CoSy logo Cognitive Systems for Cognitive Assistants
 
 
 

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 = {Proceedings of the International Conference on Intelligent Robots
	and Systems (IROS'06)},
  YEAR = {2006},
  ABSTRACT = {Scan-matching based on data from a laser scanner is frequently used
	for mapping and localization. This paper presents an scan-matching
	approach based instead on visual information from a stereo system.
	The Scale Invariant Feature Transform (SIFT) is used together with
	epipolar constraints to get high matching precision between the stereo
	images. Calculating the 3D position of the corresponding points in
	the world results in a visual scan where each point has a descriptor
	attached to it. These descriptors can be used when matching scans
	acquired from different positions. Just like in the work with laser
	based scan matching a map can be defined as a set of reference scans
	and their corresponding acquisition point. In essence this reduces
	each visual scan that can consist of hundreds of points to a single
	entity for which only the corresponding robot pose has to be estimated
	in the map. This reduces the overall complexity of the map. The SIFT
	descriptor attached to each of the points in the reference allows
	for robust matching and detection of loop closing situations. The
	paper presents real-world experimental results from an indoor office
	environment.},
  URL = {http://www.cognitivesystems.org/publications/fedepaper.pdf}
}

@INPROCEEDINGS{06cvpr,
  AUTHOR = {S. Fidler and G. Berginc and A. Leonardis},
  TITLE = {Hierarchical Statistical Learning of Generic Parts of Object Structure},
  BOOKTITLE = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
  YEAR = {2006},
  PAGES = {182--189},
  ADDRESS = {New York, USA},
  MONTH = {June},
  ABSTRACT = {With the growing interest in object categorization various methods
	have emerged that perform well in this challenging task, yet are
	inherently limited to only a moderate number of object classes. In
	pursuit of a more general categorization system this paper proposes
	a way to overcome the computational complexity encompassing the enormous
	number of different object categories by exploiting the statistical
	properties of the highly structured visual world. Our approach proposes
	a hierarchical acquisition of generic parts of object structure,
	varying from simple to more complex ones, which stem from the favorable
	statistics of natural images. The parts recovered in the individual
	layers of the hierarchy can be used in a top-down manner resulting
	in a robust statistical engine that could be efficiently used within
	many of the current categorization systems. The proposed approach
	has been applied to large image datasets yielding important statistical
	insights into the generic parts of object structure.},
  URL = {http://www.cognitivesystems.org/publications/fidlercvpr06.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.},
  URL = {http://www.cognitivesystems.org/publications/fidlerPAMI06.pdf}
}

@INPROCEEDINGS{Fritz06a,
  AUTHOR = {Mario Fritz and Bernt Schiele},
  TITLE = {Towards Unsupervised Discovery of Visual Categories},
  BOOKTITLE = {Proceedings of 28th Annual Symposium of the German Association for
	Pattern Recognition DAGM06},
  YEAR = {2006},
  ADDRESS = {Berlin, Germany},
  MONTH = SEP,
  ABSTRACT = {Recently, many approaches have been proposed for visual ob- ject 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 ob- ject category models. Experimental
	results show the effectiveness of the new approach and compare the
	performance to previous fully-supervised methods.}
}

@INPROCEEDINGS{Hawes/etal:2006b,
  AUTHOR = {Nick Hawes and Aaron Sloman and Jeremy Wyatt},
  TITLE = {Requirements \& Designs: Asking Scientific Questions About Architectures},
  BOOKTITLE = {Proceedings of AISB'06: Adaptation in Artificial and Biological Systems},
  YEAR = {2006},
  EDITOR = {Tim Kovacs and James A. R. Marshall},
  PAGES = {52-55},
  ADDRESS = {Bristol},
  MONTH = {April},
  PUBLISHER = {The Society for the Study of Artificial Intelligence and the Simulation
	of Behaviour},
  ABSTRACT = {This paper discusses our views on the future of the field of cognitive
	architectures, and how the scien- tific 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.},
  DATE-ADDED = {2009-01-05 11:51:05 +0000},
  DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
  KEYWORDS = {cosy; irlab},
  URL = {http://www.cognitivesystems.org/publications/hawesetal06gc5.pdf}
}

