|
|
cosyBib2006.bib
@INPROCEEDINGS{Bertolli06a,
AUTHOR = {Federico Bertolli and Patric Jensfelt and Henrik I. Christensen},
TITLE = {SLAM using Visual Scan-Matching with Distinguishable 3D Points},
BOOKTITLE = {Proc.~of the IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS'05)},
YEAR = {2006},
URL = {http://www.cas.kth.se/~patric/publications/fedepaper.pdf}
}
@ARTICLE{fidlerPAMI06,
AUTHOR = {Sanja Fidler and Danijel Sko\v{c}aj and Ale\v{s} Leonardis},
TITLE = {Combining Reconstructive and Discriminative Subspace Methods for
Robust Classification and Regression by Subsampling},
JOURNAL = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
YEAR = {2006},
VOLUME = {28},
PAGES = {337-350},
NUMBER = {3},
MONTH = {March},
ABSTRACT = {Linear subspace methods that provide sufficient reconstruction of
the data such as PCA offer an efficient way of dealing with missing
pixels, outliers, and occlusions that often appear in the visual
data. Discriminative methods, such as LDA and CCA, which on the other
hand, are better suited for classification and regression tasks,
are highly sensitive to corrupted data. We present a theoretical
framework for achieving best of both types of methods: an approach
that combines the discrimination power of discriminative methods
with the reconstruction property of reconstructive methods which
enables one to work on subsets of pixels in images, to efficiently
detect and reject the outliers. The proposed approach is therefore
capable of robust classification/regression with a high-breakdown
point. The theoretical results are demonstrated on several computer
vision tasks showing that the proposed approach significantly outperforms
the standard discriminative methods in the case of missing pixels
and images containing occlusions and outliers.},
OWNER = {danijels fidler vicos rpcv cosy mobvis DSSP1},
TIMESTAMP = {2006.03.03},
URL = {http://vicos.fri.uni-lj.si/data/publications/fidlerPAMI06.pdf}
}
@INPROCEEDINGS{Fritz06a,
AUTHOR = {M. Fritz and B. Schiele},
TITLE = {Towards Unsupervised Discovery of Visual Categories},
BOOKTITLE = {Proceedings of 28th Annual Symposium of the German Association for
Pattern Recognition (DAGM), Berlin, Germany},
YEAR = {2006},
MONTH = {September},
ABSTRACT = {Recently, many approaches have been proposed for visual object category
detection. They vary greatly in terms of how much supervision is
needed. High performance ob ject detection methods tend to be trained
in a supervised manner from relatively clean data. In order to deal
with a large number of ob ject classes and large amounts of training
data, there is a clear desire to use as little supervision as possible.
This paper proposes a new approach for unsupervised learning of visual
categories based on a scheme to detect reoccurring structure in sets
of images. The approach finds the locations as well as the scales
of such reoccurring structures in an unsupervised manner. In the
experiments those reoccurring structures correspond to ob ject categories
which can be used to directly learn object category models. Experimental
results show the effectiveness of the new approach and compare the
performance to previous fully-supervised methods.},
URL = {http://www.mis.informatik.tu-darmstadt.de/People/mfritz/fritz06dagm.pdf}
}
@INPROCEEDINGS{hawesetal06gc5,
AUTHOR = {Nick Hawes and Aaron Sloman and Jeremy Wyatt},
TITLE = {Requirements \& Designs: Asking Scientific Questions About Architectures},
BOOKTITLE = {Proceedings of the AISB '06 Symposium on GC5: Architecture of Brain
and Mind: Integrating high level cognitive processes with brain mechanisms
and functions in a working robot},
YEAR = {2006},
ADDRESS = {Bristol},
MONTH = {April},
ABSTRACT = {This paper discusses our views on the future of the field of cognitive
architectures, and how the scientific questions that define it should
be addressed. We also report on a set of requirements, and a related
architecture design, that we are currently investigating as part
of the CoSy project.},
URL = {http://www.cs.bham.ac.uk/~nah/bibtex/papers/hawesetal06gc5.pdf}
}
@INPROCEEDINGS{haweswyatt06towards,
AUTHOR = {Nick Hawes and Jeremy Wyatt},
TITLE = {Towards Context-Sensitive Visual Attention},
BOOKTITLE = {Proceedings of the Second International Cognitive Vision Workshop
(ICVW06)},
YEAR = {2006},
ADDRESS = {Graz, Austria},
MONTH = {May},
ABSTRACT = {In this paper we present a discussion of information processing context
