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

Streamlines, Streaklines, and Pathlines

Streamlines, Streaklines, and Pathlines

A streamline represents the trajectory that is always tangent to the fluid's velocity vector at any given point. The velocity of a fluid particle is always directed along the streamline, ensuring the particle continuously follows the streamline's path. Streamlines are particularly useful for visualizing the overall direction of flow in a fluid system, and they provide an instantaneous representation of the flow's velocity field. In steady flow, where conditions do not change over...

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.

Motor Unit Stimulation

Motor Unit Stimulation

When the neuron of a motor unit fires an action potential, it triggers a series of events, leading to a twitch contraction in the muscle fibers. The process of excitation-contraction coupling is crucial in relaying the action potential to the muscle fibers.
The latent period of contraction marks the onset of excitation-contraction coupling, when the action potential propagates across the sarcolemma, preparing the muscle fibers for contraction. As the fibers enter the contraction phase, the...

You might also read

Related Articles

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

Sort by

Same journal

Latent Space Projections and Atlases, a Cautionary Tale in Deep Neuroimaging using Autoencoders.

International journal of neural systems·2026

Same journal

Transformer-Based Anomaly Detection for Neurodegenerative Screening in MRI Images.

International journal of neural systems·2026

Same journal

Discrete Wavelet Convolution for Learnable Time-Frequency Representation with Application to Seizure Prediction.

International journal of neural systems·2026

Same journal

Automatic Seizure Detection using Hierarchical Spectral-Temporal Feature Learning with an Imbalance-Aware Transformer.

International journal of neural systems·2026

Same journal

Pyramid Vision Transformer-Enhanced Conformer Network for Epileptic Seizure Recognition Using MultiChannel EEG Signals.

International journal of neural systems·2026

Same journal

A Time-Frequency Decoupled Contrastive Learning Framework for Electroencephalography-Based Parkinson's Disease Diagnosis.

International journal of neural systems·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 16, 2025

Digital Handwriting Analysis of Characters in Chinese Patients with Mild Cognitive Impairment

Digital Handwriting Analysis of Characters in Chinese Patients with Mild Cognitive Impairment

Published on: March 11, 2021

Scene Text Detection Based on Text Stroke Components.

Xinyue Hou¹, Pengsen Cheng¹, Hongyu Gao¹

¹School of Cyber Science and Engineering, Sichuan University, Chengdu 610207, Sichuan, P. R. China.

International Journal of Neural Systems

|April 2, 2025

Summary

This summary is machine-generated.

This study introduces a new scene text detection method using Text Stroke Components (TSC) and Harris corner detection. The approach effectively locates text characters, outperforming current methods in diverse conditions.

Keywords:

Text stroke components corner detection scene text detection

More Related Videos

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

Published on: February 23, 2024

Dual-Task Stroop Paradigm for Detecting Cognitive Deficits in High-Functioning Stroke Patients

Dual-Task Stroop Paradigm for Detecting Cognitive Deficits in High-Functioning Stroke Patients

Published on: December 16, 2022

Related Experiment Videos

Last Updated: May 16, 2025

Digital Handwriting Analysis of Characters in Chinese Patients with Mild Cognitive Impairment

Digital Handwriting Analysis of Characters in Chinese Patients with Mild Cognitive Impairment

Published on: March 11, 2021

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

Published on: February 23, 2024

Dual-Task Stroop Paradigm for Detecting Cognitive Deficits in High-Functioning Stroke Patients

Dual-Task Stroop Paradigm for Detecting Cognitive Deficits in High-Functioning Stroke Patients

Published on: December 16, 2022

Area of Science:

Computer Vision
Artificial Intelligence

Background:

Scene text detection is crucial but challenged by variations in text size, background complexity, and orientation.
Existing methods struggle with these real-world scene text complexities.

Purpose of the Study:

To propose a novel methodology for robust scene text detection and recognition.
To address limitations of previous methods in handling diverse text appearances and backgrounds.

Main Methods:

Leveraging Text Stroke Components (TSC) and Harris corner detection to identify critical points in text strokes.
Utilizing a transparency parameter to enhance the fusion of original and corner-detected images, improving key point localization and reducing noise.
Jointly training the detection method with the ABINet recognition model.

Main Results:

The proposed TSC-based method demonstrates superior performance in localizing text characters compared to existing scene text detectors.
Extensive experiments on 13 datasets show significant outperformance against state-of-the-art (SOTA) methods.
The transparency parameter effectively improves key stroke point localization and minimizes background interference.

Conclusions:

Text Stroke Components combined with corner detection offer an effective strategy for key-point localization in scene text detection.
The novel methodology provides a significant advancement in accurately detecting and recognizing scene text under challenging conditions.