Authors:
Viktor Seib
;
Malte Knauf
and
Dietrich Paulus
Affiliation:
University of Koblenz-Landau, Germany
Keyword(s):
Affordances, Fine-grained Affordances, Visual Affordance Detection, Object Classification, Fuzzy Sets.
Related
Ontology
Subjects/Areas/Topics:
Active and Robot Vision
;
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Robotics
;
Software Engineering
Abstract:
Recently, object affordances have moved into the focus of researchers in computer vision. Affordances describe
how an object can be used by a specific agent. This additional information on the purpose of an object is
used to augment the classification process. With the herein proposed approach we aim at bringing affordances
and object classification closer together by proposing fine-grained affordances. We present an algorithm that
detects fine-grained sitting affordances in point clouds by iteratively transforming a human model into the
scene. This approach enables us to distinguish object functionality on a finer-grained scale, thus more closely
resembling the different purposes of similar objects. For instance, traditional methods suggest that a stool,
chair and armchair all afford sitting. This is also true for our approach, but additionally we distinguish sitting
without backrest, with backrest and with armrests. This fine-grained affordance definition closely resembles
individu
al types of sitting and better reflects the purposes of different chairs. We experimentally evaluate our
approach and provide fine-grained affordance annotations in a dataset from our lab.
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