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2002


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Gender Classification of Human Faces

Graf, A., Wichmann, F.

In Biologically Motivated Computer Vision, pages: 1-18, (Editors: Bülthoff, H. H., S.W. Lee, T. A. Poggio and C. Wallraven), Springer, Berlin, Germany, Second International Workshop on Biologically Motivated Computer Vision (BMCV), November 2002 (inproceedings)

Abstract
This paper addresses the issue of combining pre-processing methods—dimensionality reduction using Principal Component Analysis (PCA) and Locally Linear Embedding (LLE)—with Support Vector Machine (SVM) classification for a behaviorally important task in humans: gender classification. A processed version of the MPI head database is used as stimulus set. First, summary statistics of the head database are studied. Subsequently the optimal parameters for LLE and the SVM are sought heuristically. These values are then used to compare the original face database with its processed counterpart and to assess the behavior of a SVM with respect to changes in illumination and perspective of the face images. Overall, PCA was superior in classification performance and allowed linear separability.

ei

PDF PDF DOI [BibTex]

2002


PDF PDF DOI [BibTex]


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Insect-Inspired Estimation of Self-Motion

Franz, MO., Chahl, JS.

In Biologically Motivated Computer Vision, (2525):171-180, LNCS, (Editors: Bülthoff, H.H. , S.W. Lee, T.A. Poggio, C. Wallraven), Springer, Berlin, Germany, Second International Workshop on Biologically Motivated Computer Vision (BMCV), November 2002 (inproceedings)

Abstract
The tangential neurons in the fly brain are sensitive to the typical optic flow patterns generated during self-motion. In this study, we examine whether a simplified linear model of these neurons can be used to estimate self-motion from the optic flow. We present a theory for the construction of an optimal linear estimator incorporating prior knowledge about the environment. The optimal estimator is tested on a gantry carrying an omnidirectional vision sensor. The experiments show that the proposed approach leads to accurate and robust estimates of rotation rates, whereas translation estimates turn out to be less reliable.

ei

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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Combining sensory Information to Improve Visualization

Ernst, M., Banks, M., Wichmann, F., Maloney, L., Bülthoff, H.

In Proceedings of the Conference on Visualization ‘02 (VIS ‘02), pages: 571-574, (Editors: Moorhead, R. , M. Joy), IEEE, Piscataway, NJ, USA, IEEE Conference on Visualization (VIS '02), October 2002 (inproceedings)

Abstract
Seemingly effortlessly the human brain reconstructs the three-dimensional environment surrounding us from the light pattern striking the eyes. This seems to be true across almost all viewing and lighting conditions. One important factor for this apparent easiness is the redundancy of information provided by the sensory organs. For example, perspective distortions, shading, motion parallax, or the disparity between the two eyes' images are all, at least partly, redundant signals which provide us with information about the three-dimensional layout of the visual scene. Our brain uses all these different sensory signals and combines the available information into a coherent percept. In displays visualizing data, however, the information is often highly reduced and abstracted, which may lead to an altered perception and therefore a misinterpretation of the visualized data. In this panel we will discuss mechanisms involved in the combination of sensory information and their implications for simulations using computer displays, as well as problems resulting from current display technology such as cathode-ray tubes.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Sampling Techniques for Kernel Methods

Achlioptas, D., McSherry, F., Schölkopf, B.

In Advances in neural information processing systems 14 , pages: 335-342, (Editors: TG Dietterich and S Becker and Z Ghahramani), MIT Press, Cambridge, MA, USA, 15th Annual Neural Information Processing Systems Conference (NIPS), September 2002 (inproceedings)

Abstract
We propose randomized techniques for speeding up Kernel Principal Component Analysis on three levels: sampling and quantization of the Gram matrix in training, randomized rounding in evaluating the kernel expansions, and random projections in evaluating the kernel itself. In all three cases, we give sharp bounds on the accuracy of the obtained approximations.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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The Infinite Hidden Markov Model

Beal, MJ., Ghahramani, Z., Rasmussen, CE.