and how we believe a visual attention system should be influenced
by contextual information. We support this argument with a proof-of-concept
design and implementation of a context-sensitive extension to the
Itti & Koch model of visual attention as part of an architecture
for a cognitive system. Our model demonstrates improved performance
in terms of both fixations and processing time on visual search tasks
compared to the non-extended model.},
URL = {http://www.cs.bham.ac.uk/research/projects/cosy/bib-db/papers/haweswyatt06.pdf}
}
@INPROCEEDINGS{hongengwyatt06humanoids,
AUTHOR = {S. Hongeng and J. L. Wyatt},
TITLE = {Learning Causality and Intention in Human Actions},
BOOKTITLE = {Proceedings of the 6th IEEE-RAS International Conference on Humanoid
Robots},
YEAR = {2006},
ADDRESS = {Genoa{,} Italy},
MONTH = {December},
ABSTRACT = {Previous research has shown that human actions can be detected by
motion patterns. However, labeling motion patterns is not sufficient
in a cognitive system that requires reasoning about the agentā??s
intentions, and how the environmental context affects the way an
action is performed. In this paper, we develop a graphical model
that captures how the movements that realize the action vary depending
on the situations, and present statistical learning algorithms. Using
object manipulation tasks, we illustrate how a system infers the
agentā??s goals from visual observation and compare results with
findings in psychological experiments.},
URL = {http://www.cs.bham.ac.uk/~sxh/bibtex/papers/hongeng_humanoids06.pdf}
}
@INPROCEEDINGS{kruijffetal06pit,
AUTHOR = {Geert-Jan M. Kruijff and John D. Kelleher and Nick Hawes},
TITLE = {Information Fusion For Visual Reference Resolution In Dynamic Situated
Dialogue},
BOOKTITLE = {Perception and Interactive Technologies: International Tutorial and
Research Workshop, PIT 2006},
YEAR = {2006},
EDITOR = {Elisabeth Andre and Laila Dybkjaer and Wolfgang Minker and Heiko
Neumann and Michael Weber},
VOLUME = {4021},
SERIES = {Lecture Notes in Computer Science},
PAGES = {117 -- 128},
ADDRESS = {Kloster Irsee, Germany},
MONTH = {June},
PUBLISHER = {Springer Berlin / Heidelberg},
ABSTRACT = {Human-Robot Interaction (HRI) invariably involves dialogue about objects
in the environment in which the agents are situated. The paper focuses
on the issue of resolving discourse references to such visual objects.
The paper addresses the problem using strategies for intra-modal
fusion (identifying that different occurrences concern the same object),
and inter-modal fusion, (relating object references across different
modalities). Core to these strategies are sensorimotoric coordination,
and ontology-based mediation between content in different modalities.
The approach has been fully implemented, and is illustrated with
several working examples.},
EE = {http://dx.doi.org/10.1007/11768029_12},
URL = {http://www.cs.bham.ac.uk/~nah/bibtex/papers/kruijffetal06pit.pdf}
}
@INPROCEEDINGS{Kruijff06a,
AUTHOR = {Geert-Jan M. Kruijff and Hendrik Zender and Patric Jensfelt and Henrik
I. Christensen},
TITLE = {Clarification dialogues in human-augmented mapping},
BOOKTITLE = {Proc.~of the 1st Annual Conference on Human-Robot Interaction (HRI'06)},
YEAR = {2006},
ADDRESS = {Salt Lake City, UT},
MONTH = MAR,
URL = {http://www.cas.kth.se/~patric/publications/main.mapping.hri2006.pdf}
}
@INPROCEEDINGS{Kruijff06b,
AUTHOR = {Geert-Jan M. Kruijff and Hendrik Zender and Patric Jensfelt and Henrik
I. Christensen},
TITLE = {Situated dialogue and understanding spatial organization: Knowing
what is where and what you can do there},
BOOKTITLE = {Proc.~of IEEE Workshop on Robot and Human Interactive Communication
{(ROMAN)}},
YEAR = {2006}
}
@ARTICLE{Leibe05c,
AUTHOR = {B. Leibe and A. Leonardis and B. Schiele},
TITLE = {Robust Object Detection by Interleaving Categorization and Segmentation},
JOURNAL = {International Journal of Computer Vision},
YEAR = {2006},
ABSTRACT = {This paper presents a new method for visual object categorization,
i.e.~for recognizing previously unseen objects, localizing them in
cluttered images, and assigning the correct category label. It considers
object categorization and figure-ground segmentation as two interleaved
processes that closely collaborate towards a common goal. As shown
in our work, the tight coupling between those two processes allows
them to profit from each other and improve the combined performance.