@INPROCEEDINGS{Hawes/etal:2006,
  AUTHOR = {Nick Hawes and Jeremy Wyatt},
  TITLE = {Towards Context-Sensitive Visual Attention},
  BOOKTITLE = {Proceedings of the Second International Cognitive Vision Workshop
	(ICVW06)},
  YEAR = {2006},
  EDITOR = {Markus Vincze and Lucas Paletta},
  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.},
  DATE-ADDED = {2009-01-05 11:50:09 +0000},
  DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
  KEYWORDS = {cosy; irlab},
  URL = {http://www.cognitivesystems.org/publications/haweswyatt06.pdf}
}

@TECHREPORT{Hawes/etal:2006a,
  AUTHOR = {Nick Hawes and Jeremy Wyatt and Aaron Sloman},
  TITLE = {An Architecture Schema for Embodied Cognitive Systems},
  INSTITUTION = {University of Birmingham, School of Computer Science},
  YEAR = {2006},
  NUMBER = {CSR-06-12},
  MONTH = {November},
  ABSTRACT = {The study of architectures to support intelligent behaviour is certainly
	the broadest, and arguably one of the most ill-defined enterprises
	in AI and Cognitive Science. In the CoSy project one of our goals
	is to develop and understand cognitive architectures suitable for
	the control of robots. This is not the same as developing an architecture
	for robot control, nor is it the same as developing a purely cognitive
	architecture unconnected to real sensors or actuators. We argue that
	work on architectures traditionally falls into two camps. First there
	are cognitive architectures which attempt to provide unified theories
	of cognition such as SOAR [Laird et al., 1987] and ACT-R [Anderson
	et al., 2004]. Their value is typically measured in terms of an ability
	to reproduce some of the characteristics of human like information
	processing. ACT-R for example is used extensively for creating models
	of cognition that are then evaluated against data from humans. In
	other words, they are evaluated as psychological theories. On the
	other hand, roboticists have been engaged with issues of how to enable
	robots to act reliably and robustly in a rapidly changing world when
	faced with limited computational power, uncertain sensing, and uncertain
	action. Architectures for robot control such as 3T [Bonasso et al.,
	1997] are therefore largely concerned issues of real- time control,
	uncertainty, sensory fusion (or the lack of it), and error recovery.
	They are evaluated in terms of the performance of the resulting robotic
	systems on a variety of tasks.
	
	 In CoSy we have interests in both cognitive science and engineering
	science, and consequently our work is related to both of the aforementioned
	camps whilst also looking at closely related issues. Our work is
	neither concerned with trying to model humans or any other specific
	type of animal, nor with trying to compete on practical design tasks.
	Rather it is concerned with trying to understand the possibilities
	and trade-offs involved in different designs in relation to different
	sets of requirements. In this paper we will describe an architecture
	schema which inherits some of the ambitions of classic cognitive
	architectures, and those of robot control architectures, whilst allowing
	us to explore these additional issues. It is important to note now
	that we don't present an empirical evaluation of an implementation
	of a scenario-specific instantiations of the architecture schema
	in this paper, we do describe two possible instantiations based on
	the CoSy demonstrator scenarios. It is also worth stating at this
	stage that our intention is not to perform extensive evaluation of
	the architecture schema against human behaviour. Instead we intend
	to evaluate it by profiling behaviour of scenario-specific tiations
	of it under varying conditions, such as internal failures and varying
	types of change in the world.},
  DATE-ADDED = {2009-01-05 11:49:03 +0000},
  DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
  EMAIL = {N.A.Hawes@cs.bham.ac.uk, J.L.Wyatt@cs.bham.ac.uk, A.Sloman@cs.bham.ac.uk},
  KEYWORDS = {cosy; irlab},
  URL = {http://www.cognitivesystems.org/publications/CSR-06-12.pdf}
}

@INBOOK{hong08a,
  PAGES = {27-46},
  TITLE = {Learning Causality and Intentional Actions},
  PUBLISHER = {Springer},
  YEAR = {2008},
  AUTHOR = {S. Hongeng and J. Wyatt},
  SERIES = {LNAI: Towards Affordance-Based Robot Control},
  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 agent'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 situ- ations,
	and present statistical learning algorithms. Using ob ject manip-
	ulation tasks, we illustrate how a system infers the agent's goals
	from visual observation and compare results with findings in psychological
	experiments.},
  URL = {http://www.cognitivesystems.org/publications/lnai07.pdf}
}

