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2013


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Coupling between spiking activity and beta band spatio-temporal patterns in the macaque PFC

Safavi, S., Panagiotaropoulos, T., Kapoor, V., Logothetis, N., Besserve, M.

43rd Annual Meeting of the Society for Neuroscience (Neuroscience), 2013 (poster)

[BibTex]

2013

[BibTex]


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Gaussian Process Vine Copulas for Multivariate Dependence

Lopez-Paz, D., Hernandez-Lobato, J., Ghahramani, Z.

International Conference on Machine Learning (ICML), 2013 (poster)

PDF [BibTex]

PDF [BibTex]


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Domain Generalization via Invariant Feature Representation

Muandet, K., Balduzzi, D., Schölkopf, B.

30th International Conference on Machine Learning (ICML2013), 2013 (poster)

PDF [BibTex]

PDF [BibTex]


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Analyzing locking of spikes to spatio-temporal patterns in the macaque prefrontal cortex

Safavi, S., Panagiotaropoulos, T., Kapoor, V., Logothetis, N., Besserve, M.

Bernstein Conference, 2013 (poster)

DOI [BibTex]

DOI [BibTex]


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One-class Support Measure Machines for Group Anomaly Detection

Muandet, K., Schölkopf, B.

29th Conference on Uncertainty in Artificial Intelligence (UAI), 2013 (poster)

PDF [BibTex]

PDF [BibTex]


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The Randomized Dependence Coefficient

Lopez-Paz, D., Hennig, P., Schölkopf, B.

Neural Information Processing Systems (NIPS), 2013 (poster)

PDF [BibTex]

PDF [BibTex]


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Characterization of different types of sharp-wave ripple signatures in the CA1 of the macaque hippocampus

Ramirez-Villegas, J., Logothetis, N., Besserve, M.

4th German Neurophysiology PhD Meeting Networks, 2013 (poster)

Web [BibTex]

Web [BibTex]

2009


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Learning Probabilistic Models via Bayesian Inverse Planning

Boularias, A., Chaib-Draa, B.

NIPS Workshop on Probabilistic Approaches for Robotics and Control, December 2009 (poster)

PDF Web [BibTex]

2009

PDF Web [BibTex]


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Bayesian Quadratic Reinforcement Learning

Hennig, P., Stern, D., Graepel, T.

NIPS Workshop on Probabilistic Approaches for Robotics and Control, December 2009 (poster)

PDF Web [BibTex]

PDF Web [BibTex]


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Policy Transfer in Apprenticeship Learning

Boularias, A., Chaib-Draa, B.

NIPS Workshop on Transfer Learning for Structured Data (TLSD-09), December 2009 (poster)

PDF Web [BibTex]

PDF Web [BibTex]


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Clinical PET/MRI-System and Its Applications with MRI Based Attenuation Correction

Kolb, A., Hofmann, M., Sossi, V., Wehrl, H., Sauter, A., Schmid, A., Schlemmer, H., Claussen, C., Pichler, B.

IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC 2009), 2009, pages: 1, October 2009 (poster)

Abstract
Clinical PET/MRI is an emerging new hybrid imaging modality. In addition to provide an unique possibility for multifunctional imaging with temporally and spatially matched data, it also provides anatomical information that can also be used for attenuation correction with no radiation exposure to the subjects. A plus of combined compared to sequential PET and MR imaging is the reduction of total scan time. Here we present our initial experience with a hybrid brain PET/MRI system. Due to the ethical approval patient scans could only be performed after a diagnostic PET/CT. We estimate that in approximately 50% of the cases PET/MRI was of superior diagnostic value compared to PET/CT and was able to provide additional information, such as DTI, spectroscopy and Time Of Flight (TOF) angiography. Here we present 3 patient cases in oncology, a retropharyngeal carcinoma in neurooncology, a relapsing meningioma and in neurology a pharyngeal carcinoma in addition to an infraction of the right hemisphere. For quantitative PET imaging attenuation correction is obligatory. In current PET/MRI setup we used our MRI based atlas method for calculating the mu-map for attenuation correction. MR-based attenuation correction accuracy was quantitatively compared to CT-based PET attenuation correction. Extensive studies to assess potential mutual interferences between PET and MR imaging modalities as well as NEMA measurements have been performed. The first patient studies as well as the phantom tests clearly demonstrated the overall good imaging performance of this first human PET/MRI system. Ongoing work concentrates on advanced normalization and reconstruction methods incorporating count-rate based algorithms.

Web [BibTex]

Web [BibTex]


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A flowering-time gene network model for association analysis in Arabidopsis thaliana

Klotzbücher, K., Kobayashi, Y., Shervashidze, N., Borgwardt, K., Weigel, D.

