An ontology of virtual humans

Gutierrez, Alfredo; García-Rojas, Alejandra; Thalmann, Daniel; Vexo, Frédéric; Moccozet, Laurent; Magnenat‐Thalmann, Nadia; Mortara, Michela; Spagnuolo, Michela

doi:10.1007/s00371-006-0093-4

Cited by 57 publications

(4 citation statements)

References 18 publications

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“…Currently the system integrates the above described two core ontologies (tools and shape ontology) plus three domain ontologies, namely the Shape Acquisition and Reconstruction Errore. L'origine riferimento non è stata trovata., the Virtual Human [10] and the CAD/CAE process integration [11]. Further ontologies can be easily plugged-in without any programming extension need.…”

Section: Fig 5: the Ontology And Metadata Repository General Architectmentioning

confidence: 99%

The VISIONAIR Infrastructure Capabilities to Support Research

Attene¹,

Giannini²,

Pitikakis³

et al. 2013

Computer-Aided Design and Applications

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The development of information technologies, the increasing complexity of the information to be handled and analyzed, along with the growing capacities in scientific and engineering simulations, call for the development of powerful visualization tools and methods. Unfortunately, for many research teams the investment in visualization facilities is not always justified with respect to the actual usage. To overcome researchers' difficulties in using sophisticated visualization and interaction equipment, the European Commission is supporting the integration and the access to advanced visualization facilities around Europe through the VISIONAIR project financed in the frame of the FP7 Capacities program. In the use of the VISIONAIR's physical facilities, researchers may also be supported by the Virtual Visualization Services for sharing and retrieving shapes and related processing software tools. This paper provides an overview on the possibilities offered to researchers by the VISIONAIR project with particular focus on of the Virtual Visualization Services infrastructure.

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Section: Fig 5: the Ontology And Metadata Repository General Architectmentioning

confidence: 99%

The VISIONAIR Infrastructure Capabilities to Support Research

Attene¹,

Giannini²,

Pitikakis³

et al. 2013

Computer-Aided Design and Applications

Self Cite

View full text Add to dashboard Cite

show abstract

“…Unfortunately, these ontologies have been developed in biomedical environments and define a complex conceptualization which is not useful to our needs. There are also other ontologies that represent the human body in a more simplified way [7]; however these ontologies are not designed to deal with different value spaces in a cognitive environment. A general pattern based on partwhole relationships is proposed to cover the semantic representation of data captured using ToF sensors.…”

Section: General Model For Ontology-based Human Skeleton Represenmentioning

confidence: 99%

Ontological representation of time-of-flight camera data to support vision-based AmI

Serrano

Gómez‐Romero

Patricio

et al. 2012

2012 IEEE International Conference on Pervasive Computing and Communications Workshops

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“…Unfortunately, these ontologies have been developed in biomedical environments and define a complex conceptualization which is not useful to our needs. There are also other ontologies that represent the human body in a more simplified way [ 35 ]; however these ontologies are not designed to deal with sensor data in a cognitive environment. A general pattern based on part-whole relationships is proposed to cover the semantic representation of data captured using light wave sensors.…”

Section: Part-based Symbolic Layer For Cognitive Vision Approachesmentioning

confidence: 99%

Ontological Representation of Light Wave Camera Data to Support Vision-Based AmI

Serrano

Gómez‐Romero

Patricio

et al. 2012

Sensors

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Recent advances in technologies for capturing video data have opened a vast amount of new application areas in visual sensor networks. Among them, the incorporation of light wave cameras on Ambient Intelligence (AmI) environments provides more accurate tracking capabilities for activity recognition. Although the performance of tracking algorithms has quickly improved, symbolic models used to represent the resulting knowledge have not yet been adapted to smart environments. This lack of representation does not allow to take advantage of the semantic quality of the information provided by new sensors. This paper advocates for the introduction of a part-based representational level in cognitive-based systems in order to accurately represent the novel sensors' knowledge. The paper also reviews the theoretical and practical issues in part-whole relationships proposing a specific taxonomy for computer vision approaches. General part-based patterns for human body and transitive part-based representation and inference are incorporated to an ontology-based previous framework to enhance scene interpretation in the area of video-based AmI. The advantages and new features of the model are demonstrated in a Social Signal Processing (SSP) application for the elaboration of live market researches.

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An ontology of virtual humans

Cited by 57 publications

References 18 publications

The VISIONAIR Infrastructure Capabilities to Support Research

The VISIONAIR Infrastructure Capabilities to Support Research

Ontological representation of time-of-flight camera data to support vision-based AmI

Ontological Representation of Light Wave Camera Data to Support Vision-Based AmI

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