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2011


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Spatiotemporal mapping of rhythmic activity in the inferior convexity of the macaque prefrontal cortex

Panagiotaropoulos, T., Besserve, M., Crocker, B., Kapoor, V., Tolias, A., Panzeri, S., Logothetis, N.

41(239.15), 41st Annual Meeting of the Society for Neuroscience (Neuroscience), November 2011 (poster)

Abstract
The inferior convexity of the macaque prefrontal cortex (icPFC) is known to be involved in higher order processing of sensory information mediating stimulus selection, attention and working memory. Until now, the vast majority of electrophysiological investigations of the icPFC employed single electrode recordings. As a result, relatively little is known about the spatiotemporal structure of neuronal activity in this cortical area. Here we study in detail the spatiotemporal properties of local field potentials (LFP's) in the icPFC using multi electrode recordings during anesthesia. We computed the LFP-LFP coherence as a function of frequency for thousands of pairs of simultaneously recorded sites anterior to the arcuate and inferior to the principal sulcus. We observed two distinct peaks of coherent oscillatory activity between approximately 4-10 and 15-25 Hz. We then quantified the instantaneous phase of these frequency bands using the Hilbert transform and found robust phase gradients across recording sites. The dependency of the phase on the spatial location reflects the existence of traveling waves of electrical activity in the icPFC. The dominant axis of these traveling waves roughly followed the ventral-dorsal plane. Preliminary results show that repeated visual stimulation with a 10s movie had no dramatic effect on the spatial structure of the traveling waves. Traveling waves of electrical activity in the icPFC could reflect highly organized cortical processing in this area of prefrontal cortex.

ei

Web [BibTex]

2011


Web [BibTex]


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Evaluation and Optimization of MR-Based Attenuation Correction Methods in Combined Brain PET/MR

Mantlik, F., Hofmann, M., Bezrukov, I., Schmidt, H., Kolb, A., Beyer, T., Reimold, M., Schölkopf, B., Pichler, B.

2011(MIC18.M-96), 2011 IEEE Nuclear Science Symposium, Medical Imaging Conference (NSS-MIC), October 2011 (poster)

Abstract
Combined PET/MR provides simultaneous molecular and functional information in an anatomical context with unique soft tissue contrast. However, PET/MR does not support direct derivation of attenuation maps of objects and tissues within the measured PET field-of-view. Valid attenuation maps are required for quantitative PET imaging, specifically for scientific brain studies. Therefore, several methods have been proposed for MR-based attenuation correction (MR-AC). Last year, we performed an evaluation of different MR-AC methods, including simple MR thresholding, atlas- and machine learning-based MR-AC. CT-based AC served as gold standard reference. RoIs from 2 anatomic brain atlases with different levels of detail were used for evaluation of correction accuracy. We now extend our evaluation of different MR-AC methods by using an enlarged dataset of 23 patients from the integrated BrainPET/MR (Siemens Healthcare). Further, we analyze options for improving the MR-AC performance in terms of speed and accuracy. Finally, we assess the impact of ignoring BrainPET positioning aids during the course of MR-AC. This extended study confirms the overall prediction accuracy evaluation results of the first evaluation in a larger patient population. Removing datasets affected by metal artifacts from the Atlas-Patch database helped to improve prediction accuracy, although the size of the database was reduced by one half. Significant improvement in prediction speed can be gained at a cost of only slightly reduced accuracy, while further optimizations are still possible.

ei

Web [BibTex]

Web [BibTex]


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Atlas- and Pattern Recognition Based Attenuation Correction on Simultaneous Whole-Body PET/MR

Bezrukov, I., Schmidt, H., Mantlik, F., Schwenzer, N., Hofmann, M., Schölkopf, B., Pichler, B.

