Interest-point-based landmark computation for agents’ spatial description coordination

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)


In applications involving multiple conversational agents, each of these agents has its own view of a visual scene, and thus all the agents must establish common visual landmarks in order to coordinate their space understanding and to coherently share generated spatial descriptions of this scene. Whereas natural language processing approaches contribute to define the common ground through dialogues between these agents, we propose in this paper a computer-vision system to determine the object of reference for both agents efficiently and automatically. Our approach consists in processing each agent's view by computing the related, visual interest points, and then by matching them in order to extract the salient and meaningful landmark. Our approach has been successfully tested on real-world data, and its performance and design allow its use for embedded robotic system communication.
Original languageEnglish
Title of host publicationProceedings of the 8th International Conference on Agents and Artificial Intelligence
Subtitle of host publicationICAART
Number of pages4
ISBN (Print)9789897581724
Publication statusPublished - 26 Feb 2016
Externally publishedYes
EventInternational Conference on Agents and Artificial Intelligence - Rome, Italy
Duration: 24 Feb 201626 Feb 2016
Conference number: 8


ConferenceInternational Conference on Agents and Artificial Intelligence
Abbreviated titleICAART 2016
Internet address


  • Qualitative Spatial Reasoning
  • Object Detention
  • Local Feature Descriptors
  • Feature Extraction
  • Visual Scene Understanding
  • Automated Image Annotation
  • Robotics
  • Autonomic Agents


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