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Related Concept Videos

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...
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...
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 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...
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
Relative Motion Analysis - Acceleration01:10

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...

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Related Experiment Video

Updated: Jul 7, 2026

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
11:34

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

Spatio-temporal adaptive 3-D Kalman filter for video.

J Kim1, J W Woods

  • 1Samsung Semicond., San Jose, CA.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1997
PubMed
Summary
This summary is machine-generated.

This study introduces advanced three-dimensional Kalman filters for video analysis, enhancing spatial-temporal estimation. These filters, including motion-compensated and multi-model variants, improve accuracy in noisy video sequences.

Related Experiment Videos

Last Updated: Jul 7, 2026

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
11:34

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

Area of Science:

  • Computer Vision
  • Signal Processing
  • Image Analysis

Background:

  • Traditional Kalman filters are limited in handling spatio-temporal data in videos.
  • Existing two-dimensional (2-D) reduced update Kalman filter (RUKF) methods are extended for video applications.
  • Need for efficient and robust estimators for dynamic video sequences.

Purpose of the Study:

  • To develop and evaluate three-dimensional (3-D) Kalman filters for video.
  • To introduce motion-compensated (MC-RUKF) and multi-model (MM-MC-RUKF) extensions for improved performance.
  • To present a novel multiscale model detection algorithm for high-noise environments.

Main Methods:

  • Extension of the 2-D RUKF to a shift-invariant 3-D RUKF for video.
  • Development of motion-compensated MC-RUKF coupled with a motion estimator.
  • Implementation of MM-MC-RUKF with local image model detection.
  • Introduction of a multiscale model detection algorithm for noise resilience.

Main Results:

  • The 3-D RUKF offers efficiency advantages over 3-D Wiener filters.
  • MC-RUKF demonstrates improved performance with motion estimation.
  • MM-MC-RUKF adapts to variations in temporal and spatial correlations.
  • The multiscale algorithm enhances performance in high noise.

Conclusions:

  • The proposed 3-D Kalman filters provide robust spatio-temporal estimation for video.
  • Motion compensation and multi-model approaches significantly enhance video filtering accuracy.
  • The multiscale detection algorithm is effective in challenging, high-noise video conditions.