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Related Concept Videos

Oriented Surfaces01:30

Oriented Surfaces

A surface is called orientable if a consistent choice of unit normal vector can be made at every point on the surface. A thin soap film stretched across a wire loop provides a familiar example. The film separates the air on one side from the air on the other, so one side can be selected as positive and the opposite side as negative. Once this choice is made, a unit normal vector can be assigned smoothly across the entire surface.At each point on the soap film, a unit normal vector points...
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A parametric surface in three-dimensional space is defined through a vector-valued function\begin{equation*}\mathbf{r}(u, v) = x(u, v)\mathbf{i} + y(u, v)\mathbf{j} + z(u, v)\mathbf{k}\end{equation*}where u and v are parameters within a specified domain D in the uv-plane. The functions x(u, v), y(u, v), and z(u, v) define the coordinates of points on the surface. As u and v vary over D, the position vector r(u, v) traces a continuous surface in space. This parametric representation is essential...
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A level surface consists of all points in space where a function of three variables takes the same fixed value. If a point lies on this surface, understanding the surface’s geometry there requires more than just knowing the point’s coordinates; it requires describing how the surface is oriented, or how it tilts, near that point.To probe this local geometry, imagine tracing a path that stays entirely on the level surface and passes through the point of interest. This path can be described as a...
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Related Experiment Video

Updated: Jun 20, 2026

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
09:10

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

Modeling visual search on a rough surface.

Alasdair D F Clarke1, Mike J Chantler, Patrick R Green

  • 1The Texture Lab, School of Mathematics and Computer Science, Heriot-Watt University, Edinburgh, Scotland, EH14 4AS, UK. adfc1@hw.ac.uk

Journal of Vision
|September 18, 2009
PubMed
Summary
This summary is machine-generated.

A linear, non-linear, linear (LNL) model accurately simulates human visual search performance on textured surfaces. This finding holds true for both noise and near-regular textures across various task difficulties.

Related Experiment Videos

Last Updated: Jun 20, 2026

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
09:10

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

Area of Science:

  • Computational Neuroscience
  • Computer Vision
  • Human Visual Perception

Background:

  • The linear, non-linear, linear (LNL) model has shown success in texture segmentation tasks.
  • Understanding visual search mechanisms is crucial for both artificial and natural vision systems.

Purpose of the Study:

  • To evaluate the efficacy of a simple LNL model in simulating human performance during visual search tasks.
  • To assess model performance on diverse textured surfaces, including 1/f(beta)-noise and near-regular textures.

Main Methods:

  • Utilized a linear, non-linear, linear (LNL) computational model.
  • Compared model-based search performance against human participant data.
  • Tested the model on two distinct texture classes: 1/f(beta)-noise and near-regular textures.

Main Results:

  • The LNL model demonstrated comparable search performance to human participants.
  • Model and human performance showed no significant differences across a broad spectrum of task difficulties.
  • The model's accuracy was consistent for both 1/f(beta)-noise and near-regular textured surfaces.

Conclusions:

  • A simple LNL model can effectively simulate human visual search behavior on textured backgrounds.
  • The findings support the LNL model's potential as a tool for understanding and replicating visual search mechanisms.