In Advances in Neural Information Processing Systems 14, pages: 577-584, (Editors: Dietterich, T.G. , S. Becker, Z. Ghahramani), MIT Press, Cambridge, MA, USA, Fifteenth Annual Neural Information Processing Systems Conference (NIPS), September 2002 (inproceedings)

Abstract
We show that it is possible to extend hidden Markov models to have a countably infinite number of hidden states. By using the theory of Dirichlet processes we can implicitly integrate out the infinitely many transition parameters, leaving only three hyperparameters which can be learned from data. These three hyperparameters define a hierarchical Dirichlet process capable of capturing a rich set of transition dynamics. The three hyperparameters control the time scale of the dynamics, the sparsity of the underlying state-transition matrix, and the expected number of distinct hidden states in a finite sequence. In this framework it is also natural to allow the alphabet of emitted symbols to be infinite - consider, for example, symbols being possible words appearing in English text.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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A new discriminative kernel from probabilistic models

Tsuda, K., Kawanabe, M., Rätsch, G., Sonnenburg, S., Müller, K.

In Advances in Neural Information Processing Systems 14, pages: 977-984, (Editors: Dietterich, T.G. , S. Becker, Z. Ghahramani), MIT Press, Cambridge, MA, USA, Fifteenth Annual Neural Information Processing Systems Conference (NIPS), September 2002 (inproceedings)

Abstract
Recently, Jaakkola and Haussler proposed a method for constructing kernel functions from probabilistic models. Their so called \Fisher kernel" has been combined with discriminative classi ers such as SVM and applied successfully in e.g. DNA and protein analysis. Whereas the Fisher kernel (FK) is calculated from the marginal log-likelihood, we propose the TOP kernel derived from Tangent vectors Of Posterior log-odds. Furthermore, we develop a theoretical framework on feature extractors from probabilistic models and use it for analyzing the TOP kernel. In experiments our new discriminative TOP kernel compares favorably to the Fisher kernel.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Incorporating Invariances in Non-Linear Support Vector Machines

Chapelle, O., Schölkopf, B.

In Advances in Neural Information Processing Systems 14, pages: 609-616, (Editors: TG Dietterich and S Becker and Z Ghahramani), MIT Press, Cambridge, MA, USA, 15th Annual Neural Information Processing Systems Conference (NIPS), September 2002 (inproceedings)

Abstract
The choice of an SVM kernel corresponds to the choice of a representation of the data in a feature space and, to improve performance, it should therefore incorporate prior knowledge such as known transformation invariances. We propose a technique which extends earlier work and aims at incorporating invariances in nonlinear kernels. We show on a digit recognition task that the proposed approach is superior to the Virtual Support Vector method, which previously had been the method of choice.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Kernel feature spaces and nonlinear blind source separation

Harmeling, S., Ziehe, A., Kawanabe, M., Müller, K.

In Advances in Neural Information Processing Systems 14, pages: 761-768, (Editors: Dietterich, T. G., S. Becker, Z. Ghahramani), MIT Press, Cambridge, MA, USA, Fifteenth Annual Neural Information Processing Systems Conference (NIPS), September 2002 (inproceedings)

Abstract
In kernel based learning the data is mapped to a kernel feature space of a dimension that corresponds to the number of training data points. In practice, however, the data forms a smaller submanifold in feature space, a fact that has been used e.g. by reduced set techniques for SVMs. We propose a new mathematical construction that permits to adapt to the intrinsic dimension and to find an orthonormal basis of this submanifold. In doing so, computations get much simpler and more important our theoretical framework allows to derive elegant kernelized blind source separation (BSS) algorithms for arbitrary invertible nonlinear mixings. Experiments demonstrate the good performance and high computational efficiency of our kTDSEP algorithm for the problem of nonlinear BSS.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Algorithms for Learning Function Distinguishable Regular Languages

Fernau, H., Radl, A.

In Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, pages: 64-73, (Editors: Caelli, T. , A. Amin, R. P.W. Duin, M. Kamel, D. de Ridder), Springer, Berlin, Germany, Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, August 2002 (inproceedings)

Abstract
Function distinguishable languages were introduced as a new methodology of defining characterizable subclasses of the regular languages which are learnable from text. Here, we give details on the implementation and the analysis of the corresponding learning algorithms. We also discuss problems which might occur in practical applications.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


Thumb xl bildschirmfoto 2013 01 15 um 09.54.19
Inferring hand motion from multi-cell recordings in motor cortex using a Kalman filter

Wu, W., Black, M. J., Gao, Y., Bienenstock, E., Serruya, M., Donoghue, J. P.

