NDLI: Structure discovery in multi-modal data: A region-based approach

Content Provider	IEEE Xplore Digital Library
Author	Collet, A. Srinivasa, S.S. Hebert, M.
Copyright Year	2011
Description	Author affiliation: Intel Labs Pittsburgh, 4720 Forbes Ave. Suite 410, PA, USA (Srinivasa, S.S.) \|\| The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA (Collet, A.; Hebert, M.)
Abstract	The ability of a perception system to discern what is important in a scene and what is not is an invaluable asset, with multiple applications in object recognition, people detection and SLAM, among others. In this paper, we aim to analyze all sensory data available to separate a scene into a few physically meaningful parts, which we term structure, while discarding background clutter. In particular, we consider the combination of image and range data, and base our decision in both appearance and 3D shape. Our main contribution is the development of a framework to perform scene segmentation that preserves physical objects using multi-modal data. We combine image and range data using a novel mid-level fusion technique based on the concept of regions that avoids any pixel-level correspondences between data sources. We associate groups of pixels with 3D points into multi-modal regions that we term regionlets, and measure the structure-ness of each regionlet using simple, bottom-up cues from image and range features. We show that the highest-ranked regionlets correspond to the most prominent objects in the scene. We verify the validity of our approach on 105 scenes of household environments.
Starting Page	5695
Ending Page	5702
File Size	1593800
Page Count	8
File Format	PDF
ISBN	9781612843865
ISSN	10504729
e-ISBN	9781612843858
DOI	10.1109/ICRA.2011.5980475
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-09
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Three dimensional displays Image segmentation Shape Image color analysis Cameras Humans Computational modeling
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Control and Systems Engineering Electrical and Electronic Engineering Software

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