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

Session WedCT10 Lince Wednesday, October 10, 2012, 11:00–12:30
Visual Learning II
Chair Edwin Olson, Univ. of Michigan
Co-Chair
11:00–11:15 WedCT10.1
Clustering-based Discriminative Locality
Alignment for Face Gender Recognition
Duo Chen
College of Communication Engineering, Chongqing University, China
Jun Cheng
Shenzhen Institutes of Advanced Technology, CAS, China
The Chinese University of Hong Kong
Dacheng Tao
Faculty of Engineering and Information Technology, University of Technology
Sydney, Australia
• To facilitate human-robot interactions,
human gender information is very
important.
• It is essential to develop a simple and fast
way based on dimensional reduction to
recognize gender.
• Both global geometry and local geometry
of data are essential to estimate the lower
dimensional projection.
• CDLA exploits global geometry, local
geometry and discriminative information.
p1
A
B
p2
CDLA makes the connected
points in the k1 nearest graph
closer. By k-means clustering
(taking the global geometry into
count), it avoids making the far
away points connected.
11:30–11:45 WedCT10.3
A System of Automated Training Sample
Generation for Visual-based Car Detection
Chao Wang, Huijing Zhao and Hongbin Zha
Key Lab of Machine Perception (MOE), Peking Univ., China
Franck Davoine
CNRS and LIAMA Sino French Laboratory, Beijing, China
• This paper presents a system to automatically
generate car image sample dataset.
• The dataset contains multi-view car image
samples with car’s pose information.
• A system of detecting and tracking onroad
vehicles using multiple single-layer
lasers is developed.
• Multi-view car samples are generated
based on the tracking results and multiview
camera data.
Car samples divided into 8 subcategories
12:00–12:15 WedCT10.5
On-line semantic perception using uncertainty
Roderick de Nijs, Juan Sebastian Ramos Pachón, Kolja Kühnlenz
Institute of Automatic Control Engineering, Technische Universität München,
Germany
Gemma Roig, Xavier Boix, Luc van Gool
Computer Vision Laboratory, ETH Zurich, Switzerland
Can a semantic labeling algorithm
benefit from uncertainty?
• Buffer of images for on-line semantic
segmentation
• Perturb-and-MAP random fields to
compute uncertainty
• Spend more computation time on
uncertain regions
Above: Urban scene
Below: Class undertainty
11:15–11:30 WedCT10.2
IEEE/RSJ IROS 2012 Digest Template
Incorporating Geometric Information into Gaussian
Process Terrain Models from Monocular Images
Tariq Abuhashim and Salah Sukkarieh
Australian Centre for Field Robotics
The University of Sydney
NSW 206, Australia
• This paper presents a novel approach to
incorporate differential geometry into
depth estimation from monocular images
that is based on the Gaussian Process
Derivative Observations (GDP)
formulation.
• Experimental results are presented using
synthesized examples and real monocular
images captured from an Unmanned
Aerial Vehicle (UAV).
• Results show improvement in depth
estimation over standard Gaussian
Process Regression (GPR).
2012 IEEE/RSJ International Conference on Intelligent Robots and Systems
–146–
Ground and aerial robotics used
to reconstruct 3D maps
11:45–12:00 WedCT10.4
Learning and Recognition of Objects Inspired by
Early Cognition
Maja Rudinac and Pieter Jonker
Biorobotics Lab, Delft University of Technology, The Netherlands
Gert Kootstra and Danica Kragic
Computer Vision and Active Perception lab, KTH Royal Institute of Technology,
Sweden
• We present a unifying approach for learning
and recognition of objects in unstructured
environments through exploration. We
establish 4 principles for object learning.
• First, early object detection is based on an
attention mechanism detecting salient parts in
the scene.
• Second, motion of the object allows more
accurate object localization,
• Next, acquiring multiple observations of the
object through manipulation allows a more
robust representation of the object.
• And last, object recognition benefits from a
multi-modal representation.
• This approach shows significant improvement
of the system when multiple observations are
acquired from active object manipulation.
Cognitive model for object
learning and recognition
12:15–12:30 WedCT10.6
A High-Accuracy Visual Marker
Based on a Microlens Array
Hideyuki Tanaka, Yasushi Sumi, and Yoshio Matsumoto
Intelligent Systems Research Institute, AIST, Japan
• ArrayMark: A novel AR marker utilizing a 2-D moiré pattern
based on a microlens array
• Accurate pose estimation (