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1999


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Some Aspects of Modelling Human Spatial Vision: Contrast Discrimination

Wichmann, F.

University of Oxford, University of Oxford, October 1999 (phdthesis)

[BibTex]

1999

[BibTex]


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Engineering Support Vector Machine Kernels That Recognize Translation Initiation Sites in DNA

Zien, A., Rätsch, G., Mika, S., Schölkopf, B., Lemmen, C., Smola, A., Lengauer, T., Müller, K.

In German Conference on Bioinformatics (GCB 1999), October 1999 (inproceedings)

Abstract
In order to extract protein sequences from nucleotide sequences, it is an important step to recognize points from which regions encoding pro­ teins start, the so­called translation initiation sites (TIS). This can be modeled as a classification prob­ lem. We demonstrate the power of support vector machines (SVMs) for this task, and show how to suc­ cessfully incorporate biological prior knowledge by engineering an appropriate kernel function.

Web [BibTex]

Web [BibTex]


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Unexpected and anticipated pain: identification of specific brain activations by correlation with reference functions derived form conditioning theory

Ploghaus, A., Clare, S., Wichmann, F., Tracey, I.

29, 29th Annual Meeting of the Society for Neuroscience (Neuroscience), October 1999 (poster)

[BibTex]

[BibTex]


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Lernen mit Kernen: Support-Vektor-Methoden zur Analyse hochdimensionaler Daten

Schölkopf, B., Müller, K., Smola, A.

Informatik - Forschung und Entwicklung, 14(3):154-163, September 1999 (article)

Abstract
We describe recent developments and results of statistical learning theory. In the framework of learning from examples, two factors control generalization ability: explaining the training data by a learning machine of a suitable complexity. We describe kernel algorithms in feature spaces as elegant and efficient methods of realizing such machines. Examples thereof are Support Vector Machines (SVM) and Kernel PCA (Principal Component Analysis). More important than any individual example of a kernel algorithm, however, is the insight that any algorithm that can be cast in terms of dot products can be generalized to a nonlinear setting using kernels. Finally, we illustrate the significance of kernel algorithms by briefly describing industrial and academic applications, including ones where we obtained benchmark record results.

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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Input space versus feature space in kernel-based methods

Schölkopf, B., Mika, S., Burges, C., Knirsch, P., Müller, K., Rätsch, G., Smola, A.

IEEE Transactions On Neural Networks, 10(5):1000-1017, September 1999 (article)

Abstract
This paper collects some ideas targeted at advancing our understanding of the feature spaces associated with support vector (SV) kernel functions. We first discuss the geometry of feature space. In particular, we review what is known about the shape of the image of input space under the feature space map, and how this influences the capacity of SV methods. Following this, we describe how the metric governing the intrinsic geometry of the mapped surface can be computed in terms of the kernel, using the example of the class of inhomogeneous polynomial kernels, which are often used in SV pattern recognition. We then discuss the connection between feature space and input space by dealing with the question of how one can, given some vector in feature space, find a preimage (exact or approximate) in input space. We describe algorithms to tackle this issue, and show their utility in two applications of kernel methods. First, we use it to reduce the computational complexity of SV decision functions; second, we combine it with the kernel PCA algorithm, thereby constructing a nonlinear statistical denoising technique which is shown to perform well on real-world data.

Web DOI [BibTex]

Web DOI [BibTex]


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p73 and p63 are homotetramers capable of weak heterotypic interactions with each other but not with p53.

Davison, T., Vagner, C., Kaghad, M., Ayed, A., Caput, D., CH, ..

Journal of Biological Chemistry, 274(26):18709-18714, June 1999 (article)

Abstract
Mutations in the p53 tumor suppressor gene are the most frequent genetic alterations found in human cancers. Recent identification of two human homologues of p53 has raised the prospect of functional interactions between family members via a conserved oligomerization domain. Here we report in vitro and in vivo analysis of homo- and hetero-oligomerization of p53 and its homologues, p63 and p73. The oligomerization domains of p63 and p73 can independently fold into stable homotetramers, as previously observed for p53. However, the oligomerization domain of p53 does not associate with that of either p73 or p63, even when p53 is in 15-fold excess. On the other hand, the oligomerization domains of p63 and p73 are able to weakly associate with one another in vitro. In vivo co-transfection assays of the ability of p53 and its homologues to activate reporter genes showed that a DNA-binding mutant of p53 was not able to act in a dominant negative manner over wild-type p73 or p63 but that a p73 mutant could inhibit the activity of wild-type p63. These data suggest that mutant p53 in cancer cells will not interact with endogenous or exogenous p63 or p73 via their respective oligomerization domains. It also establishes that the multiple isoforms of p63 as well as those of p73 are capable of interacting via their common oligomerization domain.

Web [BibTex]

Web [BibTex]


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Shrinking the tube: a new support vector regression algorithm

Schölkopf, B., Bartlett, P., Smola, A., Williamson, R.

In Advances in Neural Information Processing Systems 11, pages: 330-336 , (Editors: MS Kearns and SA Solla and DA Cohn), MIT Press, Cambridge, MA, USA, 12th Annual Conference on Neural Information Processing Systems (NIPS), June 1999 (inproceedings)

PDF Web [BibTex]

PDF Web [BibTex]


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Semiparametric support vector and linear programming machines

Smola, A., Friess, T., Schölkopf, B.

