Recognizing scenes with hierarchical Implicit Shape Models based on spatial object relations for Programming by Demonstration

Pascal Meissner*, Reno Reckling, Rainer Jäkel, Sven R. Schmidt-Rohr, Rüdiger Dillmann

*Corresponding author for this work

Research output: Contribution to conferencePaper

5 Citations (Scopus)

Abstract

We present an approach for recognizing scenes, consisting of spatial relations between objects, in unstructured indoor environments, which change over time. Object relations are represented by full six Degree-of-Freedom (DoF) coordinate transformations between objects. They are acquired from object poses that are visually perceived while people demonstrate actions that are typically performed in a given scene. We recognize scenes using an Implicit Shape Model (ISM) that is similar to the Generalized Hough Transform. We extend it to take orientations between objects into account. This includes a verification step that allows us to infer not only the existence of scenes, but also the objects they are composed of. ISMs are restricted to represent scenes as star topologies of relations, which insufficiently approximate object relations in complex dynamic settings. False positive detections may occur. Our solution are exchangeable heuristics for recognizing object relations that have to be represented explicitly in separate ISMs. Object relations are modeled by the ISMs themselves. We use hierarchical agglomerative clustering, employing the heuristics, to construct a tree of ISMs. Learning and recognition of scenes with a single ISM is naturally extended to multiple ISMs
Original languageEnglish
Pages1-6
Number of pages6
DOIs
Publication statusPublished - 29 Nov 2013
Event2013 16th International Conference on Advanced Robotics, ICAR 2013 - Montevideo, Uruguay
Duration: 25 Nov 201329 Nov 2013

Conference

Conference2013 16th International Conference on Advanced Robotics, ICAR 2013
CountryUruguay
CityMontevideo
Period25/11/1329/11/13

Keywords

  • automatic programming
  • object recognition
  • pattern clustering
  • hape recognition
  • topology;scene recognition
  • implicit shape models
  • ISM
  • spatial object relations
  • programming by demonstration
  • PbD
  • star topologies
  • Object recognition
  • hierarchical agglomerative clustering
  • heuristics
  • Trajectory
  • Keyboards
  • SHAPE
  • Mice
  • Estimation
  • Binary trees
  • Vectors

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