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2017


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Improving performance of linear field generation with multi-coil setup by optimizing coils position

Aghaeifar, A., Loktyushin, A., Eschelbach, M., Scheffler, K.

Magnetic Resonance Materials in Physics, Biology and Medicine, 30(Supplement 1):S259, 34th Annual Scientific Meeting of the European Society for Magnetic Resonance in Medicine and Biology (ESMRMB), October 2017 (poster)

ei

link (url) DOI [BibTex]

2017


link (url) DOI [BibTex]


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Estimating B0 inhomogeneities with projection FID navigator readouts

Loktyushin, A., Ehses, P., Schölkopf, B., Scheffler, K.

25th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM), April 2017 (poster)

ei

link (url) [BibTex]

link (url) [BibTex]


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Image Quality Improvement by Applying Retrospective Motion Correction on Quantitative Susceptibility Mapping and R2*

Feng, X., Loktyushin, A., Deistung, A., Reichenbach, J.

25th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM), April 2017 (poster)

ei

link (url) [BibTex]

link (url) [BibTex]


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Design of a visualization scheme for functional connectivity data of Human Brain

Bramlage, L.

Hochschule Osnabrück - University of Applied Sciences, 2017 (thesis)

sf

Bramlage_BSc_2017.pdf [BibTex]


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Generalized phase locking analysis of electrophysiology data

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

ESI Systems Neuroscience Conference (ESI-SyNC 2017): Principles of Structural and Functional Connectivity, 2017 (poster)

ei

[BibTex]

[BibTex]

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)

ei

[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)

ei

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)

ei

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)

ei

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)

ei

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)

ei pn

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)

ei

Web [BibTex]

Web [BibTex]

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]

2009


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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.

ei

Web [BibTex]

2009


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.

ei

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)

ei

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.

ei

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.

ei

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.

ei

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.

ei

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.

ei

PDF Web [BibTex]

PDF Web [BibTex]

2005


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Kernel methods for dependence testing in LFP-MUA

Gretton, A., Belitski, A., Murayama, Y., Schölkopf, B., Logothetis, N.

35(689.17), 35th Annual Meeting of the Society for Neuroscience (Neuroscience), November 2005 (poster)

Abstract
A fundamental problem in neuroscience is determining whether or not particular neural signals are dependent. The correlation is the most straightforward basis for such tests, but considerable work also focuses on the mutual information (MI), which is capable of revealing dependence of higher orders that the correlation cannot detect. That said, there are other measures of dependence that share with the MI an ability to detect dependence of any order, but which can be easier to compute in practice. We focus in particular on tests based on the functional covariance, which derive from work originally accomplished in 1959 by Renyi. Conceptually, our dependence tests work by computing the covariance between (infinite dimensional) vectors of nonlinear mappings of the observations being tested, and then determining whether this covariance is zero - we call this measure the constrained covariance (COCO). When these vectors are members of universal reproducing kernel Hilbert spaces, we can prove this covariance to be zero only when the variables being tested are independent. The greatest advantage of these tests, compared with the mutual information, is their simplicity – when comparing two signals, we need only take the largest eigenvalue (or the trace) of a product of two matrices of nonlinearities, where these matrices are generally much smaller than the number of observations (and are very simple to construct). We compare the mutual information, the COCO, and the correlation in the context of finding changes in dependence between the LFP and MUA signals in the primary visual cortex of the anaesthetized macaque, during the presentation of dynamic natural stimuli. We demonstrate that the MI and COCO reveal dependence which is not detected by the correlation alone (which we prove by artificially removing all correlation between the signals, and then testing their dependence with COCO and the MI); and that COCO and the MI give results consistent with each other on our data.

ei

Web [BibTex]

2005


Web [BibTex]


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Rapid animal detection in natural scenes: Critical features are local

Wichmann, F., Rosas, P., Gegenfurtner, K.

Journal of Vision, 5(8):376, Fifth Annual Meeting of the Vision Sciences Society (VSS), September 2005 (poster)