2011(MIC18.M-116), 2011 IEEE Nuclear Science Symposium, Medical Imaging Conference (NSS-MIC), October 2011 (poster)

Abstract
With the recent availability of clinical whole-body PET/MRI it is possible to evaluate and further develop MR-based attenuation correction methods using simultaneously acquired PET/MR data. We present first results for MRAC on patient data acquired on a fully integrated whole-body PET/MRI (Biograph mMR, Siemens) using our method that applies atlas registration and pattern recognition (ATPR) and compare them to the segmentation-based (SEG) method provided by the manufacturer. The ATPR method makes use of a database of previously aligned pairs of MR-CT volumes to predict attenuation values on a continuous scale. The robustness of the method in presence of MR artifacts was improved by location and size based detection. Lesion to liver and lesion to blood ratios (LLR and LBR) were compared for both methods on 29 iso-contour ROIs in 4 patients. ATPR showed >20% higher LBR and LLR for ROIs in and >7% near osseous tissue. For ROIs in soft tissue, both methods yielded similar ratios with max. differences <6% . For ROIs located within metal artifacts in the MR image, ATPR showed >190% higher LLR and LBR than SEG, where ratios <0.1 occured. For lesions in the neighborhood of artifacts, both ratios were >15% higher for ATPR. If artifacts in MR volumes caused by metal implants are not accounted for in the computation of attenuation maps, they can lead to a strong decrease of lesion to background ratios, even to disappearance of hot spots. Metal implants are likely to occur in the patient collective receiving combined PET/MR scans, of our first 10 patients, 3 had metal implants. Our method is currently able to account for artifacts in the pelvis caused by prostheses. The ability of the ATPR method to account for bone leads to a significant increase of LLR and LBR in osseous tissue, which supports our previous evaluations with combined PET/CT and PET/MR data. For lesions within soft tissue, lesion to background ratios of ATPR and SEG were comparable.

ei

Web [BibTex]

Web [BibTex]


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Retrospective blind motion correction of MR images

Loktyushin, A., Nickisch, H., Pohmann, R.

Magnetic Resonance Materials in Physics, Biology and Medicine, 24(Supplement 1):498, 28th Annual Scientific Meeting ESMRMB, October 2011 (poster)

Abstract
We present a retrospective method, which significantly reduces ghosting and blurring artifacts due to subject motion. No modifications to the sequence (as in [2, 3]), or the use of additional equipment (as in [1]) are required. Our method iteratively searches for the transformation, that applied to the lines in k-space -- yields the sparsest Laplacian filter output in the spatial domain.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Model based reconstruction for GRE EPI

Blecher, W., Pohmann, R., Schölkopf, B., Seeger, M.

Magnetic Resonance Materials in Physics, Biology and Medicine, 24(Supplement 1):493-494, 28th Annual Scientific Meeting ESMRMB, October 2011 (poster)

Abstract
Model based nonlinear image reconstruction methods for MRI [3] are at the heart of modern reconstruction techniques (e.g.compressed sensing [6]). In general, models are expressed as a matrix equation where y and u are column vectors of k-space and image data, X model matrix and e independent noise. However, solving the corresponding linear system is not tractable. Therefore fast nonlinear algorithms that minimize a function wrt.the unknown image are the method of choice: In this work a model for gradient echo EPI, is proposed that incorporates N/2 Ghost correction and correction for field inhomogeneities. In addition to reconstruction from full data, the model allows for sparse reconstruction, joint estimation of image, field-, and relaxation-map (like [5,8] for spiral imaging), and improved N/2 ghost correction.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Simultaneous multimodal imaging of patients with bronchial carcinoma in a whole body MR/PET system

Brendle, C., Sauter, A., Schmidt, H., Schraml, C., Bezrukov, I., Martirosian, P., Hetzel, J., Müller, M., Claussen, C., Schwenzer, N., Pfannenberg, C.

Magnetic Resonance Materials in Physics, Biology and Medicine, 24(Supplement 1):141, 28th annual scientific meeting of the European Society for Magnetic Resonance in Medicine and Biology (ESMRB), October 2011 (poster)

