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

Classification of Signals

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...

Detection of Black Holes

Detection of Black Holes

Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...

Neural Circuits

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

You might also read

Related Articles

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

Sort by

Same author

Crossover phenomena of percolation transition in evolution networks with hybrid attachment.

Chaos (Woodbury, N.Y.)·2016

Same author

Relation Between C-X-C Motif Chemokine Receptor 4 Levels and the Presence and Extent of Angiographic Coronary Collaterals in Patients With Chronic Total Coronary Occlusion.

The American journal of cardiology·2016

Same author

Quantify patient-specific coronary material property and its impact on stress/strain calculations using in vivo IVUS data and 3D FSI models: a pilot study.

Biomechanics and modeling in mechanobiology·2016

Same author

Analysis of Genomewide DNA Methylation Reveals Differences in DNA Methylation Levels between Dormant and Naturally as well as Artificially Potentiated Pedicle Periosteum of Sika Deer (Cervus nippon).

Journal of experimental zoology. Part B, Molecular and developmental evolution·2016

Same author

Value of corneal epithelial and Bowman's layer vertical thickness profiles generated by UHR-OCT for sub-clinical keratoconus diagnosis.

Scientific reports·2016

Same author

MAPK-Mediated YAP Activation Controls Mechanical-Tension-Induced Pulmonary Alveolar Regeneration.

Cell reports·2016

Same journal

Intervention Feasible Region and Driver Risk Capacity Aware Human-Machine Collaborative Safe Trajectory Planning.

IEEE transactions on neural networks and learning systems·2026

Same journal

A Unified Differential Denoising Learning Framework With a Pre-Trained Model and Fuzzy Graph Networks for Drug-Drug Interaction Prediction.

IEEE transactions on neural networks and learning systems·2026

Same journal

Self-Supervised Continuous Dynamic Graph Representation Learning via Hawkes Processes.

IEEE transactions on neural networks and learning systems·2026

Same journal

cPU: Consistent Risk Estimator for Positive-Unlabeled Learning.

IEEE transactions on neural networks and learning systems·2026

Same journal

Tuning-Free Latent Diffusion Models for Ultrahigh-Resolution Image Editing.

IEEE transactions on neural networks and learning systems·2026

Same journal

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

IEEE transactions on neural networks and learning systems·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 30, 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

Kernel Proposal Network for Arbitrary Shape Text Detection.

Shi-Xue Zhang, Xiaobin Zhu, Jie-Bo Hou

IEEE Transactions on Neural Networks and Learning Systems

|March 10, 2022

Summary

This summary is machine-generated.

This study introduces a novel Kernel Proposal Network (KPN) to effectively separate adjacent text instances in arbitrary shape text detection. The KPN utilizes dynamic convolution kernels and an orthogonal learning loss to improve accuracy and robustness in complex scene images.

More Related Videos

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Related Experiment Videos

Last Updated: Sep 30, 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

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Area of Science:

Computer Vision
Machine Learning
Image Processing

Background:

Segmentation-based methods excel at arbitrary shape text detection.
Separating adjacent text instances remains a significant challenge due to image complexity.

Purpose of the Study:

To propose an innovative Kernel Proposal Network (KPN) for arbitrary shape text detection.
To effectively address the challenge of separating neighboring text instances.

Main Methods:

KPN predicts Gaussian center maps to extract dynamic convolution kernels from embedding features.
An orthogonal learning loss (OLL) enforces independence between kernel proposals.
Individual kernel convolutions generate distinct feature maps for each text instance.

Main Results:

KPN effectively separates neighboring text instances, improving robustness against unclear boundaries.
The method demonstrates impressive performance and efficiency on challenging datasets.
This work is the first to apply dynamic convolution kernels to the text detection adhesion problem.

Conclusions:

The proposed KPN offers an effective solution for arbitrary shape text detection, particularly in separating adjacent instances.
The dynamic convolution kernel strategy and OLL contribute to enhanced accuracy and robustness.
The KPN method shows significant potential for real-world applications requiring precise text detection.