2009(39):95-96, German Conference on Bioinformatics (GCB '09), September 2009 (poster)

Abstract
In our project we want to determine a set of single nucleotide polymorphisms (SNPs), which have a major effect on the flowering time of Arabidopsis thaliana. Instead of performing a genome-wide association study on all SNPs in the genome of Arabidopsis thaliana, we examine the subset of SNPs from the flowering-time gene network model. We are interested in how the results of the association study vary when using only the ascertained subset of SNPs from the flowering network model, and when additionally using the information encoded by the structure of the network model. The network model is compiled from the literature by manual analysis and contains genes which have been found to affect the flowering time of Arabidopsis thaliana [Far+08; KW07]. The genes in this model are annotated with the SNPs that are located in these genes, or in near proximity to them. In a baseline comparison between the subset of SNPs from the graph and the set of all SNPs, we omit the structural information and calculate the correlation between the individual SNPs and the flowering time phenotype by use of statistical methods. Through this we can determine the subset of SNPs with the highest correlation to the flowering time. In order to further refine this subset, we include the additional information provided by the network structure by conducting a graph-based feature pre-selection. In the further course of this project we want to validate and examine the resulting set of SNPs and their corresponding genes with experimental methods.

PDF Web [BibTex]

PDF Web [BibTex]


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Initial Data from a first PET/MRI-System and its Applications in Clinical Studies Using MRI Based Attenuation Correction

Kolb, A., Hofmann, M., Sossi, V., Wehrl, H., Sauter, A., Schmid, A., Judenhofer, M., Schlemmer, H., Claussen, C., Pichler, B.

2009 World Molecular Imaging Congress, 2009, pages: 1200, September 2009 (poster)

Web [BibTex]

Web [BibTex]


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A High-Speed Object Tracker from Off-the-Shelf Components

Lampert, C., Peters, J.

First IEEE Workshop on Computer Vision for Humanoid Robots in Real Environments at ICCV 2009, 1, pages: 1, September 2009 (poster)

Abstract
We introduce RTblob, an open-source real-time vision system for 3D object detection that achieves over 200 Hz tracking speed with only off-the-shelf hardware component. It allows fast and accurate tracking of colored objects in 3D without expensive and often custom-built hardware, instead making use of the PC graphics cards for the necessary image processing operations.

PDF Web [BibTex]

PDF Web [BibTex]


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Estimating Critical Stimulus Features from Psychophysical Data: The Decision-Image Technique Applied to Human Faces

Macke, J., Wichmann, F.

Journal of Vision, 9(8):31, 9th Annual Meeting of the Vision Sciences Society (VSS), August 2009 (poster)

Abstract
One of the main challenges in the sensory sciences is to identify the stimulus features on which the sensory systems base their computations: they are a pre-requisite for computational models of perception. We describe a technique---decision-images--- for extracting critical stimulus features based on logistic regression. Rather than embedding the stimuli in noise, as is done in classification image analysis, we want to infer the important features directly from physically heterogeneous stimuli. A Decision-image not only defines the critical region-of-interest within a stimulus but is a quantitative template which defines a direction in stimulus space. Decision-images thus enable the development of predictive models, as well as the generation of optimized stimuli for subsequent psychophysical investigations. Here we describe our method and apply it to data from a human face discrimination experiment. We show that decision-images are able to predict human responses not only in terms of overall percent correct but are able to predict, for individual observers, the probabilities with which individual faces are (mis-) classified. We then test the predictions of the models using optimized stimuli. Finally, we discuss possible generalizations of the approach and its relationships with other models.

Web DOI [BibTex]

Web DOI [BibTex]


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Semi-supervised Analysis of Human fMRI Data

Shelton, JA., Blaschko, MB., Lampert, CH., Bartels, A.

Berlin Brain Computer Interface Workshop on Advances in Neurotechnology, 2009, pages: 1, July 2009 (poster)

Abstract
Kernel Canonical Correlation Analysis (KCCA) is a general technique for subspace learning that incorporates principal components analysis (PCA) and Fisher linear discriminant analysis (LDA) as special cases. By finding directions that maximize correlation, CCA learns representations tied more closely to underlying process generating the the data and can ignore high-variance noise directions. However, for data where acquisition in a given modality is expensive or otherwise limited, CCA may suffer from small sample effects. We propose to use semisupervised Laplacian regularization to utilize data that are present in only one modality. This approach is able to find highly correlated directions that also lie along the data manifold, resulting in a more robust estimate of correlated subspaces. Functional magnetic resonance imaging (fMRI) acquired data are naturally amenable to subspace techniques as data are well aligned. fMRI data of the human brain are a particularly interesting candidate. In this study we implemented various supervised and semi-supervised versions of CCA on human fMRI data, with regression to single and multivariate labels (corresponding to video content subjects viewed during the image acquisition). In each variate condition, the semi-supervised variants of CCA performed better than the supervised variants, including a supervised variant with Laplacian regularization. We additionally analyze the weights learned by the regression in order to infer brain regions that are important to different types of visual processing.

PDF Web [BibTex]

PDF Web [BibTex]


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Optimization of k-Space Trajectories by Bayesian Experimental Design

Seeger, M., Nickisch, H., Pohmann, R., Schölkopf, B.

17(2627), 17th Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM), April 2009 (poster)

Abstract
MR image reconstruction from undersampled k-space can be improved by nonlinear denoising estimators since they incorporate statistical prior knowledge about image sparsity. Reconstruction quality depends crucially on the undersampling design (k-space trajectory), in a manner complicated by the nonlinear and signal-dependent characteristics of these methods. We propose an algorithm to assess and optimize k-space trajectories for sparse MRI reconstruction, based on Bayesian experimental design, which is scaled up to full MR images by a novel variational relaxation to iteratively reweighted FFT or gridding computations. Designs are built sequentially by adding phase encodes predicted to be most informative, given the combination of previous measurements with image prior information.