@INPROCEEDINGS{hong06,
  AUTHOR = {Somboon Hongeng and Jeremy Wyatt},
  TITLE = {Learning Causality and Intention in Human Actions},
  BOOKTITLE = {Proceedings of the 6th IEEE-RAS International Conference of Humanoid
	Robots (Humanoids'06)},
  YEAR = {2006},
  MONTH = {December},
  PUBLISHER = {IEEE},
  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 agent'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 agent's goals from visual
	observation and compare results with findings in psychological experiments.},
  DATE-ADDED = {2009-01-05 11:39:46 +0000},
  DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
  KEYWORDS = {cosy; irlab},
  URL = {http://www.cognitivesystems.org/publications/hongeng_humanoids06.pdf}
}

@INPROCEEDINGS{Kruijff/etal:2006-PIT,
  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 differentmodalities.
	The approach has been fully implemented, and is illustrated with
	several working examples.},
  DATE-ADDED = {2009-01-05 11:49:48 +0000},
  DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
  EE = {http://dx.doi.org/10.1007/11768029_12},
  KEYWORDS = {cosy; irlab},
  URL = {http://www.cognitivesystems.org/publications/kruijffetal06pit.pdf}
}

@INCOLLECTION{Leibe06b,
  AUTHOR = {B. Leibe and A. Leonardis and B. Schiele},
  TITLE = {An Implicit Shape Model for Combined Object Categorization and Segmentation},
  BOOKTITLE = {Towards Category-Level Object Recognition},
  PUBLISHER = {Springer},
  YEAR = {2006},
  EDITOR = {M. Hebert and J. Ponce and C. Schmid and A. Zisserman},
  SERIES = {LNCS},
  NOTE = {to appear},
  ABSTRACT = {We present a method for object categorization in real-world scenes.
	Following a common consensus in the field, we do not assume that
	a figure-ground segmentation is available prior to recognition. However,
	in contrast to most standard approaches for object class recognition,
	our approach automatically segments the object as a result of the
	categorization. This combination of recognition and segmentation
	into one process is made possible by our use of an Implicit Shape
	Model, which integrates both capabilities into a common probabilistic
	framework. This model can be thought of as a non-parametric approach
	which can easily handle configurations of large numbers of object
	parts. In addition to the recognition and segmentation result, it
	also generates a per-pixel confidence measure specifying the area
	that supports a hypothesis and how much it can be trusted. We use
	this confidence to derive a natural extension of the approach to
	handle multiple objects in a scene and resolve ambiguities between
	overlapping hypotheses with an MDL-based criterion. In addition,
	we present an extensive evaluation of our method on a standard dataset
	for car detection and compare its performance to existing methods
	from the literature. Our results show that the proposed method outperforms
	previously published methods while needing one order of magnitude
	less training examples. Finally, we present results for articulated
	objects, which show that the proposed method can categorize and segment
	unfamiliar objects in different articulations and with widely varying
	texture patterns, even under significant partial occlusion.}
}

@INPROCEEDINGS{Leibe06c,
  AUTHOR = {B. Leibe and K. Mikolajczyk and B. Schiele},
  TITLE = {Efficient Clustering and Matching for Object Class Recognition},
  BOOKTITLE = {British Machine Vision Conference (BMVC'06)},
  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.},
  OPTADDRESS = {Edinburgh, UK},
  OPTMONTH = {Sept.}
}

@INPROCEEDINGS{Leibe06d,
  AUTHOR = {B. Leibe and K. Mikolajczyk and B. Schiele},
  TITLE = {Segmentation Based Multi-Cue Integration for Object Detection},
  BOOKTITLE = {British Machine Vision Conference (BMVC'06)},
  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.},
  OPTADDRESS = {Edinburgh, UK},
  OPTMONTH = {Sept.}
}

@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 dierent
	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 dierent real-world environments
	demonstrate the effectiveness of our approach.},
  URL = {http://www.cognitivesystems.org/publications/mozos2006iros.pdf}
}

@INPROCEEDINGS{mozos/etal:2006,
  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.cognitivesystems.org/publications/mozos2006iros_w.pdf}
}

@INPROCEEDINGS{Mikolajczyk06c,
  AUTHOR = {K. Mikolajczyk and B. Leibe and B. Schiele},
  TITLE = {Multiple object class detection with a generative mode},
  BOOKTITLE = {Proceedings of International Conference on Computer Vision and Pattern
	Recognition 2006},
  YEAR = {2006},
  ADDRESS = {New York, USA},
  MONTH = JUN,
  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.}
}