Abstract
Purpose/Introduction: Lung cancer is among the most frequent cancers (1). Exact determination of tumour extent and viability is crucial for adequate therapy guidance. [18F]-FDG-PET allows accurate staging and the evaluation of therapy response based on glucose metabolism. Diffusion weighted MRI (DWI) is another promising tool for the evaluation of tumour viability (2,3). The aim of the study was the simultaneous PET-MR acquisition in lung cancer patients and correlation of PET and MR data. Subjects and Methods: Seven patients (age 38-73 years, mean 61 years) with highly suspected or known bronchial carcinoma were examined. First, a [18F]-FDG-PET/CT was performed (injected dose: 332-380 MBq). Subsequently, patients were examined at the whole-body MR/PET (Siemens Biograph mMR). The MRI is a modified 3T Verio whole body system with a magnet bore of 60 cm (max. amplitude gradients 45 mT/m, max. slew rate 200 T/m/s). Concerning the PET, the whole-body MR/PET system comprises 56 detector cassettes with a 59.4 cm transaxial and 25.8 cm axial FoV. The following parameters for PET acquisition were applied: 2 bed positions, 6 min/bed with an average uptake time of 124 min after injection (range: 110-143 min). The attenuation correction of PET data was conducted with a segmentation-based method provided by the manufacturer. Acquired PET data were reconstructed with an iterative 3D OSEM algorithm using 3 iterations and 21 subsets, Gaussian filter of 3 mm. DWI MR images were recorded simultaneously for each bed using two b-values (0/800 s/mm2). SUVmax and ADCmin were assessed in a ROI analysis. The following ratios were calculated: SUVmax(tumor)/SUVmean(liver) and ADCmin(tumor)/ADCmean(muscle). Correlation between SUV and ADC was analyzed (Pearson’s correlation). Results: Diagnostic scans could be obtained in all patients with good tumour delineation. The spatial matching of PET and DWI data was very exact. Most tumours showed a pronounced FDG-uptake in combination with decreased ADC values. Significant correlation was found between SUV and ADC ratios (r = -0.87, p = 0.0118). Discussion/Conclusion: Simultaneous MR/PET imaging of lung cancer is feasible. The whole-body MR/PET system can provide complementary information regarding tumour viability and cellularity which could facilitate a more profound tumour characterization. Further studies have to be done to evaluate the importance of these parameters for therapy decisions and monitoring

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Support Vector Machines for finding deletions and short insertions using paired-end short reads

Grimm, D., Hagmann, J., König, D., Weigel, D., Borgwardt, KM.

International Conference on Intelligent Systems for Molecular Biology (ISMB), 2011 (poster)

ei

Web [BibTex]

Web [BibTex]


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Statistical estimation for optimization problems on graphs

Langovoy, M., Sra, S.

Empirical Inference Symposium, 2011 (poster)

ei

[BibTex]


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Transfer Learning with Copulas

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

Neural Information Processing Systems (NIPS), 2011 (poster)

ei

PDF [BibTex]

PDF [BibTex]

2004


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S-cones contribute to flicker brightness in human vision

Wehrhahn, C., Hill, NJ., Dillenburger, B.

34(174.12), 34th Annual Meeting of the Society for Neuroscience (Neuroscience), October 2004 (poster)

Abstract
In the retina of primates three cone types sensitive to short, middle and long wavelengths of light convert photons into electrical signals. Many investigators have presented evidence that, in color normal observers, the signals of cones sensitive to short wavelengths of light (S-cones) do not contribute to the perception of brightness of a colored surface when this is alternated with an achromatic reference (flicker brightness). Other studies indicate that humans do use S-cone signals when performing this task. Common to all these studies is the small number of observers, whose performance data are reported. Considerable variability in the occurrence of cone types across observers has been found, but, to our knowledge, no cone counts exist from larger populations of humans. We reinvestigated how much the S-cones contribute to flicker brightness. 76 color normal observers were tested in a simple psychophysical procedure neutral to the cone type occurence (Teufel & Wehrhahn (2000), JOSA A 17: 994 - 1006). The data show that, in the majority of our observers, S-cones provide input with a negative sign - relative to L- and M-cone contribution - in the task in question. There is indeed considerable between-subject variability such that for 20 out of 76 observers the magnitude of this input does not differ significantly from 0. Finally, we argue that the sign of S-cone contribution to flicker brightness perception by an observer cannot be used to infer the relative sign their contributions to the neuronal signals carrying the information leading to the perception of flicker brightness. We conclude that studies which use only a small number of observers may easily fail to find significant evidence for the small but significant population tendency for the S-cones to contribute to flicker brightness. Our results confirm all earlier results and reconcile their contradictory interpretations.

