AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
2024
Weng, S. C. -C.; Chiou, Y. -M.; Do, E. Y. -L.
Dream Mesh: A Speech-to-3D Model Generative Pipeline in Mixed Reality Proceedings Article
In: Proc. - IEEE Int. Conf. Artif. Intell. Ext. Virtual Real., AIxVR, pp. 345–349, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-835037202-1 (ISBN).
Abstract | Links | BibTeX | Tags: 3D content, 3D modeling, 3D models, 3d-modeling, Augmented Reality, Digital assets, Generative AI, generative artificial intelligence, Intelligence models, Mesh generation, Mixed reality, Modeling, Speech-to-3D, Text modeling, Three dimensional computer graphics, User interfaces
@inproceedings{weng_dream_2024,
title = {Dream Mesh: A Speech-to-3D Model Generative Pipeline in Mixed Reality},
author = {S. C. -C. Weng and Y. -M. Chiou and E. Y. -L. Do},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187218106&doi=10.1109%2fAIxVR59861.2024.00059&partnerID=40&md5=5bfe206e841f23de6458f88a0824bd4d},
doi = {10.1109/AIxVR59861.2024.00059},
isbn = {979-835037202-1 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Proc. - IEEE Int. Conf. Artif. Intell. Ext. Virtual Real., AIxVR},
pages = {345–349},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Generative Artificial Intelligence (AI) models have risen to prominence due to their unparalleled ability to craft and generate digital assets, encompassing text, images, audio, video, and 3D models. Leveraging the capabilities of diffusion models, such as Stable Diffusion and Instruct pix2pix, users can guide AI with specific prompts, streamlining the creative journey for graphic designers. However, the primary application of these models has been to graphic content within desktop interfaces, prompting professionals in interior and architectural design to seek more tailored solutions for their daily operations. To bridge this gap, Augmented Reality (AR) and Mixed Reality (MR) technologies offer a promising solution, transforming traditional 2D artworks into engaging 3D interactive realms. In this paper, we present "Dream Mesh,"a MR application MR tool that combines a Speech-to-3D generative workflow besed on DreamFusion model without relying on pre-existing 3D content libraries. This innovative system empowers users to express 3D content needs through natural language input, promising transformative potential in real-time 3D content creation and an enhanced MR user experience. © 2024 IEEE.},
keywords = {3D content, 3D modeling, 3D models, 3d-modeling, Augmented Reality, Digital assets, Generative AI, generative artificial intelligence, Intelligence models, Mesh generation, Mixed reality, Modeling, Speech-to-3D, Text modeling, Three dimensional computer graphics, User interfaces},
pubstate = {published},
tppubtype = {inproceedings}
}
Generative Artificial Intelligence (AI) models have risen to prominence due to their unparalleled ability to craft and generate digital assets, encompassing text, images, audio, video, and 3D models. Leveraging the capabilities of diffusion models, such as Stable Diffusion and Instruct pix2pix, users can guide AI with specific prompts, streamlining the creative journey for graphic designers. However, the primary application of these models has been to graphic content within desktop interfaces, prompting professionals in interior and architectural design to seek more tailored solutions for their daily operations. To bridge this gap, Augmented Reality (AR) and Mixed Reality (MR) technologies offer a promising solution, transforming traditional 2D artworks into engaging 3D interactive realms. In this paper, we present "Dream Mesh,"a MR application MR tool that combines a Speech-to-3D generative workflow besed on DreamFusion model without relying on pre-existing 3D content libraries. This innovative system empowers users to express 3D content needs through natural language input, promising transformative potential in real-time 3D content creation and an enhanced MR user experience. © 2024 IEEE.
2023
Vincent, B.; Ayyar, K.
Roblox Generative AI in action Proceedings Article
In: S.N., Spencer (Ed.): Proc. - SIGGRAPH Real-Time Live!, Association for Computing Machinery, Inc, 2023, ISBN: 979-840070158-0 (ISBN).
