Session WedAT1 Pegaso A Wednesday, October 10, 2012 ... - Lirmm
Session WedAT1 Pegaso A Wednesday, October 10, 2012 ... - Lirmm
Session WedAT1 Pegaso A Wednesday, October 10, 2012 ... - Lirmm
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<strong>Session</strong> WedBT<strong>10</strong> Lince <strong>Wednesday</strong>, <strong>October</strong> <strong>10</strong>, <strong>2012</strong>, 09:30–<strong>10</strong>:30<br />
Visual Learning I<br />
Chair<br />
Co-Chair<br />
09:30–09:45 WedBT<strong>10</strong>.1<br />
Bag of Multimodal Hierarchical Dirichlet Processes:<br />
Model of Complex Conceptual Structure<br />
for Intelligent Robots<br />
Tomoaki Nakamura and Takayuki Nagai<br />
The University of Electro-Communications, Japan<br />
Naoto Iwahashi<br />
NICT Knowledge Creating Communication Research Center, Japan<br />
• A novel framework for concept<br />
formation<br />
• Various models are formed by<br />
multimodal HDP-based categorization<br />
with varying parameters<br />
Bag of Multimodal HDP models<br />
• Word meanings are grounded in<br />
formed categories through the<br />
interaction between users and the<br />
robot<br />
• The interaction works as model<br />
selection for Bag of Multimodal HDP<br />
• Complex structure is visualized by<br />
Multidimensional scaling<br />
Observation of<br />
objects<br />
Categorization of<br />
multimodal<br />
information<br />
Word grounding and model selection<br />
This is “yellow”<br />
“maraca”.<br />
Maraca<br />
<strong>10</strong>:00–<strong>10</strong>:15 WedBT<strong>10</strong>.3<br />
Learning a Projective Mapping to Locate<br />
Animals in Video Using RFID<br />
Pipei Huang Rahul Sawhney<br />
Daniel Walker<br />
Aaron Bobick Tucker Balch<br />
Robotics and Intelligent Machines<br />
School of Interactive Computing<br />
Georgia Institute of Technology, USA<br />
• Goal is to annotate video with correct<br />
locations and IDs of multiple moving<br />
animals wearing active RFID tags.<br />
• Challenges include noisy position data and<br />
integration with camera calibration.<br />
• Approach:<br />
• Filtering and outlier removal improves<br />
RFID reported position.<br />
• Use Machine Learning-based<br />
approach to map from RFID (x,y,z) to<br />
image plane.<br />
• Learn mapping of offset from best-fit<br />
standard camera calibration model.<br />
Reduces data needed,<br />
• Improves performance and reduces<br />
calibration effort.<br />
Kim Wallen<br />
Dept. of<br />
Psychology<br />
Emory Univ., USA<br />
Shiyin Qin<br />
Yello<br />
w<br />
School of Automation<br />
Science and Elec Eng<br />
Geihang Univ, China<br />
09:45–<strong>10</strong>:00 WedBT<strong>10</strong>.2<br />
Robust and Fast Visual Tracking Using Constrained<br />
Sparse Coding and Dictionary Learning<br />
Tianxiang Bai, Y.F. Li, and Xiaolong Zhou<br />
Department of Mechanical and Biomedical Engineer, City Univ. of Hong Kong,<br />
Hong Kong SAR, China<br />
• The visual appearance is represented<br />
and modeled by sparse<br />
representation and online dictionary<br />
learning.<br />
• A sparsity consistency constraint is<br />
defined to unify sparse representation<br />
and online dictionary learning.<br />
• An elastic-net constraint is enforced<br />
to capture the local appearances<br />
during dictionary learning stage.<br />
• The proposed appearance model is<br />
integrated with particle filter to form a<br />
robust tracking algorithm.<br />
<strong>2012</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–137–<br />
Proposed Appearance Model<br />
<strong>10</strong>:15–<strong>10</strong>:30 WedBT<strong>10</strong>.4<br />
A Discriminative Approach for Appearance<br />
Based Loop Closing<br />
Thomas Ciarfuglia, Gabriele Costante, Paolo Valigi and Elisa Ricci<br />
Departement of Information and Electronic Engineering, University of Perugia,<br />
Italy<br />
• Bag of Visual Words is a common<br />
paradigm for loop closing, but has<br />
limitations<br />
• We propose a novel optimization<br />
approach to compute visual word<br />
weights for loop closing<br />
• More discriminative words are<br />
emphasized, while less one are<br />
de-emphasized<br />
• This Place Recognition approach<br />
yields competitive results with state-of-<br />
the-art approaches<br />
Learned weights enhance visual words<br />
which increase the similarity score<br />
between images of the same place while<br />
keeping away images from different<br />
classes.