Image Interpretation Confusion Resolution by Collaboration


  • Ben Mathew Florida Institute of Technology
  • Akram Alghanmi Florida Institute of Technology
  • Marius Silaghi Florida Institute of Technology



Vision, Collaboration


Visual scene understanding can benefit from inputs provided by multiple participants with their different perspectives, and a distributed version of a modified Waltz filtering enriched with modern AI inferences can potentially help accuracy and speed trade-offs by exploiting the simultaneous perspectives and logic. Speed is improved by the contribution of the implicit parallelisation in processing. Accuracy improvements are expected from updating constraints with novel and more powerful inferences that the participants can apply. Automatically understanding scenes is a highly relevant problem. Multiple robots communicate with one another to classify shapes of edges of an object. Local reasoning can reduce communication latency.




How to Cite

Mathew, B., Alghanmi, A., & Silaghi, M. (2024). Image Interpretation Confusion Resolution by Collaboration. The International FLAIRS Conference Proceedings, 37(1).