T-CGAN: A Transformer-Embedded, Category-Conditioned Generative Adversarial Network.
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| Title: | T-CGAN: A Transformer-Embedded, Category-Conditioned Generative Adversarial Network. |
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| Authors: | Yu, Hui1, Wang, Yigang1 yigang.wang@hdu.edu.cn |
| Source: | Journal of Imaging Science & Technology. Jan/Feb2026, Vol. 70 Issue 1, p1-9. 9p. |
| Subjects: | Generative adversarial networks, Texture analysis (Image processing), Virtual reality, Robotics, Probabilistic generative models, Signal processing, Artificial neural networks |
| Abstract: | The technology of generating tactile data from visual modalities holds significant importance in cutting-edge fields such as tactile rendering, virtual reality, and robotics. This technology effectively bypasses the cumbersome process of manual tactile data collection and overcomes the limitations inherent in physical contact, thereby opening new avenues for advancement in related fields. However, current methods suffer from notable drawbacks: they struggle to ensure consistent and reliable results when generating tactile data across different categories, which greatly restricts their practical applications. To address this challenging problem, the authors have developed the T-CGAN cross-modal generation framework. Based on the FrictGAN architecture, this framework innovatively introduces an image category conditional constraint mechanism and texture feature extraction combined with the L1 loss function to precisely regulate the generation process and ensure high-quality output. Specifically, the framework can generate spectrograms of friction coefficient signals from fabric texture images and then convert these spectrograms into one-dimensional friction coefficient signals using the Griffin--Lim algorithm. During the research, the authors employed root mean square error and mean absolute error metrics to quantitatively analyze the differences among generated spectrograms, reconstructed signals, and their corresponding ground truths and conducted a comprehensive comparison with existing methods. Extensive experimental results demonstrate that this method significantly outperforms existing techniques in terms of both accuracy and stability, providing a superior solution for the field of tactile data generation. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Imaging Science & Technology is the property of International Society for Imaging Science & Technology and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 192941573 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: T-CGAN: A Transformer-Embedded, Category-Conditioned Generative Adversarial Network. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yu%2C+Hui%22">Yu, Hui</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Wang%2C+Yigang%22">Wang, Yigang</searchLink><relatesTo>1</relatesTo><i> yigang.wang@hdu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Imaging+Science+%26+Technology%22">Journal of Imaging Science & Technology</searchLink>. Jan/Feb2026, Vol. 70 Issue 1, p1-9. 9p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Generative+adversarial+networks%22">Generative adversarial networks</searchLink><br /><searchLink fieldCode="DE" term="%22Texture+analysis+%28Image+processing%29%22">Texture analysis (Image processing)</searchLink><br /><searchLink fieldCode="DE" term="%22Virtual+reality%22">Virtual reality</searchLink><br /><searchLink fieldCode="DE" term="%22Robotics%22">Robotics</searchLink><br /><searchLink fieldCode="DE" term="%22Probabilistic+generative+models%22">Probabilistic generative models</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The technology of generating tactile data from visual modalities holds significant importance in cutting-edge fields such as tactile rendering, virtual reality, and robotics. This technology effectively bypasses the cumbersome process of manual tactile data collection and overcomes the limitations inherent in physical contact, thereby opening new avenues for advancement in related fields. However, current methods suffer from notable drawbacks: they struggle to ensure consistent and reliable results when generating tactile data across different categories, which greatly restricts their practical applications. To address this challenging problem, the authors have developed the T-CGAN cross-modal generation framework. Based on the FrictGAN architecture, this framework innovatively introduces an image category conditional constraint mechanism and texture feature extraction combined with the L1 loss function to precisely regulate the generation process and ensure high-quality output. Specifically, the framework can generate spectrograms of friction coefficient signals from fabric texture images and then convert these spectrograms into one-dimensional friction coefficient signals using the Griffin--Lim algorithm. During the research, the authors employed root mean square error and mean absolute error metrics to quantitatively analyze the differences among generated spectrograms, reconstructed signals, and their corresponding ground truths and conducted a comprehensive comparison with existing methods. Extensive experimental results demonstrate that this method significantly outperforms existing techniques in terms of both accuracy and stability, providing a superior solution for the field of tactile data generation. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Imaging Science & Technology is the property of International Society for Imaging Science & Technology and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.2352/J.ImagingSci.Technol.2026.70.1.010403 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 1 Subjects: – SubjectFull: Generative adversarial networks Type: general – SubjectFull: Texture analysis (Image processing) Type: general – SubjectFull: Virtual reality Type: general – SubjectFull: Robotics Type: general – SubjectFull: Probabilistic generative models Type: general – SubjectFull: Signal processing Type: general – SubjectFull: Artificial neural networks Type: general Titles: – TitleFull: T-CGAN: A Transformer-Embedded, Category-Conditioned Generative Adversarial Network. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yu, Hui – PersonEntity: Name: NameFull: Wang, Yigang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan/Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 10623701 Numbering: – Type: volume Value: 70 – Type: issue Value: 1 Titles: – TitleFull: Journal of Imaging Science & Technology Type: main |
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