Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Difference from Background: Limit of Detection

Difference from Background: Limit of Detection

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...

Depth Perception and Spatial Vision

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.

You might also read

Related Articles

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

Sort by

Same author

Principles of Local and Global Grouping that Underlie Segmentation of Natural Texture Images.

bioRxiv : the preprint server for biology·2026

Same author

Quantifying Task-relevant Similarities in Representations Using Decision Variable Correlations.

Advances in neural information processing systems·2026

Same author

Quantifying task-relevant representational similarity using decision variable correlation.

ArXiv·2026

Same author

Optimal visual search with highly heuristic decision rules.

Journal of vision·2025

Same author

Target identification under high levels of amplitude, size, orientation and background uncertainty.

Journal of vision·2025

Same author

Optimal Visual Search with Highly Heuristic Decision Rules.

ArXiv·2024

Same journal

Layered social competition coordinates reproductive hierarchy formation in ants.

bioRxiv : the preprint server for biology·2026

Same journal

Combination epigenetic-targeted therapy increases the immunogenicity of poorly immunogenic sarcomas.

bioRxiv : the preprint server for biology·2026

Same journal

Loss of LanC-like proteins delays post-injury regeneration of aging skeletal muscles.

bioRxiv : the preprint server for biology·2026

Same journal

Integrative Transfer Network: Deep Transfer Learning Across Populations and Prediction Targets.

bioRxiv : the preprint server for biology·2026

Same journal

Confidence-supported label-free metabolic imaging with FPhaS phase autofluorescence microscopy.

bioRxiv : the preprint server for biology·2026

Same journal

Sequence-encoded autoinhibition couples mRNA decapping activity to phase separation.

bioRxiv : the preprint server for biology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 26, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Proximity as a Ground-Truth Proxy for Training Texture Discrimination and Segmentation.

Wilson S Geisler¹

¹University of Texas at Austin.

Biorxiv : the Preprint Server for Biology

|May 25, 2026

Summary

This summary is machine-generated.

Spatial proximity can train accurate texture discrimination in perceptual systems. This method, using natural images, improves scene segmentation by leveraging how features change with distance, mimicking natural selection.

Related Experiment Videos

Last Updated: May 26, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Area of Science:

Computational neuroscience
Computer vision
Perception psychology

Background:

Perceptual systems segment scenes into meaningful regions.
Accurate texture categorization is crucial for scene segmentation.
Low-level mechanisms identify same/different texture patches.

Purpose of the Study:

To investigate spatial proximity as a proxy for texture discrimination.
To train decision variables and bounds directly from natural images.
To improve scene segmentation using a proximity-based approach.

Main Methods:

Utilized spatial proximity as ground truth for training.
Applied decision variables and bounds to natural images without feedback.
Integrated trained variables into a hierarchical Bayesian observer (HBO) model.

Main Results:

Spatial proximity effectively trained accurate decision variables and bounds.
Performance improved by using proximity for final decision adjustments.
The HBO model achieved excellent image segmentation with arbitrary textures.

Conclusions:

Proximity discrimination and texture discrimination share mathematically identical decision bounds under certain conditions.
The proximity proxy is a plausible mechanism for natural selection in perceptual tasks.
This approach offers a simple yet effective method for texture discrimination and scene segmentation.