In SAB’02-Workshop on Motor Control in Humans and Robots: On the Interplay of Real Brains and Artificial Devices, pages: 66-73, Edinburgh, Scotland (UK), August 2002 (inproceedings)

ps

pdf [BibTex]

pdf [BibTex]


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Decision Boundary Pattern Selection for Support Vector Machines

Shin, H., Cho, S.

In Proc. of the Korean Data Mining Conference, pages: 33-41, Korean Data Mining Conference, May 2002 (inproceedings)

ei

[BibTex]

[BibTex]


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k-NN based Pattern Selection for Support Vector Classifiers

Shin, H., Cho, S.

In Proc. of the Korean Industrial Engineers Conference, pages: 645-651, Korean Industrial Engineers Conference, May 2002 (inproceedings)

ei

[BibTex]

[BibTex]


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Microarrays: How Many Do You Need?

Zien, A., Fluck, J., Zimmer, R., Lengauer, T.

In RECOMB 2002, pages: 321-330, ACM Press, New York, NY, USA, Sixth Annual International Conference on Research in Computational Molecular Biology, April 2002 (inproceedings)

Abstract
We estimate the number of microarrays that is required in order to gain reliable results from a common type of study: the pairwise comparison of different classes of samples. Current knowlegde seems to suffice for the construction of models that are realistic with respect to searches for individual differentially expressed genes. Such models allow to investigate the dependence of the required number of samples on the relevant parameters: the biological variability of the samples within each class; the fold changes in expression; the detection sensitivity of the microarrays; and the acceptable error rates of the results. We supply experimentalists with general conclusions as well as a freely accessible Java applet at http://cartan.gmd.de/~zien/classsize/ for fine tuning simulations to their particular actualities. Since the situation can be assumed to be very similar for large scale proteomics and metabolomics studies, our methods and results might also apply there.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Nonlinear Multivariate Analysis with Geodesic Kernels

Kuss, M.

Biologische Kybernetik, Technische Universität Berlin, February 2002 (diplomathesis)

ei

GZIP [BibTex]

GZIP [BibTex]


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Pattern Selection for Support Vector Classifiers

Shin, H., Cho, S.

In Ideal 2002, pages: 97-103, (Editors: Yin, H. , N. Allinson, R. Freeman, J. Keane, S. Hubbard), Springer, Berlin, Germany, Third International Conference on Intelligent Data Engineering and Automated Learning, January 2002 (inproceedings)

Abstract
SVMs tend to take a very long time to train with a large data set. If "redundant" patterns are identified and deleted in pre-processing, the training time could be reduced significantly. We propose a k-nearest neighbors(k-NN) based pattern selection method. The method tries to select the patterns that are near the decision boundary and that are correctly labeled. The simulations over synthetic data sets showed promising results: (1) By converting a non-separable problem to a separable one, the search for an optimal error tolerance parameter became unnecessary. (2) SVM training time decreased by two orders of magnitude without any loss of accuracy. (3) The redundant SVs were substantially reduced.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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The leave-one-out kernel

Tsuda, K., Kawanabe, M.

In Artificial Neural Networks -- ICANN 2002, 2415, pages: 727-732, LNCS, (Editors: Dorronsoro, J. R.), Artificial Neural Networks -- ICANN, 2002 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


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Concentration Inequalities and Empirical Processes Theory Applied to the Analysis of Learning Algorithms

Bousquet, O.

Biologische Kybernetik, Ecole Polytechnique, 2002 (phdthesis) Accepted

Abstract
New classification algorithms based on the notion of 'margin' (e.g. Support Vector Machines, Boosting) have recently been developed. The goal of this thesis is to better understand how they work, via a study of their theoretical performance. In order to do this, a general framework for real-valued classification is proposed. In this framework, it appears that the natural tools to use are Concentration Inequalities and Empirical Processes Theory. Thanks to an adaptation of these tools, a new measure of the size of a class of functions is introduced, which can be computed from the data. This allows, on the one hand, to better understand the role of eigenvalues of the kernel matrix in Support Vector Machines, and on the other hand, to obtain empirical model selection criteria.

ei

PostScript [BibTex]


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Support Vector Machines: Induction Principle, Adaptive Tuning and Prior Knowledge

Chapelle, O.