PDF Web [BibTex]

PDF Web [BibTex]


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MR-Based Attenuation Correction for PET/MR

Hofmann, M., Steinke, F., Bezrukov, I., Kolb, A., Aschoff, P., Lichy, M., Erb, M., Nägele, T., Brady, M., Schölkopf, B., Pichler, B.

17(260), 17th Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM), April 2009 (poster)

Abstract
There has recently been a growing interest in combining PET and MR. Attenuation correction (AC), which accounts for radiation attenuation properties of the tissue, is mandatory for quantitative PET. In the case of PET/MR the attenuation map needs to be determined from the MR image. This is intrinsically difficult as MR intensities are not related to the electron density information of the attenuation map. Using ultra-short echo (UTE) acquisition, atlas registration and machine learning, we present methods that allow prediction of the attenuation map based on the MR image both for brain and whole body imaging.

PDF Web [BibTex]

PDF Web [BibTex]

2006


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Some observations on the pedestal effect or dipper function

Henning, B., Wichmann, F.

Journal of Vision, 6(13):50, 2006 Fall Vision Meeting of the Optical Society of America, December 2006 (poster)

Abstract
The pedestal effect is the large improvement in the detectabilty of a sinusoidal “signal” grating observed when the signal is added to a masking or “pedestal” grating of the same spatial frequency, orientation, and phase. We measured the pedestal effect in both broadband and notched noise - noise from which a 1.5-octave band centred on the signal frequency had been removed. Although the pedestal effect persists in broadband noise, it almost disappears in the notched noise. Furthermore, the pedestal effect is substantial when either high- or low-pass masking noise is used. We conclude that the pedestal effect in the absence of notched noise results principally from the use of information derived from channels with peak sensitivities at spatial frequencies different from that of the signal and pedestal. The spatial-frequency components of the notched noise above and below the spatial frequency of the signal and pedestal prevent the use of information about changes in contrast carried in channels tuned to spatial frequencies that are very much different from that of the signal and pedestal. Thus the pedestal or dipper effect measured without notched noise is not a characteristic of individual spatial-frequency tuned channels.

Web DOI [BibTex]

2006

Web DOI [BibTex]


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Optimizing Spatial Filters for BCI: Margin- and Evidence-Maximization Approaches

Farquhar, J., Hill, N., Schölkopf, B.

Challenging Brain-Computer Interfaces: MAIA Workshop 2006, pages: 1, November 2006 (poster)

Abstract
We present easy-to-use alternatives to the often-used two-stage Common Spatial Pattern + classifier approach for spatial filtering and classification of Event-Related Desychnronization signals in BCI. We report two algorithms that aim to optimize the spatial filters according to a criterion more directly related to the ability of the algorithms to generalize to unseen data. Both are based upon the idea of treating the spatial filter coefficients as hyperparameters of a kernel or covariance function. We then optimize these hyper-parameters directly along side the normal classifier parameters with respect to our chosen learning objective function. The two objectives considered are margin maximization as used in Support-Vector Machines and the evidence maximization framework used in Gaussian Processes. Our experiments assessed generalization error as a function of the number of training points used, on 9 BCI competition data sets and 5 offline motor imagery data sets measured in Tubingen. Both our approaches sho w consistent improvements relative to the commonly used CSP+linear classifier combination. Strikingly, the improvement is most significant in the higher noise cases, when either few trails are used for training, or with the most poorly performing subjects. This a reversal of the usual "rich get richer" effect in the development of CSP extensions, which tend to perform best when the signal is strong enough to accurately find their additional parameters. This makes our approach particularly suitable for clinical application where high levels of noise are to be expected.

PDF PDF [BibTex]

PDF PDF [BibTex]


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Learning Eye Movements

Kienzle, W., Wichmann, F., Schölkopf, B., Franz, M.

Sensory Coding And The Natural Environment, 2006, pages: 1, September 2006 (poster)

Abstract
The human visual system samples images through saccadic eye movements which rapidly change the point of fixation. Although the selection of eye movement targets depends on numerous top-down mechanisms, a number of recent studies have shown that low-level image features such as local contrast or edges play an important role. These studies typically used predefined image features which were afterwards experimentally verified. Here, we follow a complementary approach: instead of testing a set of candidate image features, we infer these hypotheses from the data, using methods from statistical learning. To this end, we train a non-linear classifier on fixated vs. randomly selected image patches without making any physiological assumptions. The resulting classifier can be essentially characterized by a nonlinear combination of two center-surround receptive fields. We find that the prediction performance of this simple model on our eye movement data is indistinguishable from the physiologically motivated model of Itti & Koch (2000) which is far more complex. In particular, we obtain a comparable performance without using any multi-scale representations, long-range interactions or oriented image features.

Web [BibTex]

Web [BibTex]


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Classification of natural scenes: Critical features revisited

Drewes, J., Wichmann, F., Gegenfurtner, K.