@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://www.cognitivesystems.org/publications/PhiliponaVisNeurosci.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://www.cognitivesystems.org/publications/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},
  URL = {http://www.cognitivesystems.org/publications/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 (IROS'06)},
  YEAR = {2006},
  ADDRESS = {Beijing, China},
  MONTH = {October},
  ABSTRACT = {An important competence for a mobile robot system is the ability to
	localize and perform context interpretation. This is required to
	perform basic navigation and to facilitate local specific services.
	Usually localization is performed based on a purely geometric model.
	Through use of vision and place recognition a number of opportunities
	open up in terms of flexibility and association of semantics to the
	model. To achieve this the present paper presents an appearance based
	method for place recognition. The method is based on a large margin
	classifier in combination with a rich global image descriptor. The
	method is robust to variations in illumination and minor scene changes.
	The method is evaluated across several different cameras, changes
	in time-of-day and weather conditions. The results clearly demonstrate
	the value of the approach.},
  URL = {http://www.cognitivesystems.org/publications/pronobis06iros.pdf}
}

@INPROCEEDINGS{seemann06cvpr,
  AUTHOR = {E. Seemann and B. Leibe and B. Schiele},
  TITLE = {Multi-Aspect Detection of Articulated Objects},
  BOOKTITLE = {Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
	2005},
  YEAR = {2006},
  ADDRESS = {New York, USA},
  MONTH = JUN,
  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 [5]. 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.}
}

@INPROCEEDINGS{Seemann06,
  AUTHOR = {Edgar Seemann and Bernt Schiele},
  TITLE = {Cross-Articulation Learning for Robust Detection of Pedestrians},
  BOOKTITLE = {Proceedings of 28th Annual Symposium of the German Association for
	Pattern Recognition DAGM06},
  YEAR = {2006},
  ADDRESS = {Berlin, Germany},
  MONTH = SEP,
  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 ob
	jects, 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
	pro- pose an approach, which aims for a suitable trade-off for this
	problem. On the one hand, the approach is made more discriminant
	by explic- itly 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 pedestri- ans with different articulations.}
}

@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.},
  URL = {http://www.cognitivesystems.org/publications/skocajCVWW06.pdf}
}

@INPROCEEDINGS{Sloman:2006,
  AUTHOR = {A. Sloman},
  TITLE = {{Introduction to Symposium GC5: Architecture of Brain and Mind Integrating
	high level cognitive processes with brain mechanisms and functions
	in a working robot}},
  BOOKTITLE = {{Proceedings of the AISB '06 Adaptation in Artificial and Biological
	Systems}},
  YEAR = {2006},
  ADDRESS = {Bristol},
  MONTH = {April},
  NOTE = {http://www.cs.bham.ac.uk/research/projects/cosy/papers/\#tr0602},
  ABSTRACT = {This symposium is inspired by UKCRC Research Grand Challenge 5: GC5:
	Architecture of Brain and Mind. The aim of GC5 is to provoke unified
	discussion of long term research goals in AI, Cognitive Science,
	and related disciplines, especially goals concerned with giving computers
	a useful and general subset of human capabilities, implemented in
	a biologically inspired fashion. The symposium can also be seen as
	part of a series of related events attempting to promote a high-level
	long-term vision of achievable scientific goals of AI/Cognitive Science,
	including The DAM (Designing an Mind) Symposium at AISB'00 (Davis,
	2005), the Tutorial on Philosophical Foundations of AI at IJCAI'01
	(Sloman and Scheutz, 2001), the St. Thomas symposium in 2002 (Minsky
	et al., 2004), and the IJCAI'05 Tutorial on Learning and Representation
	in Animals and Robots (Sloman and Schiele, 2005). It presents themes
	central to the EC-funded Cognitive Systems initiative including the
	CoSy project which is part of that initiative, whose members have
	helped to organise this symposium, and the euCognition project which
	is funding this meeting. A common feature is the focus on scientific
	goals rather than useful applications though implementation of working
	systems is central to the proposed methodology. This introduction
	to the symposium provides some background and highlights some of
	the major problems to be overcome. },
  DATE-ADDED = {2009-01-04 19:55:40 +0000},
  DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
  KEYWORDS = {cosy; irlab},
  URL = {http://www.cognitivesystems.org/publications/sloman-aisb06-gc5-intro.pdf}
}

