PublicaHier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.
Going into depth: Evaluating 2D and 3D cues for object classification on a new, large-scale object dataset
urn:nbn:de:0011-n-1959407 (1.1 MByte PDF)
MD5 Fingerprint: 10e9162ad57f625e2f8039fc4caaff1c
© 2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Created on: 6.3.2012
|Institute of Electrical and Electronics Engineers -IEEE-:|
IEEE International Conference on Computer Vision, ICCV Workshops 2011 : 6-13 November 2011, Barcelona, Spain
Piscataway/NJ: IEEE, 2011
|International Conference on Computer Vision (ICCV) <13, 2011, Barcelona>|
Workshop on Consumer Depth Cameras for Computer Vision (CDC4CV) <1, 2011, Barcelona>
| Conference Paper, Electronic Publication|
|Fraunhofer IPA ()|
| object classification; object database; service robot; Serviceroboter; shape detection; Haushaltsroboter; Objekterkennung; Roboter; Sensor; Datenbank|
Categorization of objects solely based on shape and appearance is still a largely unresolved issue. With the advent of new sensor technologies, such as consumer-level range sensors, new possibilities for shape processing have become available for a range of new application domains. In the first part of this paper, we introduce a novel, large dataset containing 18 categories of objects found in typical household and office environments - we envision this dataset to be useful in many applications ranging from robotics to computer vision. The second part of the paper presents computational experiments on object categorization with classifiers exploiting both two-dimensional and three-dimensional information. We evaluate categorization performance for both modalities in separate and combined representations and demonstrate the advantages of using range data for object and shape processing skills.