Abstract | Links | BibTeX | Tags: AI techniques, Complex model, Creation process, Education, Game, Games, Interactive computer graphics, Interactive objects, Lighting, Metaverse, Metaverses, Modeling, Modeling languages, Natural languages, Object and scenes, Pipeline, Real-Time Rendering, Rendering (computer graphics)
@inproceedings{vincent_roblox_2023,
title = {Roblox Generative AI in action},
author = {B. Vincent and K. Ayyar},
editor = {Spencer S.N.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85167946022&doi=10.1145%2f3588430.3597250&partnerID=40&md5=61fda81c33eb3623240f7d14f51607b0},
doi = {10.1145/3588430.3597250},
isbn = {979-840070158-0 (ISBN)},
year = {2023},
date = {2023-01-01},
booktitle = {Proc. - SIGGRAPH Real-Time Live!},
publisher = {Association for Computing Machinery, Inc},
abstract = {Roblox is investing in generative AI techniques to revolutionize the creation process on its platform. By leveraging natural language and other intuitive expressions of intent, creators can build interactive objects and scenes without complex modeling or coding. The use of AI image generation services and large language models aim to make creation faster and easier for every user on the platform. © 2023 Owner/Author.},
keywords = {AI techniques, Complex model, Creation process, Education, Game, Games, Interactive computer graphics, Interactive objects, Lighting, Metaverse, Metaverses, Modeling, Modeling languages, Natural languages, Object and scenes, Pipeline, Real-Time Rendering, Rendering (computer graphics)},
pubstate = {published},
tppubtype = {inproceedings}
}
Roblox is investing in generative AI techniques to revolutionize the creation process on its platform. By leveraging natural language and other intuitive expressions of intent, creators can build interactive objects and scenes without complex modeling or coding. The use of AI image generation services and large language models aim to make creation faster and easier for every user on the platform. © 2023 Owner/Author.
2018
Cascia, Marco La; Vassallo, Giorgio; Gallo, Luigi; Pilato, Giovanni; Vella, Filippo
Automatic Image Annotation Using Random Projection in a Conceptual Space Induced from Data Proceedings Article
In: 2018 14th International Conference on Signal-Image Technology Internet-Based Systems (SITIS), pp. 464–471, 2018.
Abstract | Links | BibTeX | Tags: Feature extraction, Hidden Markov models, Image annotation, Modeling, Semantics, Visualization
@inproceedings{lacasciaAutomaticImageAnnotation2018,
title = {Automatic Image Annotation Using Random Projection in a Conceptual Space Induced from Data},
author = { Marco La Cascia and Giorgio Vassallo and Luigi Gallo and Giovanni Pilato and Filippo Vella},
doi = {10.1109/SITIS.2018.00077},
year = {2018},
date = {2018-11-01},
booktitle = {2018 14th International Conference on Signal-Image Technology Internet-Based Systems (SITIS)},
pages = {464--471},
abstract = {The main drawback of a detailed representation of visual content, whatever is its origin, is that significant features are very high dimensional. To keep the problem tractable while preserving the semantic content, a dimensionality reduction of the data is needed. We propose the Random Projection techniques to reduce the dimensionality. Even though this technique is sub-optimal with respect to Singular Value Decomposition its much lower computational cost make it more suitable for this problem and in particular when computational resources are limited such as in mobile terminals. In this paper we present the use of a ``conceptual'' space, automatically induced from data, to perform automatic image annotation. Images are represented by visual features based on color and texture and arranged as histograms of visual terms and bigrams to partially preserve the spatial information [1]. Using a set of annotated images as training data, the matrix of visual features is built and dimensionality reduction is performed using the Random Projection algorithm. A new unannotated image is then projected into the dimensionally reduced space and the labels of the closest training images are assigned to the unannotated image itself. Experiments on large real collection of images showed that the approach, despite of its low computational cost, is very effective.},
keywords = {Feature extraction, Hidden Markov models, Image annotation, Modeling, Semantics, Visualization},
pubstate = {published},
tppubtype = {inproceedings}
}
The main drawback of a detailed representation of visual content, whatever is its origin, is that significant features are very high dimensional. To keep the problem tractable while preserving the semantic content, a dimensionality reduction of the data is needed. We propose the Random Projection techniques to reduce the dimensionality. Even though this technique is sub-optimal with respect to Singular Value Decomposition its much lower computational cost make it more suitable for this problem and in particular when computational resources are limited such as in mobile terminals. In this paper we present the use of a ``conceptual'' space, automatically induced from data, to perform automatic image annotation. Images are represented by visual features based on color and texture and arranged as histograms of visual terms and bigrams to partially preserve the spatial information [1]. Using a set of annotated images as training data, the matrix of visual features is built and dimensionality reduction is performed using the Random Projection algorithm. A new unannotated image is then projected into the dimensionally reduced space and the labels of the closest training images are assigned to the unannotated image itself. Experiments on large real collection of images showed that the approach, despite of its low computational cost, is very effective.