Journal of Vision, 6(6):561, 6th Annual Meeting of the Vision Sciences Society (VSS), June 2006 (poster)

Abstract
Human observers are capable of detecting animals within novel natural scenes with remarkable speed and accuracy. Despite the seeming complexity of such decisions it has been hypothesized that a simple global image feature, the relative abundance of high spatial frequencies at certain orientations, could underly such fast image classification (A. Torralba & A. Oliva, Network: Comput. Neural Syst., 2003). We successfully used linear discriminant analysis to classify a set of 11.000 images into “animal” and “non-animal” images based on their individual amplitude spectra only (Drewes, Wichmann, Gegenfurtner VSS 2005). We proceeded to sort the images based on the performance of our classifier, retaining only the best and worst classified 400 images (“best animals”, “best distractors” and “worst animals”, “worst distractors”). We used a Go/No-go paradigm to evaluate human performance on this subset of our images. Both reaction time and proportion of correctly classified images showed a significant effect of classification difficulty. Images more easily classified by our algorithm were also classified faster and better by humans, as predicted by the Torralba & Oliva hypothesis. We then equated the amplitude spectra of the 400 images, which, by design, reduced algorithmic performance to chance whereas human performance was only slightly reduced (cf. Wichmann, Rosas, Gegenfurtner, VSS 2005). Most importantly, the same images as before were still classified better and faster, suggesting that even in the original condition features other than specifics of the amplitude spectrum made particular images easy to classify, clearly at odds with the Torralba & Oliva hypothesis.

Web DOI [BibTex]

Web DOI [BibTex]


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The pedestal effect is caused by off-frequency looking, not nonlinear transduction or contrast gain-control

Wichmann, F., Henning, B.

Journal of Vision, 6(6):194, 6th Annual Meeting of the Vision Sciences Society (VSS), June 2006 (poster)

Abstract
The pedestal or dipper effect is the large improvement in the detectabilty of a sinusoidal grating observed when the signal is added to a pedestal or masking grating having the signal‘s spatial frequency, orientation, and phase. The effect is largest with pedestal contrasts just above the ‘threshold‘ in the absence of a pedestal. We measured the pedestal effect in both broadband and notched masking noise---noise from which a 1.5- octave band centered on the signal and pedestal frequency had been removed. The pedestal effect persists in broadband noise, but almost disappears with notched noise. The spatial-frequency components of the notched noise that lie above and below the spatial frequency of the signal and pedestal prevent the use of information about changes in contrast carried in channels tuned to spatial frequencies that are very much different from that of the signal and pedestal. We conclude that the pedestal effect in the absence of notched noise results principally from the use of information derived from channels with peak sensitivities at spatial frequencies that are different from that of the signal and pedestal. Thus the pedestal or dipper effect is not a characteristic of individual spatial-frequency tuned channels.

Web DOI [BibTex]

Web DOI [BibTex]


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The Pedestal Effect is Caused by Off-Frequency Looking, not Nonlinear Transduction or Contrast Gain-Control

Wichmann, F., Henning, G.

9, pages: 174, 9th T{\"u}bingen Perception Conference (TWK), March 2006 (poster)

Abstract
The pedestal or dipper effect is the large improvement in the detectability of a sinusoidal grating observed when the signal is added to a pedestal or masking grating having the signal‘s spatial frequency, orientation, and phase. The effect is largest with pedestal contrasts just above the ‘threshold’ in the absence of a pedestal. We measured the pedestal effect in both broadband and notched masking noise---noise from which a 1.5-octave band centered on the signal and pedestal frequency had been removed. The pedestal effect persists in broadband noise, but almost disappears with notched noise. The spatial-frequency components of the notched noise that lie above and below the spatial frequency of the signal and pedestal prevent the use of information about changes in contrast carried in channels tuned to spatial frequencies that are very much different from that of the signal and pedestal. We conclude that the pedestal effect in the absence of notched noise results principally from the use of information derived from channels with peak sensitivities at spatial frequencies that are different from that of the signal and pedestal. Thus the pedestal or dipper effect is not a characteristic of individual spatial-frequency tuned channels.

Web [BibTex]

Web [BibTex]


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Efficient tests for the deconvolution hypothesis

Langovoy, M.

Workshop on Statistical Inverse Problems, March 2006 (poster)

Web [BibTex]

Web [BibTex]


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Classification of Natural Scenes: Critical Features Revisited

Drewes, J., Wichmann, F., Gegenfurtner, K.

9, pages: 92, 9th T{\"u}bingen Perception Conference (TWK), March 2006 (poster)

Abstract
Human observers are capable of detecting animals within novel natural scenes with remarkable speed and accuracy. Despite the seeming complexity of such decisions it has been hypothesized that a simple global image feature, the relative abundance of high spatial frequencies at certain orientations, could underly such fast image classification [1]. We successfully used linear discriminant analysis to classify a set of 11.000 images into “animal” and “non-animal” images based on their individual amplitude spectra only [2]. We proceeded to sort the images based on the performance of our classifier, retaining only the best and worst classified 400 images ("best animals", "best distractors" and "worst animals", "worst distractors"). We used a Go/No-go paradigm to evaluate human performance on this subset of our images. Both reaction time and proportion of correctly classified images showed a significant effect of classification difficulty. Images more easily classified by our algorithm were also classified faster and better by humans, as predicted by the Torralba & Oliva hypothesis. We then equated the amplitude spectra of the 400 images, which, by design, reduced algorithmic performance to chance whereas human performance was only slightly reduced [3]. Most importantly, the same images as before were still classified better and faster, suggesting that even in the original condition features other than specifics of the amplitude spectrum made particular images easy to classify, clearly at odds with the Torralba & Oliva hypothesis.

