Session WedAT1 Pegaso A Wednesday, October 10, 2012 ... - Lirmm

Session WedBT10 Lince Wednesday, October 10, 2012, 09:30–10:30
Visual Learning I
Chair
Co-Chair
09:30–09:45 WedBT10.1
Bag of Multimodal Hierarchical Dirichlet Processes:
Model of Complex Conceptual Structure
for Intelligent Robots
Tomoaki Nakamura and Takayuki Nagai
The University of Electro-Communications, Japan
Naoto Iwahashi
NICT Knowledge Creating Communication Research Center, Japan
• A novel framework for concept
formation
• Various models are formed by
multimodal HDP-based categorization
with varying parameters
Bag of Multimodal HDP models
• Word meanings are grounded in
formed categories through the
interaction between users and the
robot
• The interaction works as model
selection for Bag of Multimodal HDP
• Complex structure is visualized by
Multidimensional scaling
Observation of
objects
Categorization of
multimodal
information
Word grounding and model selection
This is “yellow”
“maraca”.
Maraca
10:00–10:15 WedBT10.3
Learning a Projective Mapping to Locate
Animals in Video Using RFID
Pipei Huang Rahul Sawhney
Daniel Walker
Aaron Bobick Tucker Balch
Robotics and Intelligent Machines
School of Interactive Computing
Georgia Institute of Technology, USA
• Goal is to annotate video with correct
locations and IDs of multiple moving
animals wearing active RFID tags.
• Challenges include noisy position data and
integration with camera calibration.
• Approach:
• Filtering and outlier removal improves
RFID reported position.
• Use Machine Learning-based
approach to map from RFID (x,y,z) to
image plane.
• Learn mapping of offset from best-fit
standard camera calibration model.
Reduces data needed,
• Improves performance and reduces
calibration effort.
Kim Wallen
Dept. of
Psychology
Emory Univ., USA
Shiyin Qin
Yello
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School of Automation
Science and Elec Eng
Geihang Univ, China
09:45–10:00 WedBT10.2
Robust and Fast Visual Tracking Using Constrained
Sparse Coding and Dictionary Learning
Tianxiang Bai, Y.F. Li, and Xiaolong Zhou
Department of Mechanical and Biomedical Engineer, City Univ. of Hong Kong,
Hong Kong SAR, China
• The visual appearance is represented
and modeled by sparse
representation and online dictionary
learning.
• A sparsity consistency constraint is
defined to unify sparse representation
and online dictionary learning.
• An elastic-net constraint is enforced
to capture the local appearances
during dictionary learning stage.
• The proposed appearance model is
integrated with particle filter to form a
robust tracking algorithm.
2012 IEEE/RSJ International Conference on Intelligent Robots and Systems
–137–
Proposed Appearance Model
10:15–10:30 WedBT10.4
A Discriminative Approach for Appearance
Based Loop Closing
Thomas Ciarfuglia, Gabriele Costante, Paolo Valigi and Elisa Ricci
Departement of Information and Electronic Engineering, University of Perugia,
Italy
• Bag of Visual Words is a common
paradigm for loop closing, but has
limitations
• We propose a novel optimization
approach to compute visual word
weights for loop closing
• More discriminative words are
emphasized, while less one are
de-emphasized
• This Place Recognition approach
yields competitive results with state-of-
the-art approaches
Learned weights enhance visual words
which increase the similarity score
between images of the same place while
keeping away images from different
classes.