The core part of our work is a highly flexible learned representation
for object shape that can combine the information observed on different
training examples in a probabilistic extension of the Generalized
Hough Transform. The resulting approach can detect categorical objects
in novel images and automatically infer a probabilistic segmentation
from the recognition result. This segmentation is then used to again
improve recognition by allowing the system to focus its efforts on
object pixels and discard misleading influences from the background.
Moreover, the information from where in the image a hypothesis draws
its support is used in an MDL based hypothesis verification stage
to resolve ambiguities between overlapping hypotheses and factor
out the effects of partial occlusion. An extensive evaluation on
several large data sets shows that the proposed system is applicable
to a range of different object categories, including both rigid and
articulated objects. In addition, its flexible representation allows
it to achieve competitive object detection performance already from
training sets that are between one and two orders of magnitude smaller
than those used in comparable systems.}
}
@INPROCEEDINGS{Leibe06c,
AUTHOR = {B. Leibe and K. Mikolajczyk and B. Schiele},
TITLE = {Efficient Clustering and Matching for Object Class Recognition},
BOOKTITLE = {Proceedings of the 17th British Machine Vision Conference, Edinburgh,
England},
YEAR = {2006},
ABSTRACT = {In this paper we address the problem of building object class representations
based on local features and fast matching in a large database. We
propose an efficient algorithm for hierarchical agglomerative clustering.
We examine different agglomerative and partitional clustering strategies
and compare the quality of obtained clusters. Our combination of
partitional-agglomerative clustering gives significant improvement
in terms of efficiency while maintaining the same quality of clusters.
We also propose a method for building data structures for fast matching
in high dimensional feature spaces. These improvements allow to deal
with large sets of training data typically used in recognition of
multiple object classes.},
URL = {http://www.mis.informatik.tu-darmstadt.de/Publications/leibe-efficientclustering-bmvc06.pdf}
}
@INPROCEEDINGS{Leibe06d,
AUTHOR = {B. Leibe and K. Mikolajczyk and B. Schiele},
TITLE = {Segmentation Based Multi-Cue Integration for Object Detection},
BOOKTITLE = {Proceedings of the 17th British Machine Vision Conference, Edinburgh,
England},
YEAR = {2006},
ABSTRACT = {This paper proposes a novel method for integrating multiple local
cues, i.e. local region detectors as well as descriptors, in the
context of object detection. Rather than to fuse the outputs of several
distinct classifiers in a fixed setup, our approach implements a
highly flexible combination scheme, where the contributions of all
individual cues are flexibly recombined depending on their explanatory
power for each new test image. The key idea behind our approach is
to integrate the cues over an estimated top-down segmentation, which
allows to quantify how much each of them contributed to the object
hypothesis. By combining those contributions on a per-pixel level,
our approach ensures that each cue only contributes to object regions
for which it is confident and that potential correlations between
cues are effectively factored out. Experimental results on several
benchmark data sets show that the proposed multi-cue combination
scheme significantly increases detection performance compared to
any of its constituent cues alone. Moreover, it provides an interesting
evaluation tool to analyze the complementarity of local feature detectors
and descriptors.},
URL = {http://www.mis.informatik.tu-darmstadt.de/Publications/leibe-multicue-bmvc06.pdf}
}
@TECHREPORT{luo06kth_idol2,
AUTHOR = {Luo, J. and Pronobis, A. and Caputo, B. and Jensfelt, P.},
TITLE = {The {KTH-IDOL2} Database},
INSTITUTION = {Kungliga Tekniska Hoegskolan, CVAP/CAS},
YEAR = {2006},
NUMBER = {CVAP304},
MONTH = {October},
URL = {http://www.csc.kth.se/~pronobis/research/luo06kth_idol2}
}
@INPROCEEDINGS{mozos2006iros,
AUTHOR = {Mart\'{i}nez Mozos, O. and Burgard, W.},
TITLE = {Supervised Learning of Topological Maps using Semantic Information
Extracted from Range Data},
BOOKTITLE = {Proc.~of the IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS)},
YEAR = {2006},
PAGES = {2772-2777},
ADDRESS = {Beijing, China},
ABSTRACT = { This paper presents an approach to create topological maps from geometric
maps obtained with a mobile robot in an indoor-environment using
range data. Our approach uses AdaBoost, a supervised learning algorithm,
to classify each point of the geometric map into semantic classes.