Abstract
Thorpe et al (Nature 381, 1996) first showed how rapidly human observers are able to classify natural images as to whether they contain an animal or not. Whilst the basic result has been replicated using different response paradigms (yes-no versus forced-choice), modalities (eye movements versus button presses) as well as while measuring neurophysiological correlates (ERPs), it is still unclear which image features support this rapid categorisation. Recently Torralba and Oliva (Network: Computation in Neural Systems, 14, 2003) suggested that simple global image statistics can be used to predict seemingly complex decisions about the absence and/or presence of objects in natural scences. They show that the information contained in a small number (N=16) of spectral principal components (SPC)—principal component analysis (PCA) applied to the normalised power spectra of the images—is sufficient to achieve approximately 80% correct animal detection in natural scenes. Our goal was to test whether human observers make use of the power spectrum when rapidly classifying natural scenes. We measured our subjects' ability to detect animals in natural scenes as a function of presentation time (13 to 167 msec); images were immediately followed by a noise mask. In one condition we used the original images, in the other images whose power spectra were equalised (each power spectrum was set to the mean power spectrum over our ensemble of 1476 images). Thresholds for 75% correct animal detection were in the region of 20–30 msec for all observers, independent of the power spectrum of the images: this result makes it very unlikely that human observers make use of the global power spectrum. Taken together with the results of Gegenfurtner, Braun & Wichmann (Journal of Vision [abstract], 2003), showing the robustness of animal detection to global phase noise, we conclude that humans use local features, like edges and contours, in rapid animal detection.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Learning an Interest Operator from Eye Movements

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

International Workshop on Bioinspired Information Processing (BIP 2005), 2005, pages: 1, September 2005 (poster)

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Classification of natural scenes using global image statistics

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

Journal of Vision, 5(8):602, Fifth Annual Meeting of the Vision Sciences Society (VSS), September 2005 (poster)

Abstract
The algorithmic classification of complex, natural scenes is generally considered a difficult task due to the large amount of information conveyed by natural images. Work by Simon Thorpe and colleagues showed that humans are capable of detecting animals within novel natural scenes with remarkable speed and accuracy. This suggests that the relevant information for classification can be extracted at comparatively limited computational cost. One hypothesis is that global image statistics such as the amplitude spectrum could underly fast image classification (Johnson & Olshausen, Journal of Vision, 2003; Torralba & Oliva, Network: Comput. Neural Syst., 2003). We used linear discriminant analysis to classify a set of 11.000 images into animal and non-animal images. After applying a DFT to the image, we put the Fourier spectrum into bins (8 orientations with 6 frequency bands each). Using all bins, classification performance on the Fourier spectrum reached 70%. However, performance was similar (67%) when only the high spatial frequency information was used and decreased steadily at lower spatial frequencies, reaching a minimum (50%) for the low spatial frequency information. Similar results were obtained when all bins were used on spatially filtered images. A detailed analysis of the classification weights showed that a relatively high level of performance (67%) could also be obtained when only 2 bins were used, namely the vertical and horizontal orientation at the highest spatial frequency band. Our results show that in the absence of sophisticated machine learning techniques, animal detection in natural scenes is limited to rather modest levels of performance, far below those of human observers. If limiting oneself to global image statistics such as the DFT then mostly information at the highest spatial frequencies is useful for the task. This is analogous to the results obtained with human observers on filtered images (Kirchner et al, VSS 2004).

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Comparative evaluation of Independent Components Analysis algorithms for isolating target-relevant information in brain-signal classification

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

Brain-Computer Interface Technology, 3, pages: 95, June 2005 (poster)

ei

PDF [BibTex]


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Classification of natural scenes using global image statistics

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

47, pages: 88, 47. Tagung Experimentell Arbeitender Psychologen, April 2005 (poster)

ei

[BibTex]

[BibTex]


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Classification of Natural Scenes using Global Image Statistics

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

8, pages: 88, 8th T{\"u}bingen Perception Conference (TWK), February 2005 (poster)

Abstract
The algorithmic classification of complex, natural scenes is generally considered a difficult task due to the large amount of information conveyed by natural images. Work by Simon Thorpe and colleagues showed that humans are capable of detecting animals within novel natural scenes with remarkable speed and accuracy. This suggests that the relevant information for classification can be extracted at comparatively limited computational cost. One hypothesis is that global image statistics such as the amplitude spectrum could underly fast image classification (Johnson & Olshausen, Journal of Vision, 2003; Torralba & Oliva, Network: Comput. Neural Syst., 2003). We used linear discriminant analysis to classify a set of 11.000 images into animal and nonanimal images. After applying a DFT to the image, we put the Fourier spectrum of each image into 48 bins (8 orientations with 6 frequency bands). Using all of these bins, classification performance on the Fourier spectrum reached 70%. In an iterative procedure, we then removed the bins whose absence caused the smallest damage to the classification performance (one bin per iteration). Notably, performance stayed at about 70% until less then 6 bins were left. A detailed analysis of the classification weights showed that a comparatively high level of performance (67%) could also be obtained when only 2 bins were used, namely the vertical orientations at the highest spatial frequency band. When using only a single frequency band (8 bins) we found that 67% classification performance could be reached when only the high spatial frequency information was used, which decreased steadily at lower spatial frequencies, reaching a minimum (50%) for the low spatial frequency information. Similar results were obtained when all bins were used on spatially pre-filtered images. Our results show that in the absence of sophisticated machine learning techniques, animal detection in natural scenes is limited to rather modest levels of performance, far below those of human observers. If limiting oneself to global image statistics such as the DFT then mostly information at the highest spatial frequencies is useful for the task. This is analogous to the results obtained with human observers on filtered images (Kirchner et al, VSS 2004).

ei

Web [BibTex]

Web [BibTex]


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Efficient Adaptive Sampling of the Psychometric Function by Maximizing Information Gain

Tanner, T., Hill, N., Rasmussen, C., Wichmann, F.