Web [BibTex]

Web [BibTex]


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Factorial Coding of Natural Images: How Effective are Linear Models in Removing Higher-Order Dependencies?

Bethge, M.

9, pages: 90, 9th T{\"u}bingen Perception Conference (TWK), March 2006 (poster)

Abstract
The performance of unsupervised learning models for natural images is evaluated quantitatively by means of information theory. We estimate the gain in statistical independence (the multi-information reduction) achieved with independent component analysis (ICA), principal component analysis (PCA), zero-phase whitening, and predictive coding. Predictive coding is translated into the transform coding framework, where it can be characterized by the constraint of a triangular filter matrix. A randomly sampled whitening basis and the Haar wavelet are included into the comparison as well. The comparison of all these methods is carried out for different patch sizes, ranging from 2x2 to 16x16 pixels. In spite of large differences in the shape of the basis functions, we find only small differences in the multi-information between all decorrelation transforms (5% or less) for all patch sizes. Among the second-order methods, PCA is optimal for small patch sizes and predictive coding performs best for large patch sizes. The extra gain achieved with ICA is always less than 2%. In conclusion, the `edge filters‘ found with ICA lead only to a surprisingly small improvement in terms of its actual objective.

Web [BibTex]


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Classification of natural scenes: critical features revisited

Drewes, J., Wichmann, F., Gegenfurtner, K.

Experimentelle Psychologie: Beitr{\"a}ge zur 48. Tagung experimentell arbeitender Psychologen, 48, pages: 251, 2006 (poster)

[BibTex]

[BibTex]


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Texture and haptic cues in slant discrimination: combination is sensitive to reliability but not statistically optimal

Rosas, P., Wagemans, J., Ernst, M., Wichmann, F.

Beitr{\"a}ge zur 48. Tagung experimentell arbeitender Psychologen (TeaP 2006), 48, pages: 80, 2006 (poster)

[BibTex]

[BibTex]


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Ähnlichkeitsmasse in Modellen zur Kategorienbildung

Jäkel, F., Wichmann, F.

Experimentelle Psychologie: Beitr{\"a}ge zur 48. Tagung experimentell arbeitender Psychologen, 48, pages: 223, 2006 (poster)

[BibTex]

[BibTex]


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The pedestal effect is caused by off-frequency looking, not nonlinear transduction or contrast gain-control

Wichmann, F., Henning, B.

Experimentelle Psychologie: Beitr{\"a}ge zur 48. Tagung experimentell arbeitender Psychologen, 48, pages: 205, 2006 (poster)

[BibTex]

[BibTex]

2003


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Natural Actor-Critic

Peters, J., Vijayakumar, S., Schaal, S.

NIPS Workshop " Planning for the Real World: The promises and challenges of dealing with uncertainty", December 2003 (poster)

PDF Web [BibTex]

2003

PDF Web [BibTex]


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Texture and haptic cues in slant discrimination: Measuring the effect of texture type on cue combination

Rosas, P., Wichmann, F., Ernst, M., Wagemans, J.

Journal of Vision, 3(12):26, 2003 Fall Vision Meeting of the Optical Society of America, December 2003 (poster)

Abstract
In a number of models of depth cue combination the depth percept is constructed via a weighted average combination of independent depth estimations. The influence of each cue in such average depends on the reliability of the source of information. (Young, Landy, & Maloney, 1993; Ernst & Banks, 2002.) In particular, Ernst & Banks (2002) formulate the combination performed by the human brain as that of the minimum variance unbiased estimator that can be constructed from the available cues. Using slant discrimination and slant judgment via probe adjustment as tasks, we have observed systematic differences in performance of human observers when a number of different types of textures were used as cue to slant (Rosas, Wichmann & Wagemans, 2003). If the depth percept behaves as described above, our measurements of the slopes of the psychometric functions provide the predicted weights for the texture cue for the ranked texture types. We have combined these texture types with object motion but the obtained results are difficult to reconcile with the unbiased minimum variance estimator model (Rosas & Wagemans, 2003). This apparent failure of such model might be explained by the existence of a coupling of texture and motion, violating the assumption of independence of cues. Hillis, Ernst, Banks, & Landy (2002) have shown that while for between-modality combination the human visual system has access to the single-cue information, for within-modality combination (visual cues: disparity and texture) the single-cue information is lost, suggesting a coupling between these cues. Then, in the present study we combine the different texture types with haptic information in a slant discrimination task, to test whether in the between-modality condition the texture cue and the haptic cue to slant are combined as predicted by an unbiased, minimum variance estimator model.

Web DOI [BibTex]

Web DOI [BibTex]


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A unifying computational framework for optimization and dynamic systemsapproaches to motor control

Mohajerian, P., Peters, J., Ijspeert, A., Schaal, S.