ei

Web [BibTex]

2004


Web [BibTex]


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Human Classification Behaviour Revisited by Machine Learning

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

7, pages: 134, (Editors: Bülthoff, H.H., H.A. Mallot, R. Ulrich and F.A. Wichmann), 7th T{\"u}bingen Perception Conference (TWK), Febuary 2004 (poster)

Abstract
We attempt to understand visual classication in humans using both psychophysical and machine learning techniques. Frontal views of human faces were used for a gender classication task. Human subjects classied the faces and their gender judgment, reaction time (RT) and condence rating (CR) were recorded for each face. RTs are longer for incorrect answers than for correct ones, high CRs are correlated with low classication errors and RTs decrease as the CRs increase. This results suggest that patterns difcult to classify need more computation by the brain than patterns easy to classify. Hyperplane learning algorithms such as Support Vector Machines (SVM), Relevance Vector Machines (RVM), Prototype learners (Prot) and K-means learners (Kmean) were used on the same classication task using the Principal Components of the texture and oweld representation of the faces. The classication performance of the learning algorithms was estimated using the face database with the true gender of the faces as labels, and also with the gender estimated by the subjects. Kmean yield a classication performance close to humans while SVM and RVM are much better. This surprising behaviour may be due to the fact that humans are trained on real faces during their lifetime while they were here tested on articial ones, while the algorithms were trained and tested on the same set of stimuli. We then correlated the human responses to the distance of the stimuli to the separating hyperplane (SH) of the learning algorithms. On the whole stimuli far from the SH are classied more accurately, faster and with higher condence than those near to the SH if we pool data across all our subjects and stimuli. We also nd three noteworthy results. First, SVMs and RVMs can learn to classify faces using the subjects' labels but perform much better when using the true labels. Second, correlating the average response of humans (classication error, RT or CR) with the distance to the SH on a face-by-face basis using Spearman's rank correlation coefcients shows that RVMs recreate human performance most closely in every respect. Third, the mean-of-class prototype, its popularity in neuroscience notwithstanding, is the least human-like classier in all cases examined.

ei

Web [BibTex]

Web [BibTex]


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m-Alternative-Forced-Choice: Improving the Efficiency of the Method of Constant Stimuli

Jäkel, F., Hill, J., Wichmann, F.

7, pages: 118, 7th T{\"u}bingen Perception Conference (TWK), February 2004 (poster)

Abstract
We explored several ways to improve the efficiency of measuring psychometric functions without resorting to adaptive procedures. a) The number m of alternatives in an m-alternative-forced-choice (m-AFC) task improves the efficiency of the method of constant stimuli. b) When alternatives are presented simultaneously on different positions on a screen rather than sequentially time can be saved and memory load for the subject can be reduced. c) A touch-screen can further help to make the experimental procedure more intuitive. We tested these ideas in the measurement of contrast sensitivity and compared them to results obtained by sequential presentation in two-interval-forced-choice (2-IFC). Qualitatively all methods (m-AFC and 2-IFC) recovered the characterictic shape of the contrast sensitivity function in three subjects. The m-AFC paradigm only took about 60% of the time of the 2-IFC task. We tried m=2,4,8 and found 4-AFC to give the best model fits and 2-AFC to have the least bias.

ei

Web [BibTex]

Web [BibTex]


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Efficient Approximations for Support Vector Classifiers

Kienzle, W., Franz, M.