Biologische Kybernetik, 2002 (phdthesis)

Abstract
This thesis presents a theoretical and practical study of Support Vector Machines (SVM) and related learning algorithms. In a first part, we introduce a new induction principle from which SVMs can be derived, but some new algorithms are also presented in this framework. In a second part, after studying how to estimate the generalization error of an SVM, we suggest to choose the kernel parameters of an SVM by minimizing this estimate. Several applications such as feature selection are presented. Finally the third part deals with the incoporation of prior knowledge in a learning algorithm and more specifically, we studied the case of known invariant transormations and the use of unlabeled data.

ei

GZIP [BibTex]


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Localized Rademacher Complexities

Bartlett, P., Bousquet, O., Mendelson, S.

In Proceedings of the 15th annual conference on Computational Learning Theory, pages: 44-58, Proceedings of the 15th annual conference on Computational Learning Theory, 2002 (inproceedings)

Abstract
We investigate the behaviour of global and local Rademacher averages. We present new error bounds which are based on the local averages and indicate how data-dependent local averages can be estimated without {it a priori} knowledge of the class at hand.

ei

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Film Cooling: A Comparative Study of Different Heaterfoil Configurations for Liquid Crystals Experiments

Vogel, G., Graf, ABA., Weigand, B.

In ASME TURBO EXPO 2002, Amsterdam, GT-2002-30552, ASME TURBO EXPO, Amsterdam, 2002 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


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Some Local Measures of Complexity of Convex Hulls and Generalization Bounds

Bousquet, O., Koltchinskii, V., Panchenko, D.

In Proceedings of the 15th annual conference on Computational Learning Theory, Proceedings of the 15th annual conference on Computational Learning Theory, 2002 (inproceedings)

Abstract
We investigate measures of complexity of function classes based on continuity moduli of Gaussian and Rademacher processes. For Gaussian processes, we obtain bounds on the continuity modulus on the convex hull of a function class in terms of the same quantity for the class itself. We also obtain new bounds on generalization error in terms of localized Rademacher complexities. This allows us to prove new results about generalization performance for convex hulls in terms of characteristics of the base class. As a byproduct, we obtain a simple proof of some of the known bounds on the entropy of convex hulls.

ei

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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A kernel approach for learning from almost orthogonal patterns

Schölkopf, B., Weston, J., Eskin, E., Leslie, C., Noble, W.

In Principles of Data Mining and Knowledge Discovery, Lecture Notes in Computer Science, 2430/2431, pages: 511-528, Lecture Notes in Computer Science, (Editors: T Elomaa and H Mannila and H Toivonen), Springer, Berlin, Germany, 13th European Conference on Machine Learning (ECML) and 6th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'2002), 2002 (inproceedings)

ei

PostScript DOI [BibTex]

PostScript DOI [BibTex]


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Infinite Mixtures of Gaussian Process Experts

Rasmussen, CE., Ghahramani, Z.

In (Editors: Dietterich, Thomas G.; Becker, Suzanna; Ghahramani, Zoubin), 2002 (inproceedings)

Abstract
We present an extension to the Mixture of Experts (ME) model, where the individual experts are Gaussian Process (GP) regression models. Using a input-dependent adaptation of the Dirichlet Process, we implement a gating network for an infinite number of Experts. Inference in this model may be done efficiently using a Markov Chain relying on Gibbs sampling. The model allows the effective covariance function to vary with the inputs, and may handle large datasets -- thus potentially overcoming two of the biggest hurdles with GP models. Simulations show the viability of this approach.

ei

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Marginalized kernels for RNA sequence data analysis

Kin, T., Tsuda, K., Asai, K.

In Genome Informatics 2002, pages: 112-122, (Editors: Lathtop, R. H.; Nakai, K.; Miyano, S.; Takagi, T.; Kanehisa, M.), Genome Informatics, 2002, (Best Paper Award) (inproceedings)

ei

Web [BibTex]

Web [BibTex]


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Luminance Artifacts on CRT Displays

Wichmann, F.

In IEEE Visualization, pages: 571-574, (Editors: Moorhead, R.; Gross, M.; Joy, K. I.), IEEE Visualization, 2002 (inproceedings)

Abstract
Most visualization panels today are still built around cathode-ray tubes (CRTs), certainly on personal desktops at work and at home. Whilst capable of producing pleasing images for common applications ranging from email writing to TV and DVD presentation, it is as well to note that there are a number of nonlinear transformations between input (voltage) and output (luminance) which distort the digital and/or analogue images send to a CRT. Some of them are input-independent and hence easy to fix, e.g. gamma correction, but others, such as pixel interactions, depend on the content of the input stimulus and are thus harder to compensate for. CRT-induced image distortions cause problems not only in basic vision research but also for applications where image fidelity is critical, most notably in medicine (digitization of X-ray images for diagnostic purposes) and in forms of online commerce, such as the online sale of images, where the image must be reproduced on some output device which will not have the same transfer function as the customer's CRT. I will present measurements from a number of CRTs and illustrate how some of their shortcomings may be problematic for the aforementioned applications.

ei

[BibTex]

[BibTex]


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Inferring hand motion from multi-cell recordings in motor cortex using a Kalman filter

Wu, W., Black M., Gao, Y., Bienenstock, E., Serruya, M., Donoghue, J.

