Divided we fall: Resolving occlusions using causal reasoning

Paul R. Cooper, Lawrence A Birnbaum, Daniel Halabe, Matthew Brand, Peter N. Prokopowicz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


An image of a scene with occlusions can yield only partial knowledge about disconnected fragments of the scene. If this were the only knowledge available, programs attempting to interpret the scene would have to conclude that the scene fragments would collapse in a jumble. But they won't. We describe a program that exploits commonsense knowledge of naive physics to make sense of scenes with occlusion. Our causal analysis focuses on the static stability of structures: what supports what. Occluded connections in a link-and-junction scene are inferred by determining the stability of each subassembly in the scene, and connecting parts when they are unstable. The causal explanation that is generated reflects a deeper understanding of the scene than mere model matching; it allows the seeing agent to predict what will happen next in the scene, and determine how to interact with it.

Original languageEnglish (US)
Title of host publicationComputer Vision — ECCV 1994 - 3rd European Conference on Computer Vision, Proceedings
EditorsJan-Olof Eklundh
PublisherSpringer Verlag
Number of pages6
ISBN (Print)9783540579564
StatePublished - 1994
Event3rd European Conference on Computer Vision, ECCV 1994 - Stockholm, Sweden
Duration: May 2 1994May 6 1994

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume800 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other3rd European Conference on Computer Vision, ECCV 1994

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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