Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Modeling and Similitude01:12

Modeling and Similitude

Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
Gradient Vectors and Their Applications01:19

Gradient Vectors and Their Applications

Every point on a topographical map corresponds to a particular elevation, so the landscape can be modeled as a surface whose height depends on horizontal position. From any given location, a hiker may face infinitely many directions, but only one direction produces the fastest possible increase in elevation. This unique route is called the direction of steepest ascent, and in multivariable calculus, it is represented by the gradient vector of the elevation function.The gradient vector points...

You might also read

Related Articles

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

Sort by
Same author

Influence of artificial intelligence on ophthalmologists' judgments in glaucoma.

PloS one·2025
Same author

Identification of a Person in a Trajectory Based on Wearable Sensor Data Analysis.

Sensors (Basel, Switzerland)·2024
Same author

Non-Contact Breathing Monitoring Using Sleep Breathing Detection Algorithm (SBDA) Based on UWB Radar Sensors.

Sensors (Basel, Switzerland)·2022
Same author

Loss of intercellular bridges in the depth of invasion measurement area is a novel negative prognostic factor for oral squamous cell carcinoma: A retrospective study.

Oral surgery, oral medicine, oral pathology and oral radiology·2022
Same author

LASOR: Learning Accurate 3D Human Pose and Shape via Synthetic Occlusion-Aware Data and Neural Mesh Rendering.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2022
Same author

Noise-Reducing Fabric Electrode for ECG Measurement.

Sensors (Basel, Switzerland)·2021

Related Experiment Video

Updated: Jul 8, 2026

A Simple and Scalable Fabrication Method for Organic Electronic Devices on Textiles
06:21

A Simple and Scalable Fabrication Method for Organic Electronic Devices on Textiles

Published on: March 13, 2017

Integrating Domain Knowledge into Image Generation Models for Textile Design.

Yu Otani, Karol Nowakowski, Masahiro Toyoura

    IEEE Computer Graphics and Applications
    |July 6, 2026
    PubMed
    Summary

    This study introduces a prompt augmentation method for textile design image generation. Integrating domain knowledge improves generated image quality compared to standard or large language model-only prompts.

    Related Experiment Videos

    Last Updated: Jul 8, 2026

    A Simple and Scalable Fabrication Method for Organic Electronic Devices on Textiles
    06:21

    A Simple and Scalable Fabrication Method for Organic Electronic Devices on Textiles

    Published on: March 13, 2017

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Textile Design

    Background:

    • Text-to-image generative models offer new possibilities for textile design.
    • Prompt engineering is crucial for high-quality image generation but remains a challenge for designers.
    • Existing methods lack domain-specific knowledge integration for specialized fields like textile design.

    Purpose of the Study:

    • To develop a novel prompt augmentation method for textile design image generation.
    • To integrate textile-specific domain knowledge into prompts for improved synthesis.
    • To enhance the quality and relevance of AI-generated textile designs.

    Main Methods:

    • Proposing a prompt augmentation technique incorporating textile patterns, historical context, and design constraints.
    • Utilizing large language models (LLMs) to expand prompts with domain-specific information.
    • Conducting qualitative and quantitative evaluations to assess performance.

    Main Results:

    • Prompts augmented with textile domain knowledge significantly improve image generation quality.
    • The proposed method outperforms simple prompts and prompts expanded solely by LLM parametric knowledge.
    • Evaluations demonstrate enhanced suitability for textile design image synthesis.

    Conclusions:

    • The developed prompt augmentation method effectively enhances textile design image generation.
    • Integrating domain-specific knowledge is critical for specialized AI applications.
    • This approach offers a practical solution for designers using text-to-image models.