Program No. 357.5. 2002 Abstract Viewer/Itinerary Planner, Society for Neuroscience, Washington, DC, 2002, Online (conference)

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abstract [BibTex]

abstract [BibTex]


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Learning rhythmic movements by demonstration using nonlinear oscillators

Ijspeert, J. A., Nakanishi, J., Schaal, S.

In IEEE International Conference on Intelligent Robots and Systems (IROS 2002), pages: 958-963, Piscataway, NJ: IEEE, Lausanne, Sept.30-Oct.4 2002, 2002, clmc (inproceedings)

Abstract
Locally weighted learning (LWL) is a class of statistical learning techniques that provides useful representations and training algorithms for learning about complex phenomena during autonomous adaptive control of robotic systems. This paper introduces several LWL algorithms that have been tested successfully in real-time learning of complex robot tasks. We discuss two major classes of LWL, memory-based LWL and purely incremental LWL that does not need to remember any data explicitly. In contrast to the traditional beliefs that LWL methods cannot work well in high-dimensional spaces, we provide new algorithms that have been tested in up to 50 dimensional learning problems. The applicability of our LWL algorithms is demonstrated in various robot learning examples, including the learning of devil-sticking, pole-balancing of a humanoid robot arm, and inverse-dynamics learning for a seven degree-of-freedom robot.

am

link (url) [BibTex]

link (url) [BibTex]


Thumb xl bildschirmfoto 2012 12 11 um 09.50.58
Automatic detection and tracking of human motion with a view-based representation

Fablet, R., Black, M. J.

In European Conf. on Computer Vision, ECCV 2002, 1, pages: 476-491, LNCS 2353, (Editors: A. Heyden and G. Sparr and M. Nielsen and P. Johansen), Springer-Verlag , 2002 (inproceedings)

Abstract
This paper proposes a solution for the automatic detection and tracking of human motion in image sequences. Due to the complexity of the human body and its motion, automatic detection of 3D human motion remains an open, and important, problem. Existing approaches for automatic detection and tracking focus on 2D cues and typically exploit object appearance (color distribution, shape) or knowledge of a static background. In contrast, we exploit 2D optical flow information which provides rich descriptive cues, while being independent of object and background appearance. To represent the optical flow patterns of people from arbitrary viewpoints, we develop a novel representation of human motion using low-dimensional spatio-temporal models that are learned using motion capture data of human subjects. In addition to human motion (the foreground) we probabilistically model the motion of generic scenes (the background); these statistical models are defined as Gibbsian fields specified from the first-order derivatives of motion observations. Detection and tracking are posed in a principled Bayesian framework which involves the computation of a posterior probability distribution over the model parameters (i.e., the location and the type of the human motion) given a sequence of optical flow observations. Particle filtering is used to represent and predict this non-Gaussian posterior distribution over time. The model parameters of samples from this distribution are related to the pose parameters of a 3D articulated model (e.g. the approximate joint angles and movement direction). Thus the approach proves suitable for initializing more complex probabilistic models of human motion. As shown by experiments on real image sequences, our method is able to detect and track people under different viewpoints with complex backgrounds.

ps

pdf [BibTex]

pdf [BibTex]


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Pressure Isotherms of Hydrogen Adsorption in Carbon Nanostructures

Chen, X., Dettlaff-Weglikowska, U., Haluska, M., Hulman, M., Roth, S., Hirscher, M., Becher, M.

In Making Functional Materials with Nanotubes, pages: Z9.11.1-Z9.11.6, Materials Research Society Symposium Proceedings, MRS, Boston [Mass.], 2002 (inproceedings)

mms

[BibTex]

[BibTex]


Thumb xl bildschirmfoto 2012 12 11 um 10.06.33
A layered motion representation with occlusion and compact spatial support

Fleet, D. J., Jepson, A., Black, M. J.