We then apply a segmentation procedure based on probabilistic relaxation
labeling on the resulting classications to eliminate errors. The
topological graph is then extracted from the individual di erent
regions and their connections. In this way, we obtain a topological
map in the form of a graph, in which each node indicates a region
in the environment with its corresponding semantic class (e.g., corridor,
or room) and the edges indicate the connections between them. Experimental
results obtained with data from di erent real-world environments
demonstrate the effectiveness of our approach.},
URL = {http://www.informatik.uni-freiburg.de/~omartine/publications/mozos2006iros.pdf}
}
@INPROCEEDINGS{mozos2006iros_w,
AUTHOR = {Mart\'{i}nez Mozos, O. and Rottmann, A. and Triebel, R. and Jensfelt,
P. and Burgard, W.},
TITLE = {Semantic Labeling of Places using Information Extracted from Laser
and Vision Sensor Data},
BOOKTITLE = {In Proc.~of the IEEE/RSJ IROS 2006 Workshop: From Sensors to Human
Spatial Concepts},
YEAR = {2006},
ADDRESS = {Beijing, China},
ABSTRACT = {Indoor environments can typically be divided into places with different
functionalities like corridors, kitchens, offices, or seminar rooms.
The ability to learn such semantic categories from sensor data enables
a mobile robot to extend the representation of the environment facilitating
the interaction with humans. As an example, natural language terms
like ``corridor" or ``room" can be used to communicate the position
of the robot in a map in a more intuitive way. In this work, we first
propose an approach based on supervised learning to classify the
pose of a mobile robot into semantic classes. Our method uses AdaBoost
to boost simple features extracted from range data and vision into
a strong classifier. We present two main applications of this approach.
Firstly, we show how our approach can be utilized by a moving robot
for an online classification of the poses traversed along its path
using a hidden Markov model. Secondly, we introduce an approach to
learn topological maps from geometric maps by applying our semantic
classification procedure in combination with a probabilistic relaxation
procedure. We finally show how to apply associative Markov networks
(AMNs) together with AdaBoost for classifying complete geometric
maps. Experimental results obtained in simulation and with real robots
demonstrate the effectiveness of our approach in various indoor environments.},
URL = {http://www.informatik.uni-freiburg.de/~omartine/publications/mozos2006iros_w.pdf}
}
@INPROCEEDINGS{Mikolajczyk06c,
AUTHOR = {K. Mikolajczyk and B. Leibe and B. Schiele},
TITLE = {Multiple Object Class Detection with a Generative Model},
BOOKTITLE = {Proceedings of the Conference on Computer Vision and Pattern Recognition,
New York, USA},
YEAR = {2006},
PAGES = { },
MONTH = {June},
ABSTRACT = {In this paper we propose an approach capable of simultaneous recognition
and localization of multiple object classes using a generative model.
