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

Parallel Processing

Parallel Processing

The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...

You might also read

Related Articles

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

Sort by

Same author

Association of Hemodynamic Disease Severity and Distribution With Risk of Future Acute Coronary Syndrome.

JACC. Cardiovascular imaging·2026

Same author

Acute coronary syndrome risk in coronary side branch versus main vessel lesions: Lumen, plaque, and hemodynamic insights from coronary CT angiography.

Journal of cardiovascular computed tomography·2026

Same author

Extracellular ATP-P2RY2 signaling drives intratumoral prostaglandin E2 accumulation and adaptive resistance to immunotherapy in solid tumors.

Immunity·2026

Same author

Reply - Letter to the editor.

Clinical nutrition ESPEN·2026

Same author

Clinical outcomes following fractional flow reserve-guided revascularization using nicorandil versus conventional hyperemic agents: insights from the J-PRIDE Registry.

Cardiovascular intervention and therapeutics·2026

Same author

Association of reduced Cu,Zn-superoxide dismutase with coronary microvascular dysfunction-An observational study of oxidative stress markers.

International journal of cardiology. Heart & vasculature·2026

Search research articles

Related Experiment Video

Updated: Nov 1, 2025

Video Movement Analysis Using Smartphones ViMAS: A Pilot Study

Video Movement Analysis Using Smartphones ViMAS: A Pilot Study

Published on: March 14, 2017

Image and video processing on mobile devices: a survey.

Chamin Morikawa¹, Michihiro Kobayashi¹, Masaki Satoh¹

¹Morpho Inc, Chiyoda-ku, Tokyo, 101-0065 Japan.

The Visual Computer

|June 28, 2021

Summary

This summary is machine-generated.

This survey explores mobile image processing and computer vision, detailing algorithm adaptations for device constraints. It serves as a resource for researchers applying these technologies in real-world mobile applications.

Keywords:

Computer vision Image processing Mobile devices

More Related Videos

Smartphone Fundus Photography

Smartphone Fundus Photography

Published on: July 6, 2017

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

Related Experiment Videos

Last Updated: Nov 1, 2025

Video Movement Analysis Using Smartphones ViMAS: A Pilot Study

Video Movement Analysis Using Smartphones ViMAS: A Pilot Study

Published on: March 14, 2017

Smartphone Fundus Photography

Smartphone Fundus Photography

Published on: July 6, 2017

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

Area of Science:

Computer Science
Image Processing
Computer Vision

Background:

Mobile devices offer diverse applications like digital image enhancement and augmented reality.
Generic algorithms face limitations due to mobile device constraints and external factors.

Purpose of the Study:

To survey mobile image processing and computer vision applications.
To highlight constraints specific to mobile devices.
To explain algorithm adaptations for accuracy and performance.

Main Methods:

Literature review of mobile image processing and computer vision.
Analysis of common constraints on mobile platforms.
Examination of algorithm modifications for mobile environments.

Main Results:

Identified key constraints affecting mobile image processing and computer vision.
Detailed how algorithms are adapted to overcome these limitations.
Provided examples of successful mobile applications.

Conclusions:

Customized algorithms are essential for effective mobile image processing and computer vision.
This survey provides a valuable resource for researchers in the field.
Future research can build upon these adaptations for advanced mobile applications.