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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.
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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.
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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.
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Curvilinear Motion: Normal and Tangential Components01:27

Curvilinear Motion: Normal and Tangential Components

When a car traverses a curved road, its motion can be elucidated by breaking it down into tangential and normal components. The car-centric coordinates attached to the vehicle move with it.
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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...

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Updated: Jun 17, 2026

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

Tracking motion, deformation, and texture using conditionally gaussian processes.

Tim K Marks1, John R Hershey, Javier R Movellan

  • 1Mitsubishi Electric Research Laboratories, 201 Broadway, Cambridge, MA 02139, USA. tmarks@merl.com

IEEE Transactions on Pattern Analysis and Machine Intelligence
|January 16, 2010
PubMed
Summary
This summary is machine-generated.

We developed G-flow, a generative model for 3D nonrigid object tracking. This advanced computer vision algorithm accurately tracks facial expressions and head motion from video, outperforming existing methods.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • 3D Reconstruction

Background:

  • 3D nonrigid object tracking is crucial for applications like facial expression analysis.
  • Existing methods struggle with accuracy and robustness due to limitations in realistic data and algorithms.

Purpose of the Study:

  • To introduce G-flow, a novel generative model and inference algorithm for joint 3D nonrigid object tracking.
  • To address the lack of realistic ground truth data for evaluating nonrigid tracking algorithms.
  • To demonstrate the superiority of G-flow over traditional computer vision approaches.

Main Methods:

  • Developed G-flow, a generative model for joint inference of 3D position, orientation, nonrigid deformations, and textures.
  • Formulated optimal inference as a conditionally Gaussian stochastic filtering problem.
  • Introduced a practical method for generating ground truth data and a new face video dataset.

Main Results:

  • G-flow enables joint inference of complex 3D object dynamics and appearance.
  • The optimal inference reveals a new class of computer vision algorithms.
  • Evaluated on a new dataset, G-flow significantly outperforms deterministic optic-flow methods in robustness and accuracy.

Conclusions:

  • G-flow represents a significant advancement in 3D nonrigid object tracking.
  • The proposed method and dataset facilitate more rigorous evaluation of nonrigid tracking techniques.
  • G-flow offers a more robust and accurate solution for real-world applications involving dynamic 3D objects.