Cascia, Marco La; Vassallo, Giorgio; Gallo, Luigi; Pilato, Giovanni; Vella, Filippo
Automatic Image Annotation Using Random Projection in a Conceptual Space Induced from Data Proceedings Article
In: 2018 14th International Conference on Signal-Image Technology Internet-Based Systems (SITIS), pp. 464–471, 2018.
Abstract | Links | BibTeX | Tags: Feature extraction, Hidden Markov models, Image annotation, Modeling, Semantics, Visualization
@inproceedings{la_cascia_automatic_2018,
title = {Automatic Image Annotation Using Random Projection in a Conceptual Space Induced from Data},
author = {Marco La Cascia and Giorgio Vassallo and Luigi Gallo and Giovanni Pilato and Filippo Vella},
doi = {10.1109/SITIS.2018.00077},
year = {2018},
date = {2018-11-01},
booktitle = {2018 14th International Conference on Signal-Image Technology Internet-Based Systems (SITIS)},
pages = {464–471},
abstract = {The main drawback of a detailed representation of visual content, whatever is its origin, is that significant features are very high dimensional. To keep the problem tractable while preserving the semantic content, a dimensionality reduction of the data is needed. We propose the Random Projection techniques to reduce the dimensionality. Even though this technique is sub-optimal with respect to Singular Value Decomposition its much lower computational cost make it more suitable for this problem and in particular when computational resources are limited such as in mobile terminals. In this paper we present the use of a “conceptual” space, automatically induced from data, to perform automatic image annotation. Images are represented by visual features based on color and texture and arranged as histograms of visual terms and bigrams to partially preserve the spatial information [1]. Using a set of annotated images as training data, the matrix of visual features is built and dimensionality reduction is performed using the Random Projection algorithm. A new unannotated image is then projected into the dimensionally reduced space and the labels of the closest training images are assigned to the unannotated image itself. Experiments on large real collection of images showed that the approach, despite of its low computational cost, is very effective.},
keywords = {Feature extraction, Hidden Markov models, Image annotation, Modeling, Semantics, Visualization},
pubstate = {published},
tppubtype = {inproceedings}
}
The main drawback of a detailed representation of visual content, whatever is its origin, is that significant features are very high dimensional. To keep the problem tractable while preserving the semantic content, a dimensionality reduction of the data is needed. We propose the Random Projection techniques to reduce the dimensionality. Even though this technique is sub-optimal with respect to Singular Value Decomposition its much lower computational cost make it more suitable for this problem and in particular when computational resources are limited such as in mobile terminals. In this paper we present the use of a “conceptual” space, automatically induced from data, to perform automatic image annotation. Images are represented by visual features based on color and texture and arranged as histograms of visual terms and bigrams to partially preserve the spatial information [1]. Using a set of annotated images as training data, the matrix of visual features is built and dimensionality reduction is performed using the Random Projection algorithm. A new unannotated image is then projected into the dimensionally reduced space and the labels of the closest training images are assigned to the unannotated image itself. Experiments on large real collection of images showed that the approach, despite of its low computational cost, is very effective.
2011
Blandford, Ann; Pietro, Giuseppe De; Gallo, Luigi; Gimblett, Andy; Oladimeji, Patrick; Thimbleby, Harold
Engineering Interactive Computer Systems for Medicine and Healthcare (EICS4Med) Proceedings Article
In: EICS '11 Proceedings of the 3rd ACM SIGCHI Symposium on Engineering Interactive Computing Systems, pp. 341–342, ACM, Pisa, Italy, 2011, ISBN: 978-1-4503-0670-6.
