Space debris is a rising problem in today's world. Because there is so much in space that is unknown, it is critical to eventually catalog every piece. Since there are many attributes and properties attached to space objects, it is preferable to use an ontological classification method. The information presented in the ontology can then be used to answer questions about space debris. A Bayesian network would accomplish that because of its quantitative nature. The similarities between ontologies and Bayesian networks, such as their architectures and their flexibility, make it possible to integrate an ontology into a Bayesian network. Image determination and object collision assessment were used as applications to check the viability of integ...
Abstract. Building a probabilistic network for a real-life domain of application is a hard and time-...
Bayesian networks are a very general and powerful tool that can be used for a large number of proble...
Probabilistic graphical models (PGMs) are powerful tools for representing and reasoning under uncert...
Thesis (Ph.D.)--University of Washington, 2015Bayesian networks (BNs) are compact, powerful represen...
Today, ontologies are the standard for representing knowledge about concepts and relations among con...
Abstract: The increase and diversification of information has created new user requirements. The pro...
International audienceProbabilistic Graphical Models (PGMs) are powerful tools for representing and ...
Abstract. A drawback of current computer vision techniques is that, in contrast to human perception ...
In the maritime domain, surveillance systems are used to track vessels in a certain area of interest...
Abstract. Building a probabilistic network for a real-life domain of ap-plication is a hard and time...
Video data is being collected at alarming rates and yet there exists no comprehensive forensic tools...
In this paper, we propose a method for learning ontologies used to model a domain in the field of in...
International audienceOntologies and probabilistic graphical models are considered within the most e...
Bayesian Belief Networks are a powerful tool for combining different knowledge sources with various ...
Bayesian Network based ontologies enable specification of partial relations between concepts as an a...
Abstract. Building a probabilistic network for a real-life domain of application is a hard and time-...
Bayesian networks are a very general and powerful tool that can be used for a large number of proble...
Probabilistic graphical models (PGMs) are powerful tools for representing and reasoning under uncert...
Thesis (Ph.D.)--University of Washington, 2015Bayesian networks (BNs) are compact, powerful represen...
Today, ontologies are the standard for representing knowledge about concepts and relations among con...
Abstract: The increase and diversification of information has created new user requirements. The pro...
International audienceProbabilistic Graphical Models (PGMs) are powerful tools for representing and ...
Abstract. A drawback of current computer vision techniques is that, in contrast to human perception ...
In the maritime domain, surveillance systems are used to track vessels in a certain area of interest...
Abstract. Building a probabilistic network for a real-life domain of ap-plication is a hard and time...
Video data is being collected at alarming rates and yet there exists no comprehensive forensic tools...
In this paper, we propose a method for learning ontologies used to model a domain in the field of in...
International audienceOntologies and probabilistic graphical models are considered within the most e...
Bayesian Belief Networks are a powerful tool for combining different knowledge sources with various ...
Bayesian Network based ontologies enable specification of partial relations between concepts as an a...
Abstract. Building a probabilistic network for a real-life domain of application is a hard and time-...
Bayesian networks are a very general and powerful tool that can be used for a large number of proble...
Probabilistic graphical models (PGMs) are powerful tools for representing and reasoning under uncert...