In European Conf. on Computer Vision, ECCV 2002, 1, pages: 692-706, LNCS 2353, (Editors: A. Heyden and G. Sparr and M. Nielsen and P. Johansen), Springer-Verlag , 2002 (inproceedings)

Abstract
We describe a 2.5D layered representation for visual motion analysis. The representation provides a global interpretation of image motion in terms of several spatially localized foreground regions along with a background region. Each of these regions comprises a parametric shape model and a parametric motion model. The representation also contains depth ordering so visibility and occlusion are rightly included in the estimation of the model parameters. Finally, because the number of objects, their positions, shapes and sizes, and their relative depths are all unknown, initial models are drawn from a proposal distribution, and then compared using a penalized likelihood criterion. This allows us to automatically initialize new models, and to compare different depth orderings.

ps

pdf [BibTex]

pdf [BibTex]


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Untersuchungen zur Spindynamik in nanostrukturierten ferromagnetischen Schichtsystemen

Puzic, A.

Universität Stuttgart, Stuttgart, 2002 (mastersthesis)

mms

[BibTex]

[BibTex]


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Hydrogen Storage in Carbon SWNTs: Atomic or Molecular?

Haluska, M., Hirscher, M., Becher, M., Dettlaff-Weglikowska, U., Chen, X., Roth, S.

In Structural and Electronic Properties of Molecular Nanostructures, pages: 601-605, AIP Conference Proceedings, AIP, Kirchberg, Tirol [Austria], 2002 (inproceedings)

mms

[BibTex]

[BibTex]


Thumb xl eccv2002hvg
Implicit probabilistic models of human motion for synthesis and tracking

Sidenbladh, H., Black, M. J., Sigal, L.

In European Conf. on Computer Vision, 1, pages: 784-800, 2002 (inproceedings)

Abstract
This paper addresses the problem of probabilistically modeling 3D human motion for synthesis and tracking. Given the high dimensional nature of human motion, learning an explicit probabilistic model from available training data is currently impractical. Instead we exploit methods from texture synthesis that treat images as representing an implicit empirical distribution. These methods replace the problem of representing the probability of a texture pattern with that of searching the training data for similar instances of that pattern. We extend this idea to temporal data representing 3D human motion with a large database of example motions. To make the method useful in practice, we must address the problem of efficient search in a large training set; efficiency is particularly important for tracking. Towards that end, we learn a low dimensional linear model of human motion that is used to structure the example motion database into a binary tree. An approximate probabilistic tree search method exploits the coefficients of this low-dimensional representation and runs in sub-linear time. This probabilistic tree search returns a particular sample human motion with probability approximating the true distribution of human motions in the database. This sampling method is suitable for use with particle filtering techniques and is applied to articulated 3D tracking of humans within a Bayesian framework. Successful tracking results are presented, along with examples of synthesizing human motion using the model.

ps

pdf [BibTex]

pdf [BibTex]


Thumb xl bildschirmfoto 2012 12 11 um 10.29.56
Robust parameterized component analysis: Theory and applications to 2D facial modeling

De la Torre, F., Black, M. J.

In European Conf. on Computer Vision, ECCV 2002, 4, pages: 653-669, LNCS 2353, Springer-Verlag, 2002 (inproceedings)

ps

pdf [BibTex]

pdf [BibTex]


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Magnetic Imaging of Nanostructured Systems with Transmission X-Ray Microscopy

Eimüller, T.

Bayrische Julius-Maximilians-Universität Würzburg, Würzburg, 2002 (phdthesis)

mms

[BibTex]

[BibTex]


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Ab-initio Berechnung der Spinwellenspektren von Eisen, Kobalt und Nickel

Grotheer, O.

Universität Stuttgart, Stuttgart, 2002 (phdthesis)

mms

[BibTex]

[BibTex]


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Kernspinresonanzuntersuchungen zur Diffusion von Wasserstoff in kubischen Lavesphasen

Eberle, U.

Universität Stuttgart, Stuttgart, 2002 (phdthesis)

mms

[BibTex]

[BibTex]


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Hydrogen Storage in Nanostructured Carbon Materials at Room Temperature

Chen, X., Dettlaff-Weglikowska, U., Haluska, M., Hirscher, M., Becher, M., Roth, S.