Abstract | Links | BibTeX | Tags: Formal methods, Healthcare, Human computer interaction, Medical devices, Mobile computing, Modeling, Natural User Interfaces, Safety
@inproceedings{blandfordEngineeringInteractiveComputer2011,
title = {Engineering Interactive Computer Systems for Medicine and Healthcare (EICS4Med)},
author = { Ann Blandford and Giuseppe De Pietro and Luigi Gallo and Andy Gimblett and Patrick Oladimeji and Harold Thimbleby},
doi = {http://doi.acm.org/10.1145/1996461.1996556},
isbn = {978-1-4503-0670-6},
year = {2011},
date = {2011-06-01},
booktitle = {EICS '11 Proceedings of the 3rd ACM SIGCHI Symposium on Engineering Interactive Computing Systems},
pages = {341--342},
publisher = {ACM},
address = {Pisa, Italy},
abstract = {This workshop brings together and develops the community of researchers and practitioners concerned with the design and evaluation of interactive medical devices (infusion pumps, etc) and systems (electronic patient records, etc), to deliver a roadmap for future research in this area. The workshop involves researchers and practitioners designing and evaluating dependable systems in a variety of contexts, and those developing innovative interactive computer systems for healthcare. These pose particular challenges because of the inherent variability - of patients, system configurations, and so on. Participants will represent a range of perspectives, including safety engineering and innovative design.},
keywords = {Formal methods, Healthcare, Human computer interaction, Medical devices, Mobile computing, Modeling, Natural User Interfaces, Safety},
pubstate = {published},
tppubtype = {inproceedings}
}
This workshop brings together and develops the community of researchers and practitioners concerned with the design and evaluation of interactive medical devices (infusion pumps, etc) and systems (electronic patient records, etc), to deliver a roadmap for future research in this area. The workshop involves researchers and practitioners designing and evaluating dependable systems in a variety of contexts, and those developing innovative interactive computer systems for healthcare. These pose particular challenges because of the inherent variability - of patients, system configurations, and so on. Participants will represent a range of perspectives, including safety engineering and innovative design.
Blandford, Ann; Pietro, Giuseppe De; Gallo, Luigi; Gimblett, Andy; Oladimeji, Patrick; Thimbleby, Harold
Engineering interactive computer systems for medicine and healthcare (EICS4Med) Proceedings Article
In: EICS '11 Proceedings of the 3rd ACM SIGCHI symposium on Engineering interactive computing systems, pp. 341–342, ACM, Pisa, Italy, 2011, ISBN: 978-1-4503-0670-6.
Abstract | Links | BibTeX | Tags: Formal methods, Healthcare, Human computer interaction, Medical devices, Mobile computing, Modeling, Natural User Interfaces, Safety
@inproceedings{blandford_engineering_2011,
title = {Engineering interactive computer systems for medicine and healthcare (EICS4Med)},
author = {Ann Blandford and Giuseppe De Pietro and Luigi Gallo and Andy Gimblett and Patrick Oladimeji and Harold Thimbleby},
doi = {http://doi.acm.org/10.1145/1996461.1996556},
isbn = {978-1-4503-0670-6},
year = {2011},
date = {2011-06-01},
booktitle = {EICS '11 Proceedings of the 3rd ACM SIGCHI symposium on Engineering interactive computing systems},
pages = {341–342},
publisher = {ACM},
address = {Pisa, Italy},
abstract = {This workshop brings together and develops the community of researchers and practitioners concerned with the design and evaluation of interactive medical devices (infusion pumps, etc) and systems (electronic patient records, etc), to deliver a roadmap for future research in this area. The workshop involves researchers and practitioners designing and evaluating dependable systems in a variety of contexts, and those developing innovative interactive computer systems for healthcare. These pose particular challenges because of the inherent variability - of patients, system configurations, and so on. Participants will represent a range of perspectives, including safety engineering and innovative design.},
keywords = {Formal methods, Healthcare, Human computer interaction, Medical devices, Mobile computing, Modeling, Natural User Interfaces, Safety},
pubstate = {published},
tppubtype = {inproceedings}
}
This workshop brings together and develops the community of researchers and practitioners concerned with the design and evaluation of interactive medical devices (infusion pumps, etc) and systems (electronic patient records, etc), to deliver a roadmap for future research in this area. The workshop involves researchers and practitioners designing and evaluating dependable systems in a variety of contexts, and those developing innovative interactive computer systems for healthcare. These pose particular challenges because of the inherent variability - of patients, system configurations, and so on. Participants will represent a range of perspectives, including safety engineering and innovative design.