@INPROCEEDINGS{Sloman:2006b,
  AUTHOR = {Aaron Sloman},
  TITLE = {{Polyflaps as a domain for perceiving, acting and learning in a 3-D
	world}},
  BOOKTITLE = {{Position Papers for 2006 AAAI Fellows Symposium}},
  YEAR = {2006},
  ADDRESS = {Menlo Park, CA},
  PUBLISHER = {AAAI},
  NOTE = {http://www.aaai.org/Fellows/fellows.php and http://www.cognitivesystems.org/publications/Fellows16.pdf},
  ABSTRACT = {Test domains for AI can have a deep impact on research. The polyflap
	domain is proposed for testing complex AI theories about architectures,
	mechanisms and forms of representation involved in features of human
	and animal intelligence that evolved to enable perception, action,
	and learning in diverse environments containing things that we can
	perceive and manipulate, and many complex processes involving objects
	that differ in shape, materials, causal properties, and relations
	to one another. We need a test environment that is rich enough to
	provide some of that variety of structures, processes and affordances,
	yet simple enough to be within reach of robotics research in the
	not too distant future.},
  DATE-ADDED = {2009-01-04 19:55:40 +0000},
  DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
  KEYWORDS = {cosy; irlab},
  URL = {http://www.cognitivesystems.org/publications/Fellows16.pdf}
}

@INPROCEEDINGS{Sloman/etal:2006,
  AUTHOR = {Aaron Sloman and Jackie Chappell and The CoSy Team},
  TITLE = {{How an animal or robot with 3-D manipulation skills experiences
	the world}},
  BOOKTITLE = {{The tenth annual meeting of the Association for the Scientific Study
	of Consciousness, Oxford}},
  YEAR = {2006},
  ADDRESS = {Internet},
  MONTH = {June},
  PUBLISHER = {ASSC},
  NOTE = {http://www.cs.bham.ac.uk/research/projects/cosy/papers/\#pr0602,
	Poster for ASSC10, Oxford June 2006. Also at ASSC10 Eprints Archive:
	http://eprints.assc.caltech.edu/112/},
  ABSTRACT = {This presentation elaborates on
	
	 'The substratum of this experience is the mastery of a technique'
	(Wittgenstein)
	
	 I try to show, with illustrative videos, that many 'techniques' are
	implicitly involved in ordinary experiences -- and that the complexities
	grow as a child develops, extending its ontology and therefore the
	variety of affordances it can experience and use. I point out that
	there are two interpretations of sensorimotor contingencies, one
	intrasomatic (relating only the contents of sensory and motor signals
	at various levels of abstraction) the other extrasomatic (amodal,
	objective), referring to an environment that exists independently
	of whether and how it is experienced or acted on, and that the latter
	provides computational advantages in some cases, supporting a Kantian
	rather than a Humean view of knowledge and concepts. This also suggests
	a re-interpretation of mirror neurons as 'abstraction neurons'.
	
	 What we are conscious of in the environment depends on the ontology
	we have available. A child whose ontology does not include the notion
	of boundary, or the notion of alignment of boundaries may not be
	able to replace a cut-out wooden picture in its recess, even if he
	knows which recess it should go in. Careful observation of children
	at various stages shows transitions that involve extensions of the
	available ontology, which must go along with development of suitable
	forms of representation and mechanisms for manipulating them, and
	an architecture that combines them all. Thus the substratum of the
	more sophisticated child's experience is mastery of many 'techniques',
	not just one as implied by Wittgenstein (who probably did not intend
	that). It is suggested that there are considerable differences between
	precocial species whose competences and architecture are mostly genetically
	determined and altricial species that develop most of their own competences
	e.g. through playful exploration, driven by meta-level bootstrapping
	mechanisms.
	
	 Only when I started working in detail on requirements for a human-like
	robot able to manipulate 3-D objects using vision and an arm with
	gripper did I notice what should have been obvious long before, namely
	that structured objects have 'multi-strand' relationships not expressible
	simply as R(x, y), because the relation between x and y involves
	many relations between parts of x and parts of y.
	
	 For a more detailed presentation of the resulting theory see COSY-PR-0505:
	A (Possibly) New Theory of Vision (PDF)
	
	 Hence, motion of such structured objects involves 'multi-strand'
	(concurrent) processes. That is, many relationships change in parallel
	-- e.g. faces, edges, corners of one block may all be changing their
	relationships to faces edges and corners of another (and things get
	more complex when objects are flexible, e.g. your hand peeling a
	banana or a sweater being put on a child).
	