We propose a novel hierarchical representation which allows to represent
individual images as well as various objects classes in a single
similarity invariant model. The recognition method is based on a
codebook representation where appearance clusters built from edge
based features are shared among several object classes. A probabilistic
model based on Bayesian rules allows for reliable detection of various
objects in the same image. The approach is very efficient due to
applied fast clustering and matching method capable of dealing with
millions of high dimensional features. The system shows an excellent
performance on several object categories in wide range of scales,
in-plane rotations, background clutter, and occlusion. The performance
is comparable with state of the art approaches dedicated to single
object classes.},
URL = {http://www.mis.informatik.tu-darmstadt.de/Publications/mikolajczyk-multiclass-cvpr06.pdf}
}
@INPROCEEDINGS{Pacchierotti06a,
AUTHOR = {E. Pacchierotti and H.I. Christensen and P. Jensfelt},
TITLE = {Design of an office guide robot for social interaction studies},
BOOKTITLE = {Proc.~of the IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS'06)},
YEAR = {2006},
URL = {http://www.cas.kth.se/~patric/publications/iros06_elena.pdf}
}
@INPROCEEDINGS{Pacchierotti06b,
AUTHOR = {E. Pacchierotti and H.I. Christensen and P. Jensfelt},
TITLE = {Evaluation of passing distance for social robots},
BOOKTITLE = {IEEE Workshop on Robot and Human Interactive Communication {(ROMAN)}},
YEAR = {2006},
ADDRESS = {Hartfordshire, UK},
MONTH = SEP,
URL = {http://www.cas.kth.se/~patric/publications/roman06-elena.pdf}
}
@ARTICLE{Philipona06,
AUTHOR = { David L Philipona and J Kevin O'Regan},
TITLE = {Color naming, unique hues, and hue cancellation predicted from singularities
in reflection properties.},
JOURNAL = {Vis Neurosci},
YEAR = {2006},
VOLUME = {23},
PAGES = {331-9},
NUMBER = {3-4},
ABSTRACT = {Psychophysical studies suggest that different colors have different
perceptual status: red and blue for example are thought of as elementary
sensations whereas yellowish green is not. The dominant account for
such perceptual asymmetries attributes them to specificities of the
neuronal representation of colors. Alternative accounts involve cultural
or linguistic arguments. What these accounts have in common is the
idea that there are no asymmetries in the physics of light and surfaces
that could underlie the perceptual structure of colors, and this
is why neuronal or cultural processes must be invoked as the essential
underlying mechanisms that structure color perception. Here, we suggest
a biological approach for surface reflection properties that takes
into account only the information about light that is accessible
to an organism given the photopigments it possesses, and we show
that now asymmetries appear in the behavior of surfaces with respect
to light. These asymmetries provide a classification of surface properties
that turns out to be identical to the one observed in linguistic
color categorization across numerous cultures, as pinned down by
cross cultural studies. Further, we show that data from psychophysical
studies about unique hues and hue cancellation are consistent with
the viewpoint that stimuli reported by observers as special are those
associated with this singularity-based categorization of surfaces
under a standard illuminant. The approach predicts that unique blue
and unique yellow should be aligned in chromatic space while unique
red and unique green should not, a fact usually conjectured to result
from nonlinearities in chromatic pathways.},
URL = {http://nivea.psycho.univ-paris5.fr/PhiliponaVisNeurosci/PhiliponaVisNeurosci.pdf}
}
@INPROCEEDINGS{plagemann06euros,
AUTHOR = {Plagemann, C. and Stachniss, C. and Burgard, W.},
TITLE = {Efficient Failure Detection for Mobile Robots using Mixed-Abstraction
Particle Filters},
BOOKTITLE = {European Robotics Symposium 2006},
YEAR = {2006},
EDITOR = {H.I. Christiensen},
VOLUME = {22},
SERIES = {STAR Springer tracts in advanced robotics},
PAGES = {93--107},
PUBLISHER = {Springer-Verlag Berlin Heidelberg, Germany},
ABSTRACT = {In this paper, we consider the problem of online failure detection
and isolation for mobile robots. The goal is to enable a mobile robot
to determine whether the system is running free of faults or to identify
the cause for faulty behavior. In general, failures cannot be detected
by solely monitoring the process model for the error free mode because
if certain model assumptions are violated the observation likelihood
might not indicate a defect. Existing approaches therefore use comparably
complex system models to cover all possible system behaviors. In
this paper, we propose the mixed-abstraction particle filter as an
efficient way of dealing with potential failures of mobile robots.
It uses a hierarchy of process models to actively validate the model
assumptions and distribute the computational resources between the
models adaptively. We present an implementation of our algorithm
and discuss results obtained from simulated and real-robot experiments.},
ISBN = {3-540-32688-X},
PDFURL = {http://www.informatik.uni-freiburg.de/~plagem/bib/plagemann06euros.pdf}
}
@INPROCEEDINGS{pronobis06iros,
AUTHOR = {Pronobis, A. and Caputo, B. and Jensfelt, P. and Christensen, H.
