NDLI: Multiple-Cue Object Recognition on outside datasets

Content Provider	IEEE Xplore Digital Library
Author	Aboutalib, S. Veloso, M.
Copyright Year	2010
Description	Author affiliation: Computer Science Department, School of Computer Science, Carnegie Mellon University, PA 15213, USA (Aboutalib, S.; Veloso, M.)
Abstract	This work builds upon the fact that robots can observe humans interacting with the objects in their environment, and that humans provide numerous non-visual cues to the identity of objects. In previous work, we outlined a Multiple-Cue Object Recognition (MCOR) algorithm which attempted to use multiple features of any type to produce more robust object recognition. All results so far reported with MCOR have been on data collected by ourselves. In this work, we introduce new advancements in the MCOR algorithm to increase its effectiveness and ability to deal with complex real data from outside datasets. These advancements include the integration of Scale-Invariant Feature Transform (SIFT) features and an improvement in training. To demonstrate the effectiveness of the MCOR framework, we first show a comparison of the MCOR algorithm to an outside dataset to show its basic advantages. We then demonstrate the advanced MCOR features on real television video datasets in particular cooking.
Starting Page	4554
Ending Page	4559
File Size	1507079
Page Count	6
File Format	PDF
ISBN	9781424466740
ISSN	21530866
e-ISBN	9781424466764
DOI	10.1109/IROS.2010.5649377
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-18
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Visualization Object recognition Training Feature extraction Dictionaries Histograms Image color analysis
Content Type	Text
Resource Type	Article

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