7, pages: 68, 7th T{\"u}bingen Perception Conference (TWK), February 2004 (poster)

Abstract
In face detection, support vector machines (SVM) and neural networks (NN) have been shown to outperform most other classication methods. While both approaches are learning-based, there are distinct advantages and drawbacks to each method: NNs are difcult to design and train but can lead to very small and efcient classiers. In comparison, SVM model selection and training is rather straightforward, and, more importantly, guaranteed to converge to a globally optimal (in the sense of training errors) solution. Unfortunately, SVM classiers tend to have large representations which are inappropriate for time-critical image processing applications. In this work, we examine various existing and new methods for simplifying support vector decision rules. Our goal is to obtain efcient classiers (as with NNs) while keeping the numerical and statistical advantages of SVMs. For a given SVM solution, we compute a cascade of approximations with increasing complexities. Each classier is tuned so that the detection rate is near 100%. At run-time, the rst (simplest) detector is evaluated on the whole image. Then, any subsequent classier is applied only to those positions that have been classied as positive throughout all previous stages. The false positive rate at the end equals that of the last (i.e. most complex) detector. In contrast, since many image positions are discarded by lower-complexity classiers, the average computation time per patch decreases signicantly compared to the time needed for evaluating the highest-complexity classier alone.

ei

Web [BibTex]

Web [BibTex]


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Efficient Approximations for Support Vector Classiers

Kienzle, W., Franz, M.

7, pages: 68, 7th T{\"u}bingen Perception Conference (TWK), February 2004 (poster)

Abstract
In face detection, support vector machines (SVM) and neural networks (NN) have been shown to outperform most other classication methods. While both approaches are learning-based, there are distinct advantages and drawbacks to each method: NNs are difcult to design and train but can lead to very small and efcient classiers. In comparison, SVM model selection and training is rather straightforward, and, more importantly, guaranteed to converge to a globally optimal (in the sense of training errors) solution. Unfortunately, SVM classiers tend to have large representations which are inappropriate for time-critical image processing applications. In this work, we examine various existing and new methods for simplifying support vector decision rules. Our goal is to obtain efcient classiers (as with NNs) while keeping the numerical and statistical advantages of SVMs. For a given SVM solution, we compute a cascade of approximations with increasing complexities. Each classier is tuned so that the detection rate is near 100%. At run-time, the rst (simplest) detector is evaluated on the whole image. Then, any subsequent classier is applied only to those positions that have been classied as positive throughout all previous stages. The false positive rate at the end equals that of the last (i.e. most complex) detector. In contrast, since many image positions are discarded by lower-complexity classiers, the average computation time per patch decreases signicantly compared to the time needed for evaluating the highest-complexity classier alone.

ei

Web [BibTex]

Web [BibTex]


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Selective Attention to Auditory Stimuli: A Brain-Computer Interface Paradigm

Hill, N., Lal, T., Schröder, M., Hinterberger, T., Birbaumer, N., Schölkopf, B.

7, pages: 102, (Editors: Bülthoff, H.H., H.A. Mallot, R. Ulrich and F.A. Wichmann), 7th T{\"u}bingen Perception Conference (TWK), February 2004 (poster)

Abstract
During the last 20 years several paradigms for Brain Computer Interfaces have been proposed— see [1] for a recent review. They can be divided into (a) stimulus-driven paradigms, using e.g. event-related potentials or visual evoked potentials from an EEG signal, and (b) patient-driven paradigms such as those that use premotor potentials correlated with imagined action, or slow cortical potentials (e.g. [2]). Our aim is to develop a stimulus-driven paradigm that is applicable in practice to patients. Due to the unreliability of visual perception in “locked-in” patients in the later stages of disorders such as Amyotrophic Lateral Sclerosis, we concentrate on the auditory modality. Speci- cally, we look for the effects, in the EEG signal, of selective attention to one of two concurrent auditory stimulus streams, exploiting the increased activation to attended stimuli that is seen under some circumstances [3]. We present the results of our preliminary experiments on normal subjects. On each of 400 trials, two repetitive stimuli (sequences of drum-beats or other pulsed stimuli) could be heard simultaneously. The two stimuli were distinguishable from one another by their acoustic properties, by their source location (one from a speaker to the left of the subject, the other from the right), and by their differing periodicities. A visual cue preceded the stimulus by 500 msec, indicating which of the two stimuli to attend to, and the subject was instructed to count the beats in the attended stimulus stream. There were up to 6 beats of each stimulus: with equal probability on each trial, all 6 were played, or the fourth was omitted, or the fth was omitted. The 40-channel EEG signals were analyzed ofine to reconstruct which of the streams was attended on each trial. A linear Support Vector Machine [4] was trained on a random subset of the data and tested on the remainder. Results are compared from two types of pre-processing of the signal: for each stimulus stream, (a) EEG signals at the stream's beat periodicity are emphasized, or (b) EEG signals following beats are contrasted with those following missing beats. Both forms of pre-processing show promising results, i.e. that selective attention to one or the other auditory stream yields signals that are classiable signicantly above chance performance. In particular, the second pre-processing was found to be robust to reduction in the number of features used for classication (cf. [5]), helping us to eliminate noise.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Texture and Haptic Cues in Slant Discrimination: Measuring the Effect of Texture Type