I.},
TITLE = {A Discriminative Approach to Robust Visual Place Recognition},
BOOKTITLE = {Proceedings of the IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS06)},
YEAR = {2006},
ADDRESS = {Beijing, China},
MONTH = {October},
URL = {http://www.csc.kth.se/~pronobis/research/pronobis06iros/pronobis06iros.pdf}
}
@INPROCEEDINGS{seemann06cvpr,
AUTHOR = {E. Seemann and B. Leibe and B. Schiele},
TITLE = {Multi-Aspect Detection of Articulated Objects},
BOOKTITLE = {Proceedings of the Conference on Computer Vision and Pattern Recognition,
New York, USA},
YEAR = {2006},
PAGES = { },
MONTH = {June},
ABSTRACT = {A wide range of methods have been proposed to detect and recognize
objects. However, effective and efficient multiviewpoint detection
of objects is still in its infancy, since most current approaches
can only handle single viewpoints or aspects. This paper proposes
a general approach for multi-aspect detection of objects. As the
running example for detection we use pedestrians, which add another
difficulty to the problem, namely human body articulations. Global
appearance changes caused by different articulations and viewpoints
of pedestrians are handled in a unified manner by a generalization
of the Implicit Shape Model. An important property of this new approach
is to share local appearance across different articulations and viewpoints,
therefore requiring relatively few training samples. The effectiveness
of the approach is shown and compared to previous approaches on two
datasets containing pedestrians with different articulations and
from multiple viewpoints.},
URL = {http://www.mis.informatik.tu-darmstadt.de/seemann/seemann06cvpr.pdf}
}
@INPROCEEDINGS{Seemann06b,
AUTHOR = {E. Seemann and B. Schiele},
TITLE = {Cross-Articulation Learning for Robust Detection of Pedestrians},
BOOKTITLE = {Proceedings of 28th Annual Symposium of the German Association for
Pattern Recognition (DAGM), Berlin, Germany},
YEAR = {2006},
ADDRESS = {Berlin, Germany},
MONTH = {September},
ABSTRACT = {Recognizing categories of articulated ob jects in real-world scenarios
is a challenging problem for today's vision algorithms. Due to the
large appearance changes and intra-class variability of these objects,
it is hard to define a model, which is both general and discriminative
enough to capture the properties of the category. In this work, we
propose an approach, which aims for a suitable trade-off for this
problem. On the one hand, the approach is made more discriminant
by explicitly distinguishing typical ob ject shapes. On the other
hand, the method generalizes well and requires relatively few training
samples by cross-articulation learning. The effectiveness of the
approach is shown and compared to previous approaches on two datasets
containing pedestrians with different articulations.},
URL = {http://www.mis.informatik.tu-darmstadt.de/seemann/seemann06dagm.pdf}
}
@ARTICLE{dsPR06,
AUTHOR = {D. Sko\v{c}aj and A. Leonardis and H. Bischof},
TITLE = {Weighted and robust learning of subspace representations},
JOURNAL = {Pattern recognition},
YEAR = {2006},
VOLUME = {40},
PAGES = {1556-1569},
NUMBER = {5},
ABSTRACT = {A reliable system for visual learning and recognition should enable
a selective treatment of individual parts of input data and should
successfully deal with noise and occlusions. These requirements are
not satisfactorily met when visual learning is approached by appearance-based
modeling of objects and scenes using the traditional PCA approach.
In this paper we extend standard PCA approach to overcome these shortcomings.
We first present a weighted version of PCA, which, unlike the standard
approach, considers individual pixels and images selectively, depending
on the corresponding weights. Then we propose a robust PCA method
for obtaining a consistent subspace representation in the presence
of outlying pixels in the training images. The method is based on
the EM algorithm for estimation of principal subspaces in the presence
of missing data. We demonstrate the efficiency of the proposed methods
in a number of experiments.},
URL = {http://www.cognitivesystems.org/publications/uol_ds_pr06.pdf}
}
@INPROCEEDINGS{skocajCVWW06,
AUTHOR = {D. Sko\v{c}aj and M. Uray and A. Leonardis and H. Bischof},
TITLE = {Why to Combine Reconstructive and Discriminative Information for
Incremental Subspace Learning},
BOOKTITLE = {CVWW 2006 : proceedings of the 11th Computer Vision Winter Workshop},
YEAR = {2006},
PAGES = {52-57},
ADDRESS = {Tel\v{c}, Czech Republic},
MONTH = {February 6-8},
ABSTRACT = {In the paper we propose a novel method for incremental visual learning
by combining reconstructive and discriminative subspace methods.