10th Joint Symposium on Neural Computation (JSNC 2003), 10, pages: 1, May 2003 (poster)

PDF Web [BibTex]

PDF Web [BibTex]


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A Unifying Computational Framework for Optimization and Dynamic Systems Approaches to Motor Control

Mohajerian, P., Peters, J., Ijspeert, A., Schaal, S.

13th Annual Neural Control of Movement Meeting 2003, 13, pages: 1, April 2003 (poster)

[BibTex]

[BibTex]


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Phase Information and the Recognition of Natural Images

Braun, D., Wichmann, F., Gegenfurtner, K.

6, pages: 138, (Editors: H.H. Bülthoff, K.R. Gegenfurtner, H.A. Mallot, R. Ulrich, F.A. Wichmann), 6. T{\"u}binger Wahrnehmungskonferenz (TWK), February 2003 (poster)

Abstract
Fourier phase plays an important role in determining image structure. For example, when the phase spectrum of an image showing a ower is swapped with the phase spectrum of an image showing a tank, then we will usually perceive a tank in the resulting image, even though the amplitude spectrum is still that of the ower. Also, when the phases of an image are randomly swapped across frequencies, the resulting image becomes impossible to recognize. Our goal was to evaluate the e ect of phase manipulations in a more quantitative manner. On each trial subjects viewed two images of natural scenes. The subject had to indicate which one of the two images contained an animal. The spectra of the images were manipulated by adding random phase noise at each frequency. The phase noise was uniformly distributed in the interval [;+], where  was varied between 0 degree and 180 degrees. Image pairs were displayed for 100 msec. Subjects were remarkably resistant to the addition of phase noise. Even with [120; 120] degree noise, subjects still were at a level of 75% correct. The introduction of phase noise leads to a reduction of image contrast. Subjects were slightly better than a simple prediction based on this contrast reduction. However, when contrast response functions were measured in the same experimental paradigm, we found that performance in the phase noise experiment was signi cantly lower than that predicted by the corresponding contrast reduction.

Web [BibTex]

Web [BibTex]


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Constraints measures and reproduction of style in robot imitation learning

Bakir, GH., Ilg, W., Franz, MO., Giese, M.

6, pages: 70, (Editors: H.H. Bülthoff, K.R. Gegenfurtner, H.A. Mallot, R. Ulrich, F.A. Wichmann), 6. T{\"u}binger Wahrnehmungskonferenz (TWK), February 2003 (poster)

Abstract
Imitation learning is frequently discussed as a method for generating complex behaviors in robots by imitating human actors. The kinematic and the dynamic properties of humans and robots are typically quite di erent, however. For this reason observed human trajectories cannot be directly transferred to robots, even if their geometry is humanoid. Instead the human trajectory must be approximated by trajectories that can be realized by the robot. During this approximation deviations from the human trajectory may arise that change the style of the executed movement. Alternatively, the style of the movement might be well reproduced, but the imitated trajectory might be suboptimal with respect to di erent constraint measures from robotics control, leading to non-robust behavior. Goal of the presented work is to quantify this trade-o between \imitation quality" and constraint compatibility for the imitation of complex writing movements. In our experiment, we used trajectory data from human writing movements (see the abstract of Ilg et al. in this volume). The human trajectories were mapped onto robot trajectories by minimizing an error measure that integrates constraints that are important for the imitation of movement style and a regularizing constraint that ensures smooth joint trajectories with low velocities. In a rst experiment, both the end-e ector position and the shoulder angle of the robot were optimized in order to achieve good imitation together with accurate control of the end-e ector position. In a second experiment only the end-e ector trajectory was imitated whereas the motion of the elbow joint was determined using the optimal inverse kinematic solution for the robot. For both conditions di erent constraint measures (dexterity and relative jointlimit distances) and a measure for imitation quality were assessed. By controling the weight of the regularization term we can vary continuously between robot behavior optimizing imitation quality, and behavior minimizing joint velocities.

PDF Web [BibTex]

PDF Web [BibTex]


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Study of Human Classification using Psychophysics and Machine Learning

Graf, A., Wichmann, F., Bülthoff, H., Schölkopf, B.

6, pages: 149, (Editors: H.H. Bülthoff, K.R. Gegenfurtner, H.A. Mallot, R. Ulrich, F.A. Wichmann), 6. T{\"u}binger Wahrnehmungskonferenz (TWK), Febuary 2003 (poster)