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

7, pages: 165, (Editors: Bülthoff, H. H., H. A. Mallot, R. Ulrich, F. A. Wichmann), 7th T{\"u}bingen Perception Conference (TWK), February 2004 (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 inuence of each cue in such average depends on the reliability of the source of information [1,5]. In particular, Ernst and Banks (2002) formulate such combination as that of the minimum variance unbiased estimator that can be constructed from the available cues. We have observed systematic differences in slant discrimination performance of human observers when different types of textures were used as cue to slant [4]. 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. However, the results for slant discrimination obtained when combining these texture types with object motion results are difcult to reconcile with the minimum variance unbiased estimator model [3]. 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, and Landy (2002) [2] have shown that while for between-modality combination the human visual system has access to the single-cue information, for withinmodality combination (visual cues) the single-cue information is lost. This suggests a coupling between visual cues and independence between visual and haptic cues. Then, in the present study we combined the different texture types with haptic information in a slant discrimination task, to test whether in the between-modality condition these cues are combined as predicted by an unbiased, minimum variance estimator model. The measured weights for the cues were consistent with a combination rule sensitive to the reliability of the sources of information, but did not match the predictions of a statistically optimal combination.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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EEG Channel Selection for Brain Computer Interface Systems Based on Support Vector Methods

Schröder, M., Lal, T., Bogdan, M., Schölkopf, B.

7, pages: 50, (Editors: Bülthoff, H.H., H.A. Mallot, R. Ulrich and F.A. Wichmann), 7th T{\"u}bingen Perception Conference (TWK), February 2004 (poster)

Abstract
A Brain Computer Interface (BCI) system allows the direct interpretation of brain activity patterns (e.g. EEG signals) by a computer. Typical BCI applications comprise spelling aids or environmental control systems supporting paralyzed patients that have lost motor control completely. The design of an EEG based BCI system requires good answers for the problem of selecting useful features during the performance of a mental task as well as for the problem of classifying these features. For the special case of choosing appropriate EEG channels from several available channels, we propose the application of variants of the Support Vector Machine (SVM) for both problems. Although these algorithms do not rely on prior knowledge they can provide more accurate solutions than standard lter methods [1] for feature selection which usually incorporate prior knowledge about neural activity patterns during the performed mental tasks. For judging the importance of features we introduce a new relevance measure and apply it to EEG channels. Although we base the relevance measure for this purpose on the previously introduced algorithms, it does in general not depend on specic algorithms but can be derived using arbitrary combinations of feature selectors and classifiers.

ei

Web [BibTex]

Web [BibTex]


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Learning Depth

Sinz, F., Franz, MO.

pages: 69, (Editors: H.H.Bülthoff, H.A.Mallot, R.Ulrich,F.A.Wichmann), 7th T{\"u}bingen Perception Conference (TWK), February 2004 (poster)