8, pages: 109, (Editors: Bülthoff, H. H., H. A. Mallot, R. Ulrich and F. A. Wichmann), 8th T{\"u}bingen Perception Conference (TWK), February 2005 (poster)

Abstract
A psychometric function can be described by its shape and four parameters: position or threshold, slope or width, false alarm rate or chance level, and miss or lapse rate. Depending on the parameters of interest some points on the psychometric function may be more informative than others. Adaptive methods attempt to place trials on the most informative points based on the data collected in previous trials. We introduce a new adaptive bayesian psychometric method which collects data for any set of parameters with high efficency. It places trials by minimizing the expected entropy [1] of the posterior pdf over a set of possible stimuli. In contrast to most other adaptive methods it is neither limited to threshold measurement nor to forced-choice designs. Nuisance parameters can be included in the estimation and lead to less biased estimates. The method supports block designs which do not harm the performance when a sufficient number of trials are performed. Block designs are useful for control of response bias and short term performance shifts such as adaptation. We present the results of evaluations of the method by computer simulations and experiments with human observers. In the simulations we investigated the role of parametric assumptions, the quality of different point estimates, the effect of dynamic termination criteria and many other settings. [1] Kontsevich, L.L. and Tyler, C.W. (1999): Bayesian adaptive estimation of psychometric slope and threshold. Vis. Res. 39 (16), 2729-2737.

ei

Web [BibTex]

Web [BibTex]


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Bayesian Inference for Psychometric Functions

Kuss, M., Jäkel, F., Wichmann, F.

8, pages: 106, (Editors: Bülthoff, H. H., H. A. Mallot, R. Ulrich and F. A. Wichmann), 8th T{\"u}bingen Perception Conference (TWK), February 2005 (poster)

Abstract
In psychophysical studies of perception the psychometric function is used to model the relation between the physical stimulus intensity and the observer's ability to detect or discriminate between stimuli of different intensities. We propose the use of Bayesian inference to extract the information contained in experimental data to learn about the parameters of psychometric functions. Since Bayesian inference cannot be performed analytically we use a Markov chain Monte Carlo method to generate samples from the posterior distribution over parameters. These samples can be used to estimate Bayesian confidence intervals and other characteristics of the posterior distribution. We compare our approach with traditional methods based on maximum-likelihood parameter estimation combined with parametric bootstrap techniques for confidence interval estimation. Experiments indicate that Bayesian inference methods are superior to bootstrap-based methods and are thus the method of choice for estimating the psychometric function and its confidence-intervals.

ei

Web [BibTex]

Web [BibTex]


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Global image statistics of natural scenes

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

Bioinspired Information Processing, 08, pages: 1, 2005 (poster)

ei

[BibTex]

[BibTex]


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Kernel-Methods, Similarity, and Exemplar Theories of Categorization

Jäkel, F., Wichmann, F.

ASIC, 4, 2005 (poster)

Abstract
Kernel-methods are popular tools in machine learning and statistics that can be implemented in a simple feed-forward neural network. They have strong connections to several psychological theories. For example, Shepard‘s universal law of generalization can be given a kernel interpretation. This leads to an inner product and a metric on the psychological space that is different from the usual Minkowski norm. The metric has psychologically interesting properties: It is bounded from above and does not have additive segments. As categorization models often rely on Shepard‘s law as a model for psychological similarity some of them can be recast as kernel-methods. In particular, ALCOVE is shown to be closely related to kernel logistic regression. The relationship to the Generalized Context Model is also discussed. It is argued that functional analysis which is routinely used in machine learning provides valuable insights also for psychology.

ei

Web [BibTex]


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Rapid animal detection in natural scenes: critical features are local

Wichmann, F., Rosas, P., Gegenfurtner, K.

Experimentelle Psychologie. Beitr{\"a}ge zur 47. Tagung experimentell arbeitender Psychologen, 47, pages: 225, 2005 (poster)

ei

[BibTex]

[BibTex]


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The human brain as large margin classifier

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

Proceedings of the Computational & Systems Neuroscience Meeting (COSYNE), 2, pages: 1, 2005 (poster)

ei

[BibTex]

[BibTex]