Abstract
We attempt to reach a better understanding of classi cation in humans using both psychophysical and machine learning techniques. In our psychophysical paradigm the stimuli presented to the human subjects are modi ed using machine learning algorithms according to their responses. Frontal views of human faces taken from a processed version of the MPI face database are employed for a gender classi cation task. The processing assures that all heads have same mean intensity, same pixel-surface area and are centered. This processing stage is followed by a smoothing of the database in order to eliminate, as much as possible, scanning artifacts. Principal Component Analysis is used to obtain a low-dimensional representation of the faces in the database. A subject is asked to classify the faces and experimental parameters such as class (i.e. female/male), con dence ratings and reaction times are recorded. A mean classi cation error of 14.5% is measured and, on average, 0.5 males are classi ed as females and 21.3females as males. The mean reaction time for the correctly classi ed faces is 1229 +- 252 [ms] whereas the incorrectly classi ed faces have a mean reaction time of 1769 +- 304 [ms] showing that the reaction times increase with the subject's classi- cation error. Reaction times are also shown to decrease with increasing con dence, both for the correct and incorrect classi cations. Classi cation errors, reaction times and con dence ratings are then correlated to concepts of machine learning such as separating hyperplane obtained when considering Support Vector Machines, Relevance Vector Machines, boosted Prototype and K-means Learners. Elements near the separating hyperplane are found to be classi ed with more errors than those away from it. In addition, the subject's con dence increases when moving away from the hyperplane. A preliminary analysis on the available small number of subjects indicates that K-means classi cation seems to re ect the subject's classi cation behavior best. The above learnersare then used to generate \special" elements, or representations, of the low-dimensional database according to the labels given by the subject. A memory experiment follows where the representations are shown together with faces seen or unseen during the classi cation experiment. This experiment aims to assess the representations by investigating whether some representations, or special elements, are classi ed as \seen before" despite that they never appeared in the classi cation experiment, possibly hinting at their use during human classi cation.

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PDF Web [BibTex]


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A Representation of Complex Movement Sequences Based on Hierarchical Spatio-Temporal Correspondence for Imitation Learning in Robotics

Ilg, W., Bakir, GH., Franz, MO., Giese, M.

6, pages: 74, (Editors: H.H. Bülthoff, K.R. Gegenfurtner, H.A. Mallot, R. Ulrich, F.A. Wichmann), 6. T{\"u}binger Wahrnehmungskonferenz (TWK), February 2003 (poster)

Abstract
Imitation learning of complex movements has become a popular topic in neuroscience, as well as in robotics. A number of conceptual as well as practical problems are still unsolved. One example is the determination of the aspects of movements which are relevant for imitation. Problems concerning the movement representation are twofold: (1) The movement characteristics of observed movements have to be transferred from the perceptual level to the level of generated actions. (2) Continuous spaces of movements with variable styles have to be approximated based on a limited number of learned example sequences. Therefore, one has to use representation with a high generalisation capability. We present methods for the representation of complex movement sequences that addresses these questions in the context of the imitation learning of writing movements using a robot arm with human-like geometry. For the transfer of complex movements from perception to action we exploit a learning-based method that represents complex action sequences by linear combination of prototypical examples (Ilg and Giese, BMCV 2002). The method of hierarchical spatio-temporal morphable models (HSTMM) decomposes action sequences automatically into movement primitives. These primitives are modeled by linear combinations of a small number of learned example trajectories. The learned spatio-temporal models are suitable for the analysis and synthesis of long action sequences, which consist of movement primitives with varying style parameters. The proposed method is illustrated by imitation learning of complex writing movements. Human trajectories were recorded using a commercial motion capture system (VICON). In the rst step the recorded writing sequences are decomposed into movement primitives. These movement primitives can be analyzed and changed in style by de ning linear combinations of prototypes with di erent linear weight combinations. Our system can imitate writing movements of di erent actors, synthesize new writing styles and can even exaggerate the writing movements of individual actors. Words and writing movements of the robot look very natural, and closely match the natural styles. These preliminary results makes the proposed method promising for further applications in learning-based robotics. In this poster we focus on the acquisition of the movement representation (identi cation and segmentation of movement primitives, generation of new writing styles by spatio-temporal morphing). The transfer of the generated writing movements to the robot considering the given kinematic and dynamic constraints is discussed in Bakir et al (this volume).

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PDF Web [BibTex]


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Models of contrast transfer as a function of presentation time and spatial frequency.

Wichmann, F.

2003 (poster)

Abstract
Understanding contrast transduction is essential for understanding spatial vision. Using standard 2AFC contrast discrimination experiments conducted using a carefully calibrated display we previously showed that the shape of the threshold versus (pedestal) contrast (TvC) curve changes with presentation time and the performance level defined as threshold (Wichmann, 1999; Wichmann & Henning, 1999). Additional experiments looked at the change of the TvC curve with spatial frequency (Bird, Henning & Wichmann, 2002), and at how to constrain the parameters of models of contrast processing (Wichmann, 2002). Here I report modelling results both across spatial frequency and presentation time. An extensive model-selection exploration was performed using Bayesian confidence regions for the fitted parameters as well as cross-validation methods. Bird, C.M., G.B. Henning and F.A. Wichmann (2002). Contrast discrimination with sinusoidal gratings of different spatial frequency. Journal of the Optical Society of America A, 19, 1267-1273. Wichmann, F.A. (1999). Some aspects of modelling human spatial vision: contrast discrimination. Unpublished doctoral dissertation, The University of Oxford. Wichmann, F.A. & Henning, G.B. (1999). Implications of the Pedestal Effect for Models of Contrast-Processing and Gain-Control. OSA Annual Meeting Program, 62. Wichmann, F.A. (2002). Modelling Contrast Transfer in Spatial Vision [Abstract]. Journal of Vision, 2, 7a.

