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Related Concept Videos

Relative Motion Analysis using Rotating Axes-Problem Solving01:29

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 using Rotating Axes01:25

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...
Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
As the drone's propellers rotate, an upward force is generated that counteracts the force of gravity, enabling the drone to lift off from the ground. This initial movement of the drone is along a straight path, representing a form of translational motion. In this phase, every point on the drone...
Relative Motion Analysis - Velocity01:24

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...
Relative Motion Analysis using Rotating Axes - Acceleration01:22

Relative Motion Analysis using Rotating Axes - Acceleration

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. The absolute velocity of point B is determined by adding the absolute velocity of point A, the relative velocity of point B in the rotating frame, and the effects caused by the angular velocity within the rotating frame.
Time differentiation is...
Curvilinear Motion: Rectangular Components01:23

Curvilinear Motion: Rectangular Components

Curvilinear motion characterizes the movement of a particle or object along a curved path, notably evident when envisioning a car navigating a winding road. If the car starts at point A, its position vector is established within a fixed frame of reference, where the ratio of the position vector to its magnitude signifies the unit vector pointing in the position vector's direction.
As the car advances, its position evolves over time. Quantifying the car's velocity involves computing the time...

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Related Experiment Video

Updated: May 29, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Scene segmentation from visual motion using global optimization.

D W Murray1, B F Buxton

  • 1GEC Research Ltd., Long Range Research Laboratory, Hirst Research Centre, East Lane, Wembley HA9 7PP, England.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces an algorithm for scene and motion segmentation using optic flow. It employs a Bayesian approach with Markov random fields and simulated annealing to interpret moving planar surfaces.

Related Experiment Videos

Last Updated: May 29, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Robotics

Background:

  • Scene understanding and motion segmentation are crucial for autonomous systems.
  • Interpreting complex visual scenes with moving objects remains a challenge.
  • Existing methods often struggle with occlusions and non-rigid motion.

Purpose of the Study:

  • To develop and evaluate an algorithm for scene disposition and motion segmentation.
  • To utilize a Bayesian framework for optimal scene interpretation from optic flow.
  • To model scenes as collections of moving planar surface patches.

Main Methods:

  • Employed the maximum a posteriori (MAP) criterion for segmentation.
  • Modeled optic flow fields using spatial and temporal Markov random fields.
  • Integrated a method for reconstructing motion and orientation of planar facets.
  • Utilized simulated annealing for globally optimal segmentation search.

Main Results:

  • Demonstrated effective scene and motion segmentation from visual motion data.
  • The Bayesian approach successfully incorporated spatial and temporal continuity constraints.
  • The algorithm accurately predicted optic flow based on the scene segmentation.
  • Simulated annealing efficiently found globally optimal scene interpretations.

Conclusions:

  • The proposed algorithm provides a robust method for scene and motion segmentation.
  • The Bayesian framework with Markov random fields is well-suited for this task.
  • This approach advances the understanding of dynamic scenes in computer vision.