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Using Object Affordances to Improve Object Recognition

Castellini, Claudio and Tommasi, Tatiana and Noceti, Nicoletta and Odone, Francesca and Caputo, Barbara (2011) Using Object Affordances to Improve Object Recognition. IEEE Transactions on Autonomous Mental Development, 3 (3), pp. 207-215. IEEE - Institute of Electrical and Electronics Engineers. DOI: 10.1109/TAMD.2011.2106782 ISSN 1943-0604

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Official URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5699912

Abstract

The problem of object recognition has not yet been solved in its general form. The most successful approach to it so far relies on object models obtained by training a statistical method on visual features obtained from camera images. The images must necessarily come from huge visual datasets, in order to circumvent all problems related to changing illumination, point of view, etc. We hereby propose to also consider, in an object model, a simple model of how a human being would grasp that object (its affordance). This knowledge is represented as a function mapping visual features of an object to the kinematic features of a hand while grasping it. The function is practically enforced via regression on a human grasping database. After describing the database (which is publicly available) and the proposed method, we experimentally evaluate it, showing that a standard object classifier working on both sets of features (visual and motor) has a significantly better recognition rate than that of a visual-only classifier.

Item URL in elib:https://elib.dlr.de/84982/
Document Type:Article
Title:Using Object Affordances to Improve Object Recognition
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Castellini, ClaudioDLR, GermanyUNSPECIFIED
Tommasi, TatianaIDIAP, SwitzerlandUNSPECIFIED
Noceti, NicolettaDISI, ItalyUNSPECIFIED
Odone, FrancescaDISI, ItalyUNSPECIFIED
Caputo, BarbaraIDIAP, SwitzerlandUNSPECIFIED
Date:2011
Journal or Publication Title:IEEE Transactions on Autonomous Mental Development
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:No
Volume:3
DOI :10.1109/TAMD.2011.2106782
Page Range:pp. 207-215
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:1943-0604
Status:Published
Keywords:computer vision, machine learning, grasping, affordances
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Technik für Raumfahrtsysteme
DLR - Research theme (Project):R - Vorhaben Multisensorielle Weltmodellierung
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (until 2012)
Deposited By: Castellini, Dr. Claudio
Deposited On:16 Dec 2013 18:05
Last Modified:31 Jul 2019 19:42

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