Abstract
The depth of a point in space can be estimated by observing its image position from two different viewpoints. The classical approach to stereo vision calculates depth from the two projection equations which together form a stereocamera model. An unavoidable preparatory work for this solution is a calibration procedure, i.e., estimating the external (position and orientation) and internal (focal length, lens distortions etc.) parameters of each camera from a set of points with known spatial position and their corresponding image positions. This is normally done by iteratively linearizing the single camera models and reestimating their parameters according to the error on the known datapoints. The advantage of the classical method is the maximal usage of prior knowledge about the underlying physical processes and the explicit estimation of meaningful model parameters such as focal length or camera position in space. However, the approach neglects the nonlinear nature of the problem such that the results critically depend on the choice of the initial values for the parameters. In this study, we approach the depth estimation problem from a different point of view by applying generic machine learning algorithms to learn the mapping from image coordinates to spatial position. These algorithms do not require any domain knowledge and are able to learn nonlinear functions by mapping the inputs into a higher-dimensional space. Compared to classical calibration, machine learning methods give a direct solution to the depth estimation problem which means that the values of the stereocamera parameters cannot be extracted from the learned mapping. On the poster, we compare the performance of classical camera calibration to that of different machine learning algorithms such as kernel ridge regression, gaussian processes and support vector regression. Our results indicate that generic learning approaches can lead to higher depth accuracies than classical calibration although no domain knowledge is used.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Neural mechanisms underlying control of a Brain-Computer-Interface (BCI): Simultaneous recording of bold-response and EEG

Hinterberger, T., Wilhelm, B., Veit, R., Weiskopf, N., Lal, TN., Birbaumer, N.

2004 (poster)

Abstract
Brain computer interfaces (BCI) enable humans or animals to communicate or activate external devices without muscle activity using electric brain signals. The BCI Thought Translation Device (TTD) uses learned regulation of slow cortical potentials (SCPs), a skill most people and paralyzed patients can acquire with training periods of several hours up to months. The neurophysiological mechanisms and anatomical sources of SCPs and other event-related brain macro-potentials are well understood, but the neural mechanisms underlying learning of the self-regulation skill for BCI-use are unknown. To uncover the relevant areas of brain activation during regulation of SCPs, the TTD was combined with functional MRI and EEG was recorded inside the MRI scanner in twelve healthy participants who have learned to regulate their SCP with feedback and reinforcement. The results demonstrate activation of specific brain areas during execution of the brain regulation skill: successf! ul control of cortical positivity allowing a person to activate an external device was closely related to an increase of BOLD (blood oxygen level dependent) response in the basal ganglia and frontal premotor deactivation indicating learned regulation of a cortical-striatal loop responsible for local excitation thresholds of cortical assemblies. The data suggest that human users of a BCI learn the regulation of cortical excitation thresholds of large neuronal assemblies as a prerequisite of direct brain communication: the learning of this skill depends critically on an intact and flexible interaction between these cortico-basal ganglia-circuits. Supported by the Deutsche Forschungsgemeinschaft (DFG) and the National Institute of Health (NIH).

ei

[BibTex]

[BibTex]


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Masking by plaid patterns revisited

Wichmann, F.

Experimentelle Psychologie. Beitr{\"a}ge zur 46. Tagung experimentell arbeitender Psychologen, 46, pages: 285, 2004 (poster)

ei

[BibTex]

[BibTex]


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Early visual processing—data, theory, models

Wichmann, F.

Experimentelle Psychologie. Beitr{\"a}ge zur 46. Tagung experimentell arbeitender Psychologen, 46, pages: 24, 2004 (poster)

ei

[BibTex]

[BibTex]


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Implicit Wiener series for capturing higher-order interactions in images

Franz, M., Schölkopf, B.

Sensory coding and the natural environment, (Editors: Olshausen, B.A. and M. Lewicki), 2004 (poster)

Abstract
The information about the objects in an image is almost exclusively described by the higher-order interactions of its pixels. The Wiener series is one of the standard methods to systematically characterize these interactions. However, the classical estimation method of the Wiener expansion coefficients via cross-correlation suffers from severe problems that prevent its application to high-dimensional and strongly nonlinear signals such as images. We propose an estimation method based on regression in a reproducing kernel Hilbert space that overcomes these problems using polynomial kernels as known from Support Vector Machines and other kernel-based methods. Numerical experiments show performance advantages in terms of convergence, interpretability and system sizes that can be handled. By the time of the conference, we will be able to present first results on the higher-order structure of natural images.

ei

[BibTex]

[BibTex]


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Classification and Memory Behaviour of Man Revisited by Machine

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

CSHL Meeting on Computational & Systems Neuroscience (COSYNE), 2004 (poster)

ei

[BibTex]

[BibTex]