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

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.

Visual System

Visual System

Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...

Vision

Vision

Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.

Gestalt Principles of Perception

Gestalt Principles of Perception

Gestalt principles provide a framework for understanding how humans perceive objects as unified wholes within their context. These principles are essential in explaining the cognitive processes that make sense of complex visual stimuli by organizing them into coherent groups. One fundamental principle is proximity, which posits that objects located close to each other are perceived as a collective group. For instance, when dots are positioned near one another, the visual system interprets them...

You might also read

Related Articles

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

Sort by

Same author

Boosting the immune response and protective efficacy of inactivated PRV vaccine using cGAMP as a mucosal adjuvant.

BMC veterinary research·2026

Same author

Mapping neutralizing epitopes and developing protective chimeric antibodies against porcine epidemic diarrhea virus infection.

International journal of biological macromolecules·2026

Same author

Multimodal deep-learning optimization of chiroptical properties in all-inorganic perovskite-coated TiO<sub>2</sub> nanohelices and inverse-design transfer to organic chiral luminophores.

Nature communications·2026

Same author

Interpretable machine learning model for predicting operative difficulty in robotic total mesorectal excision for mid-low rectal cancer.

Journal of robotic surgery·2026

Same author

Frequency-scanning nonlinearity suppression for FSI ranging based on a phenomenological modeling approach.

Optics express·2026

Same author

Virtual-interferometer approach to frequency-scanning nonlinearity calibration via harmonic extraction from a Fabry-Pérot etalon.

Optics letters·2026

Search research articles

Related Experiment Video

Updated: Oct 6, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Exploring Local Detail Perception for Scene Sketch Semantic Segmentation.

Ce Ge, Haifeng Sun, Yi-Zhe Song

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|January 19, 2022

Summary

This summary is machine-generated.

This study enhances deep learning for scene sketch semantic segmentation by introducing new datasets and a novel method focusing on local stroke details. This improves fine-grained perception and segmentation accuracy for abstract drawings.

More Related Videos

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

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

Related Experiment Videos

Last Updated: Oct 6, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

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:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Scene sketch semantic segmentation is crucial for human-computer interaction but lacks large-scale datasets.
Existing datasets like SketchyScene lack abstractness and diversity, hindering research.
Deep models struggle with the abstract and diverse nature of hand-drawn scene sketches.

Purpose of the Study:

To advance fine-grained perception in deep models for scene sketch semantic segmentation.
To address the lack of large-scale, diverse datasets for this task.
To improve the accuracy and interpretability of sketch segmentation.

Main Methods:

Developed two new large-scale datasets from real hand-drawn object sketches.
Introduced an automatic scene sketch synthesis process for dataset creation.
Proposed a method to enhance local detail perception in deep models using stroke features and masks.

Main Results:

Achieved state-of-the-art performance on three large-scale scene sketch datasets across four metrics.
Demonstrated improved detail discrimination and segmentation accuracy.
Provided meaningful interpretability through visual analytics.

Conclusions:

The proposed method effectively enhances deep model perception for scene sketch semantic segmentation.
The new datasets and synthesis process facilitate further research in this area.
Integrating low-level stroke features and masks significantly improves segmentation of abstract drawings.