[BibTex]

2000


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Contrast discrimination using periodic pulse trains

Wichmann, F., Henning, G.

pages: 74, 3. T{\"u}binger Wahrnehmungskonferenz (TWK), February 2000 (poster)

Abstract
Understanding contrast transduction is essential for understanding spatial vision. Previous research (Wichmann et al. 1998; Wichmann, 1999; Henning and Wichmann, 1999) has demonstrated the importance of high contrasts to distinguish between alternative models of contrast discrimination. However, the modulation transfer function of the eye imposes large contrast losses on stimuli, particularly for stimuli of high spatial frequency, making high retinal contrasts difficult to obtain using sinusoidal gratings. Standard 2AFC contrast discrimination experiments were conducted using periodic pulse trains as stimuli. Given our Mitsubishi display we achieve stimuli with up to 160% contrast at the fundamental frequency. The shape of the threshold versus (pedestal) contrast (TvC) curve using pulse trains shows the characteristic dipper shape, i.e. contrast discrimination is sometimes “easier” than detection. The rising part of the TvC function has the same slope as that measured for contrast discrimination using sinusoidal gratings of the same frequency as the fundamental. Periodic pulse trains offer the possibility to explore the visual system’s properties using high retinal contrasts. Thus they might prove useful in tasks other than contrast discrimination. Second, at least for high spatial frequencies (8 c/deg) it appears that contrast discrimination using sinusoids and periodic pulse trains results in virtually identical TvC functions, indicating a lack of probability summation. Further implications of these results are discussed.

Web [BibTex]

2000

Web [BibTex]


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Subliminale Darbietung verkehrsrelevanter Information in Kraftfahrzeugen

Staedtgen, M., Hahn, S., Franz, MO., Spitzer, M.

pages: 98, (Editors: H.H. Bülthoff, K.R. Gegenfurtner, H.A. Mallot), 3. T{\"u}binger Wahrnehmungskonferenz (TWK), February 2000 (poster)

Abstract
Durch moderne Bildverarbeitungstechnologien ist es m{\"o}glich, in Kraftfahrzeugen bestimmte kritische Verkehrssituationen automatisch zu erkennen und den Fahrer zu warnen bzw. zu informieren. Ein Problem ist dabei die Darbietung der Ergebnisse, die den Fahrer m{\"o}glichst wenig belasten und seine Aufmerksamkeit nicht durch zus{\"a}tzliche Warnleuchten oder akustische Signale vom Verkehrsgeschehen ablenken soll. In einer Reihe von Experimenten wurde deshalb untersucht, ob subliminal dargebotene, das heißt nicht bewußt wahrgenommene, verkehrsrelevante Informationen verhaltenswirksam werden und zur Informations{\"u}bermittlung an den Fahrer genutzt werden k{\"o}nnen. In einem Experiment zur semantischen Bahnung konnte mit Hilfe einer lexikalischen Entscheidungsaufgabe gezeigt werden, daß auf den Straßenverkehr bezogene Worte schneller verarbeitet werden, wenn vorher ein damit in Zusammenhang stehendes Bild eines Verkehrsschildes subliminal pr{\"a}sentiert wurde. Auch bei parafovealer Darbietung der subliminalen Stimuli wurde eine Beschleunigung erzielt. In einer visuellen Suchaufgabe wurden in Bildern realer Verkehrssituationen Verkehrszeichen schneller entdeckt, wenn das Bild des Verkehrszeichens vorher subliminal dargeboten wurde. In beiden Experimenten betrug die Pr{\"a}sentationszeit f{\"u}r die Hinweisreize 17 ms, zus{\"a}tzlich wurde durch Vorw{\"a}rts- und R{\"u}ckw{\"a}rtsmaskierung die bewußteWahrnehmung verhindert. Diese Laboruntersuchungen zeigten, daß sich auch im Kontext des Straßenverkehrs Beschleunigungen der Informationsverarbeitung durch subliminal dargebotene Stimuli erreichen lassen. In einem dritten Experiment wurde die Darbietung eines subliminalen Hinweisreizes auf die Reaktionszeit beim Bremsen in einem realen Fahrversuch untersucht. Die Versuchspersonen (n=17) sollten so schnell wie m{\"o}glich bremsen, wenn die Bremsleuchten eines im Abstand von 12-15 m voran fahrenden Fahrzeuges aufleuchteten. In 50 von insgesamt 100 Durchg{\"a}ngen wurde ein subliminaler Stimulus (zwei rote Punkte mit einem Zentimeter Durchmesser und zehn Zentimeter Abstand) 150 ms vor Aufleuchten der Bremslichter pr{\"a}sentiert. Die Darbietung erfolgte durch ein im Auto an Stelle des Tachometers integriertes TFT-LCD Display. Im Vergleich zur Reaktion ohne subliminalen Stimulus verk{\"u}rzte sich die Reaktionszeit dadurch signifikant um 51 ms. In den beschriebenen Experimenten konnte gezeigt werden, daß die subliminale Darbietung verkehrsrelevanter Information auch in Kraftfahrzeugen verhaltenswirksam werden kann. In Zukunft k{\"o}nnte durch die Kombination der online-Bildverarbeitung im Kraftfahrzeug mit subliminaler Darbietung der Ergebnisse eine Erh{\"o}hung der Verkehrssicherheit und des Komforts erreicht werden.

Web [BibTex]

Web [BibTex]