DocumentCode
2028600
Title
Learning spatial relations between objects from 3D scenes
Author
Fichtl, Severin ; Alexander, James ; Guerin, Francois ; Mustafa, W. ; Kraft, Daniel ; Kruger, Norbert
Author_Institution
Comput. Sci., Univ. of Aberdeen, Aberdeen, UK
fYear
2013
fDate
18-22 Aug. 2013
Firstpage
1
Lastpage
2
Abstract
In this work, we learn a limited number of abstractions which can then be used to form preconditions for motor actions. These abstractions take the form of spatial relations amongst objects. We consider three “classes” of spatial relation: The objects either are separated from, on-top of, or inside each other. We have tackled this same problem in previous work (Fichtl et al., 2013). Here we report on recent improved results using a novel application of histograms to visually recognise a spatial relation between objects in the environment. Using this histogram based approach we are able to report a very high rate of success when the system is asked to recognise a spatial relation.
Keywords
learning (artificial intelligence); robot vision; 3D scenes; histogram based approach; histograms; motor actions; object spatial relation; spatial relation learning; spatial relations; visual recognition; Conferences; Histograms; Image color analysis; Machine vision; Robot sensing systems; Three-dimensional displays;
fLanguage
English
Publisher
ieee
Conference_Titel
Development and Learning and Epigenetic Robotics (ICDL), 2013 IEEE Third Joint International Conference on
Conference_Location
Osaka
Type
conf
DOI
10.1109/DevLrn.2013.6652552
Filename
6652552
Link To Document