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

Absolute Motion Analysis- General Plane Motion

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Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

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Kinematic Equations for Rotation01:30

Kinematic Equations for Rotation

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Orthogonal Trajectories01:26

Orthogonal Trajectories

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Related Experiment Video

Updated: May 11, 2026

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

Joint albedo estimation and pose tracking from video.

Sima Taheri1, Aswin C Sankaranarayanan, Rama Chellappa

  • 1Department of Electrical and Computer Engineering, University of Maryland, 1103 A.V. Williams, College Park, MD 20742, USA. taheri@cs.umd.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|May 18, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel sequential algorithm for estimating object albedo from image sequences. The method accurately estimates albedo and object pose even with unknown initial conditions, improving object recognition.

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Area of Science:

  • Computer Vision
  • Computer Graphics
  • Robotics

Background:

  • Albedo, a key surface property, is crucial for object recognition but challenging to estimate from single images due to illumination variations, shadows, and non-Lambertian effects.
  • Existing single-image albedo estimation methods are limited by their inability to handle complex real-world imaging conditions.

Purpose of the Study:

  • To develop a robust sequential algorithm for estimating object albedo from image sequences of a known 3D object.
  • To address limitations of single-image methods by incorporating pose and illumination variations.
  • To enable illumination-insensitive object recognition through accurate albedo estimation.

Main Methods:

  • A sequential algorithm utilizing a Kalman filter for albedo estimation when object pose is known or estimated.
  • Extension to unknown poses using a Rao-Blackwellized particle filter (RBPF) for simultaneous pose tracking and albedo updating.
  • Marginalization of albedo for analytical Kalman filter estimation and importance sampling for pose estimation via projection error minimization on a spherical harmonic subspace.

Main Results:

  • The proposed algorithm effectively estimates albedo from image sequences with varying poses and illumination.
  • The Rao-Blackwellized particle filter approach demonstrates robust performance in simultaneous pose tracking and albedo estimation.
  • Validation through experiments with synthetic and real image sequences confirms the approach's effectiveness.

Conclusions:

  • The developed sequential algorithm provides an effective solution for albedo estimation in dynamic environments.
  • The method offers improved robustness against illumination changes and pose variations compared to single-image techniques.
  • The approach has practical applications in unconstrained, video-based face recognition and other object recognition tasks.