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Association Areas of the Cortex01:21

Association Areas of the Cortex

Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
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Normal and Tangetial Components: Problem Solving

Consider a man with a mass of 70 kg seated in a chair connected to a pin support through a member BC. If the man maintains an upright position, the task is to determine the horizontal and vertical reactions of the chair on the man when the member makes a 45° angle with the horizontal. At this moment, the man has a speed of 5 m/s, increasing at a rate of 1 m/s².
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Centroid of a Body: Problem Solving

The centroid of a body is a crucial concept in engineering and physics. Finding the centroid of a body can help determine its stability, its balance point, and even its design. In this context, consider a thin wire bent in the form of a quarter circular arc. Polar coordinates are used to calculate the centroid. The wire is first divided into small differential elements of a length equal to the radius multiplied by the differential angle.
The x-coordinates and y-coordinates of each element's...

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Related Experiment Video

Updated: May 22, 2026

Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans
10:23

Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans

Published on: September 8, 2023

Iterative closest normal point for 3D face recognition.

Hoda Mohammadzade1, Dimitrios Hatzinakos

  • 1Department of Electrical and Computer Engineering, University of Toronto, 40 St. George Street, Toronto, ON M5S 2E4, Canada. hoda@-comm.utoronto.ca

IEEE Transactions on Pattern Analysis and Machine Intelligence
|May 16, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new 3D face recognition method using iterative closest normal points. This approach significantly reduces errors caused by facial expression variations, improving 3D face recognition accuracy.

Related Experiment Videos

Last Updated: May 22, 2026

Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans
10:23

Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans

Published on: September 8, 2023

Area of Science:

  • Computer Science
  • Biometrics
  • Pattern Recognition

Background:

  • Traditional 3D face recognition relies on computationally intensive point-to-point registration.
  • Existing methods are highly susceptible to variations in facial expressions.
  • A need exists for robust and efficient 3D face recognition techniques.

Purpose of the Study:

  • To develop a novel method for establishing point correspondences in 3D face recognition.
  • To address the challenge of facial expression variation in 3D face recognition.
  • To enhance the discriminatory power of features used in 3D face recognition.

Main Methods:

  • Introduced the iterative closest normal point (ICNP) method for correspondence finding.
  • Sampled closest normal points on generic reference and input 3D faces.
  • Applied discriminant analysis by minimizing within-class and maximizing between-class variability.

Main Results:

  • Demonstrated that surface normal vectors provide more discriminatory information than point coordinates.
  • Achieved high verification rates (99.6% and 99.2%) at a 0.1% false acceptance rate on the Face Recognition Grand Challenge database.
  • Reported significantly lower error rates compared to existing state-of-the-art methods.

Conclusions:

  • The ICNP method effectively addresses facial expression variations in 3D face recognition.
  • Surface normal information is crucial for accurate 3D face recognition.
  • The proposed method offers superior performance on large-scale 3D face databases.