image generation.

Related Experiment Videos

Last Updated: Jul 7, 2026

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

Area of Science:

Computer Vision
Image Processing
Statistical Modeling

Background:

Stochastic and structured textures present challenges in accurate modeling and synthesis.
Existing methods may lack the flexibility to capture complex textural properties.

Discussion:

A random field model integrating moving average (MA) time-series concepts is proposed.
A frequency domain algorithm is developed for synthesizing MA textures.
Maximum likelihood estimators and Cramer-Rao lower bounds are derived for robust parameter estimation and accuracy assessment.

Key Insights:

The proposed model effectively captures both stochastic and structured components of textures.
The frequency domain synthesis algorithm allows for efficient generation of realistic MA textures.
Derived estimators provide a quantifiable measure of accuracy for texture modeling.

Outlook:

Potential applications in realistic image synthesis, texture analysis, and computer graphics.
Further research could explore extensions to non-stationary textures or different time-series models.
Validation on diverse real-world texture datasets will be crucial for broader adoption.