Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Boundary Layer Characteristics01:18

Boundary Layer Characteristics

180
When a fluid encounters a solid surface, a boundary layer forms due to the interaction between the fluid's motion and the stationary surface. This phenomenon is characterized by a thin region adjacent to the surface where viscous forces dominate, influencing the fluid's velocity profile. The development of the boundary layer begins at the leading edge of the surface and evolves as the fluid moves downstream.As the fluid flows over the surface, friction between the fluid and the wall slows down...
180
Boundary Conditions: Lossless Lines01:21

Boundary Conditions: Lossless Lines

116
Consider a single-phase, two-wire, lossless transmission line terminated by an impedance at the receiving end and a source with Thevenin voltage and impedance at the sending end. The line, with length, has a surge impedance and wave velocity determined by the line's inductance and capacitance.
At the receiving end, the boundary condition states that the voltage equals the product of the receiving-end impedance and current. This relationship is expressed as a function of the incident and...
116
Electrostatic Boundary Conditions01:16

Electrostatic Boundary Conditions

515
Consider an external electric field propagating through a homogeneous medium. When the electric field crosses the surface boundary of the medium, it undergoes a discontinuity. The electric field can be resolved into normal and tangential components. The amount by which the field changes at any boundary is given by the difference between the field components above and below the surface boundary.
The surface integral of an electric field is given by Gauss's law in integral form and is related to...
515
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

6.4K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
6.4K
Boundary Conditions for Current Density01:25

Boundary Conditions for Current Density

905
Current density becomes discontinuous across an interface of materials with different electrical conductivities. The normal component of the current density is continuous across the boundary.
905
Histogram01:05

Histogram

13.9K
The histogram is a graphical representation in the x-y form of data distribution in a data set. The horizontal x-axis is labeled with what the data represents (for instance, distance from your home to school). The vertical y-axis is labeled either frequency or relative frequency (or percent frequency or probability).
A histogram graph consists of contiguous (adjoining) boxes. The heights of the bars correspond to frequency values. The graph will have the same shape with respective labels. The...
13.9K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Local cues enable classification of image patches as surfaces, object boundaries, or illumination changes.

Journal of vision·2026
Same author

A comparison of density-based and feature-based texture boundary segmentation.

Vision research·2025
Same author

Local cues enable classification of image patches as surfaces, object boundaries, or illumination changes.

bioRxiv : the preprint server for biology·2025
Same author

Trypophobia, skin disease, and the visual discomfort of natural textures.

Scientific reports·2024
Same author

Distinguishing shadows from surface boundaries using local achromatic cues.

PLoS computational biology·2022
Same author

Evaluating the 'skin disease-avoidance' and 'dangerous animal' frameworks for understanding trypophobia.

Cognition & emotion·2022
Same journal

Whole-Embryo 3D Quantification Reveals Conserved Topological Design and Scaling of Germ Layers in Xenopus.

bioRxiv : the preprint server for biology·2026
Same journal

scRNA-seq and genomics analyses reveal key mechanisms of inverted papilloma-associated sinonasal squamous cell carcinoma malignant transformation.

bioRxiv : the preprint server for biology·2026
Same journal

M1C IS NECESSARY FOR DARAXONRASIB RESISTANCE OF NSCLC KRAS(G12C) MUTANT CELLS.

bioRxiv : the preprint server for biology·2026
Same journal

A human-specific genetic modifier reconfigures large-scale cortical network dynamics underlying behavioral performance.

bioRxiv : the preprint server for biology·2026
Same journal

<i>Staphylococcus aureus</i> uses a eukaryotic-like uridyltransferase to make UDP-GlcNAc for cell wall synthesis.

bioRxiv : the preprint server for biology·2026
Same journal

Dynamic redistribution of eIF4F controls cap-dependent translation initiation.

bioRxiv : the preprint server for biology·2026
See all related articles

Related Experiment Video

Updated: Jul 21, 2025

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
12:08

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

24.6K

Second-order boundaries segment more easily when they are density-defined rather than feature-defined.

Christopher DiMattina1,2

  • 1Computational Perception Laboratory, Florida Gulf Coast University, Fort Myers, FL, USA 33965-6565.

Biorxiv : the Preprint Server for Biology
|July 28, 2023
PubMed
Summary
This summary is machine-generated.

Density cues are crucial for texture segmentation, with density boundaries showing lower segmentation thresholds than feature boundaries. This suggests density may utilize distinct neural mechanisms for visual processing.

More Related Videos

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

2.5K
Determination of Aggregate Surface Morphology at the Interfacial Transition Zone ITZ
08:59

Determination of Aggregate Surface Morphology at the Interfacial Transition Zone ITZ

Published on: December 16, 2019

8.2K

Related Experiment Videos

Last Updated: Jul 21, 2025

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
12:08

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

24.6K
Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

2.5K
Determination of Aggregate Surface Morphology at the Interfacial Transition Zone ITZ
08:59

Determination of Aggregate Surface Morphology at the Interfacial Transition Zone ITZ

Published on: December 16, 2019

8.2K

Area of Science:

  • Visual perception
  • Computational neuroscience
  • Psychophysics

Background:

  • Density is a key perceptual attribute of texture, significantly influencing texture segmentation.
  • The Filter-Rectify-Filter (FRF) model posits density as a second-order cue for texture segmentation.

Approach:

  • Directly tested FRF model predictions by comparing segmentation thresholds for compound feature and density boundaries.
  • Investigated summation of micropatterns in compound boundary segmentation.

Key Points:

  • Compound density boundaries exhibited lower segmentation thresholds than compound feature boundaries.
  • Segmentation performance for compound feature boundaries was impaired compared to individual channels.
  • Density segmentation appears to involve probability summation, unlike feature segmentation.

Conclusions:

  • Density segmentation may rely on neural mechanisms distinct from feature segmentation.
  • Findings support the hypothesis that density constitutes a separate psychophysical channel.