In Structural and Electronic Properties of Molecular Nanostructures, pages: 597-600, AIP Conference Proceedings, AIP, Kirchberg, Tirol [Austria], 2002 (inproceedings)

mms

[BibTex]

[BibTex]


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Movement imitation with nonlinear dynamical systems in humanoid robots

Ijspeert, J. A., Nakanishi, J., Schaal, S.

In International Conference on Robotics and Automation (ICRA2002), Washinton, May 11-15 2002, 2002, clmc (inproceedings)

Abstract
Locally weighted learning (LWL) is a class of statistical learning techniques that provides useful representations and training algorithms for learning about complex phenomena during autonomous adaptive control of robotic systems. This paper introduces several LWL algorithms that have been tested successfully in real-time learning of complex robot tasks. We discuss two major classes of LWL, memory-based LWL and purely incremental LWL that does not need to remember any data explicitly. In contrast to the traditional beliefs that LWL methods cannot work well in high-dimensional spaces, we provide new algorithms that have been tested in up to 50 dimensional learning problems. The applicability of our LWL algorithms is demonstrated in various robot learning examples, including the learning of devil-sticking, pole-balancing of a humanoid robot arm, and inverse-dynamics learning for a seven degree-of-freedom robot.

am

link (url) [BibTex]

link (url) [BibTex]


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A locally weighted learning composite adaptive controller with structure adaptation

Nakanishi, J., Farrell, J. A., Schaal, S.

In IEEE International Conference on Intelligent Robots and Systems (IROS 2002), Lausanne, Sept.30-Oct.4 2002, 2002, clmc (inproceedings)

Abstract
This paper introduces a provably stable adaptive learning controller which employs nonlinear function approximation with automatic growth of the learning network according to the nonlinearities and the working domain of the control system. The unknown function in the dynamical system is approximated by piecewise linear models using a nonparametric regression technique. Local models are allocated as necessary and their parameters are optimized on-line. Inspired by composite adaptive control methods, the pro-posed learning adaptive control algorithm uses both the tracking error and the estimation error to up-date the parameters. We provide Lyapunov analyses that demonstrate the stability properties of the learning controller. Numerical simulations illustrate rapid convergence of the tracking error and the automatic structure adaptation capability of the function approximator. This paper introduces a provably stable adaptive learning controller which employs nonlinear function approximation with automatic growth of the learning network according to the nonlinearities and the working domain of the control system. The unknown function in the dynamical system is approximated by piecewise linear models using a nonparametric regression technique. Local models are allocated as necessary and their parameters are optimized on-line. Inspired by composite adaptive control methods, the pro-posed learning adaptive control algorithm uses both the tracking error and the estimation error to up-date the parameters. We provide Lyapunov analyses that demonstrate the stability properties of the learning controller. Numerical simulations illustrate rapid convergence of the tracking error and the automatic structure adaptation capability of the function approximator

am

link (url) [BibTex]

link (url) [BibTex]


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Nanomolding based fabrication of synthetic gecko foot-hairs

Sitti, M., Fearing, R. S.

In Nanotechnology, 2002. IEEE-NANO 2002. Proceedings of the 2002 2nd IEEE Conference on, pages: 137-140, 2002 (inproceedings)

pi

Project Page [BibTex]

Project Page [BibTex]


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Micromagnetism and the microstructure of the cell walls in Sm2Co17 based permanent magnets

Goll, D., Hadjipanayis, G. C., Kronmüller, H.

In Proceedings of the 17th International Workshop on Rare-Earth Magnets and their Applications, pages: 696-703, Rinton Press, Newark, Delaware, USA, 2002 (inproceedings)

mms

[BibTex]

[BibTex]


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Ab-initio study of the influence of epitaxial strain on magnetoelastic properties

Komelj, M., Fähnle, M.

In Atomistic Aspects of Epitaxial Growth, pages: 439-447, NATO Science series: Series 2, Mathematics, Physics, and Chemistry, Kluwer Academic Publishers, Dassia, Corfu [Greece], 2002 (inproceedings)

mms

[BibTex]

[BibTex]


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Probabilistic inference of hand motion from neural activity in motor cortex

Gao, Y., Black, M. J., Bienenstock, E., Shoham, S., Donoghue, J.

In Advances in Neural Information Processing Systems 14, pages: 221-228, MIT Press, 2002 (inproceedings)

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pdf [BibTex]

pdf [BibTex]