	 Thus seeing what you are doing in such cases can have a kind of complexity
	that appears not to have been noticed previously because of too much
	focus on simpler visual tasks like recognition and tracking.
	
	 I'll show why we need to postulate mechanisms in which concurrent
	processes at different levels of abstraction, in partial registration
	with the optic array (NOT the retina, since saccades, etc., occur
	frequently) are represented.
	
	 Nothing in AI comes close to modelling this, and it seems likely
	that it will be hard to explain in terms of known neural mechanisms.
	If the opportunity arises I'll try to explain some of the implications
	for human development, understanding of causation, and computational
	modelling, and spell out requirements to be addressed in future interdisciplinary
	research, explaining deep connections with Gibson's notion of affordance,
	and its generalisation to 'vicarious affordance'.
	
	 The evolution of grasping devices that move independently of eyes
	(i.e. hands instead of mouth or beak) had profound implications --
	undermining claims about sensory-motor contingencies -- also suggesting
	that mirror neurons should have been called 'abstraction neurons'.
	
	 Some of the ideas are also sketched here: COSY-DP-0601 'Orthogonal
	Competences Acquired by Altricial Species'
	
	 A critique of common assumptions about 'sensorimotor contingencies'
	is presented, including making a distinction between somatic (internal)
	and exosomatic (external) ontologies. Too many people expect too
	much to come from the somatic (intrasomatic) variety -- including
	knowledge of sensorimotor contingencies, a notion criticised in http://www.cs.bham.ac.uk/research/projects/cosy/papers/#dp0603
	
	 Requirements for 'fully deliberative' systems are analysed in http://www.cs.bham.ac.uk/research/projects/cosy/papers/#dp0604
	},
  DATE-ADDED = {2009-01-04 19:55:40 +0000},
  DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
  KEYWORDS = {cosy; irlab},
  URL = {http://www.cognitivesystems.org/publications/assc10-poster.pdf}
}

@INPROCEEDINGS{Sloman/etal:2006b,
  AUTHOR = {Aaron Sloman and Birmingham CoSy Project Team and Jackie Chappell},
  TITLE = {{Poster: Acquiring Orthogonal Recombinable Competences}},
  YEAR = {2006},
  EDITOR = {Harold Bekkering},
  ADDRESS = {Radboud University Nijmegen, NL},
  MONTH = {April},
  NOTE = {http://www.cs.bham.ac.uk/research/projects/cosy/papers/\#pr0601,
	Conference url: http://www.socsci.ru.nl/CogSys2},
  ABSTRACT = {A child or baby robot that has to manipulate 3-D objects in its environment
	would face a combinatorial explosion if all possible situations have
	to be learnt about separately. This could take evolutionary time-scales.
	
	 It is conjectured that humans and some other altricial species instead
	use innate mechanisms for decomposing situations into components
	that can be explicitly learnt about, and stored in such a way that
	the competence can be re-used in combination with other learnt competences,
	in perceiving novel situations and performing novel actions.
	
	 That includes learning about kinds of surface fragments (e.g. varieties
	of curvature and surface discontinuities), kinds of surface properties
	(e.g. texture, hardness, etc.), kinds of material (rigid, flexible
	in different ways), kinds of objects composed of materials and shapes,
	kinds of relationships, kinds of changes in relationships, kinds
	of causal connections between changes.
	
	 These need to be represented in a manner that is independent of precise
	sense-data when they are perceived, or sensorimotor contingencies,
	so that knowledge about them can be used in planning future actions,
	thinking about the past, and comparing actions using different hands,
	or hands or mouth in different positions. This implies a use of 'objective'
	representations (e.g. of 3-D structure) which can then also be used
	in perceiving 'vicarious' affordances (for others).
	
	 An implication is that mirror neurons should have been called 'abstraction
	neurons'. There are many other implications, for robotics, psychology
	and neuroscience. },
  DATE-ADDED = {2009-01-04 19:55:40 +0000},
  DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
  KEYWORDS = {cosy; irlab},
  URL = {http://www.cognitivesystems.org/publications/cogsys2-poster.pdf}
}

@INPROCEEDINGS{Sloman/etal:2006d,
  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.},
  DATE-ADDED = {2009-01-05 11:49:29 +0000},
  DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
  KEYWORDS = {cosy; irlab},
  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.cognitivesystems.org/publications/stachniss2006icra.pdf}
}


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