This is achieved by embedding LDA learning and classification into
the incremental PCA framework. The combined subspace consists of
a truncated PCA subspace and a few additional basis vectors that
encompass the discriminative information, which would be lost by
the discarded principal vectors. As such it contains both sufficient
reconstructive information to enable incremental learning, and the
previously extracted discriminative information to enable efficient
classification as well. We demonstrate that we are able to efficiently
update the current model with new instances of the already learned
classes as well as to introduce new classes.},
OWNER = {danijels vicos rpcv cosy mobvis DSSP3},
TIMESTAMP = {2006.02.06},
URL = {http://vicos.fri.uni-lj.si/data/publications/skocajCVWW06.pdf}
}
@INPROCEEDINGS{sloman2006pieces,
AUTHOR = {Aaron Sloman},
TITLE = {How to Put the Pieces of AI Together Again},
BOOKTITLE = {Proceedings AAAI'06},
YEAR = {2006},
ADDRESS = {Boston},
MONTH = {July},
ABSTRACT = {Since the 1970s AI as a science has progressively fragmented into
many activities that are very narrowly focused. It is not clear that
work done within these fragments can be combined in the design of
a human-like integrated system -- long held as one of the goals of
AI as science. A strategy is proposed for reintegrating AI based
around a backward-chaining analysis to produce a roadmap with partially
ordered milestones, based on detailed scenarios, that everyone can
agree are worth achieving, even when they disagree about means. This
is a summary of ideas being developed within the CoSy project about
how to plan long term research using a partially ordered network
of scenarios and a grid of requirements for competences.},
URL = {http://www.cs.bham.ac.uk/research/projects/cosy/papers/aaai06-member.pdf}
}
@INPROCEEDINGS{slomanetal06cogrob,
AUTHOR = {Aaron Sloman and Jeremy Wyatt and Nick Hawes and Jackie Chappell
and Geert-Jan M. Kruijff},
TITLE = {Long Term Requirements for Cognitive Robotics},
BOOKTITLE = {Proceedings CogRob2006, The Fifth International Cognitive Robotics
Workshop. The AAAI-06 Workshop on Cognitive Robotics},
YEAR = {2006},
ADDRESS = {Boston, Massachusetts, USA},
MONTH = {July},
ABSTRACT = {This paper discusses some of the long term objectives of cognitive
robotics and some of the requirements for meeting those objectives
that are still a very long way off. These include requirements for
visual perception, for architectures, for kinds of learning, and
for innate competences needed to drive learning and development in
a variety of different environments. The work arises mainly out of
research on requirements for forms of representation and architectures
within the PlayMate scenario, which is a scenario concerned with
a robot that perceives, interacts with and talks about 3-D objects
on a tabletop, one of the scenarios in the EC-funded CoSy Robotics
project.},
URL = {http://www.cs.bham.ac.uk/~nah/bibtex/papers/slomanetal06cogrob.pdf}
}
@INPROCEEDINGS{stachniss2006icra,
AUTHOR = {Stachniss, C. and Mart\'{i}nez Mozos, O. and Burgard, W.},
TITLE = {Speeding-Up Multi-Robot Exploration by Considering Semantic Place
Information},
BOOKTITLE = {Proc.~of the IEEE Int.~Conf.~on Robotics \& Automation (ICRA)},
YEAR = {2006},
ADDRESS = {Orlando, FL, USA},
ABSTRACT = {In this paper, we consider the problem of exploring an unknown environment
with a team of mobile robots. One of the key issues in multi-robot
exploration is how to assign target locations to the individual robots.
To better distribute the robots over the environment and to avoid
redundant work, we take into account the type of place a potential
target is located in (e.g., a corridor or a room). To determine the
type of a place, we apply a classifier learned with AdaBoost which
additionally considers spatial dependencies between nearby locations.
Our approach to incorporate the type of places in the coordination
of the robots has been implemented and tested in different environments.
The experiments demonstrate that our system effectively distributes
the robots over the environment and allows them to accomplish their
mission faster compared to approaches that ignore the semantic place
labels.},
URL = {http://www.informatik.uni-freiburg.de/~omartine/publications/stachniss2006icra.pdf}
}
This file has been generated by
bibtex2html 1.79
Print this page
|