In Advances in Neural Information Processing Systems 11, pages: 585-591 , (Editors: MS Kearns and SA Solla and DA Cohn), MIT Press, Cambridge, MA, USA, Twelfth Annual Conference on Neural Information Processing Systems (NIPS), June 1999 (inproceedings)

Abstract
Semiparametric models are useful tools in the case where domain knowledge exists about the function to be estimated or emphasis is put onto understandability of the model. We extend two learning algorithms - Support Vector machines and Linear Programming machines to this case and give experimental results for SV machines.

PDF Web [BibTex]

PDF Web [BibTex]


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Kernel PCA and De-noising in feature spaces

Mika, S., Schölkopf, B., Smola, A., Müller, K., Scholz, M., Rätsch, G.

In Advances in Neural Information Processing Systems 11, pages: 536-542 , (Editors: MS Kearns and SA Solla and DA Cohn), MIT Press, Cambridge, MA, USA, 12th Annual Conference on Neural Information Processing Systems (NIPS), June 1999 (inproceedings)

Abstract
Kernel PCA as a nonlinear feature extractor has proven powerful as a preprocessing step for classification algorithms. But it can also be considered as a natural generalization of linear principal component analysis. This gives rise to the question how to use nonlinear features for data compression, reconstruction, and de-noising, applications common in linear PCA. This is a nontrivial task, as the results provided by kernel PCA live in some high dimensional feature space and need not have pre-images in input space. This work presents ideas for finding approximate pre-images, focusing on Gaussian kernels, and shows experimental results using these pre-images in data reconstruction and de-noising on toy examples as well as on real world data.

PDF Web [BibTex]

PDF Web [BibTex]


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Kernel principal component analysis.

Schölkopf, B., Smola, A., Müller, K.

In Advances in Kernel Methods—Support Vector Learning, pages: 327-352, (Editors: B Schölkopf and CJC Burges and AJ Smola), MIT Press, Cambridge, MA, 1999 (inbook)

[BibTex]

[BibTex]


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Estimating the support of a high-dimensional distribution

Schölkopf, B., Platt, J., Shawe-Taylor, J., Smola, A., Williamson, R.

(MSR-TR-99-87), Microsoft Research, 1999 (techreport)

Web [BibTex]

Web [BibTex]


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Single-class Support Vector Machines

Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J.

Dagstuhl-Seminar on Unsupervised Learning, pages: 19-20, (Editors: J. Buhmann, W. Maass, H. Ritter and N. Tishby), 1999 (poster)

[BibTex]

[BibTex]


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Spatial Learning and Localization in Animals: A Computational Model and Its Implications for Mobile Robots

Balakrishnan, K., Bousquet, O., Honavar, V.

Adaptive Behavior, 7(2):173-216, 1999 (article)

[BibTex]


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SVMs for Histogram Based Image Classification

Chapelle, O., Haffner, P., Vapnik, V.

IEEE Transactions on Neural Networks, (9), 1999 (article)

Abstract
Traditional classification approaches generalize poorly on image classification tasks, because of the high dimensionality of the feature space. This paper shows that Support Vector Machines (SVM) can generalize well on difficult image classification problems where the only features are high dimensional histograms. Heavy-tailed RBF kernels of the form $K(mathbf{x},mathbf{y})=e^{-rhosum_i |x_i^a-y_i^a|^{b}}$ with $aleq 1$ and $b leq 2$ are evaluated on the classification of images extracted from the Corel Stock Photo Collection and shown to far outperform traditional polynomial or Gaussian RBF kernels. Moreover, we observed that a simple remapping of the input $x_i rightarrow x_i^a$ improves the performance of linear SVMs to such an extend that it makes them, for this problem, a valid alternative to RBF kernels.

GZIP [BibTex]

GZIP [BibTex]


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Classifying LEP data with support vector algorithms.

Vannerem, P., Müller, K., Smola, A., Schölkopf, B., Söldner-Rembold, S.

In Artificial Intelligence in High Energy Nuclear Physics 99, Artificial Intelligence in High Energy Nuclear Physics 99, 1999 (inproceedings)

[BibTex]

[BibTex]


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Generalization Bounds via Eigenvalues of the Gram matrix

Schölkopf, B., Shawe-Taylor, J., Smola, A., Williamson, R.

(99-035), NeuroCOLT, 1999 (techreport)

[BibTex]

[BibTex]


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Pedestal effects with periodic pulse trains

Henning, G., Wichmann, F.

Perception, 28, pages: S137, 1999 (poster)

Abstract
It is important to know for theoretical reasons how performance varies with stimulus contrast. But, for objects on CRT displays, retinal contrast is limited by the linear range of the display and the modulation transfer function of the eye. For example, with an 8 c/deg sinusoidal grating at 90% contrast, the contrast of the retinal image is barely 45%; more retinal contrast is required, however, to discriminate among theories of contrast discrimination (Wichmann, Henning and Ploghaus, 1998). The stimulus with the greatest contrast at any spatial-frequency component is a periodic pulse train which has 200% contrast at every harmonic. Such a waveform cannot, of course, be produced; the best we can do with our Mitsubishi display provides a contrast of 150% at an 8-c/deg fundamental thus producing a retinal image with about 75% contrast. The penalty of using this stimulus is that the 2nd harmonic of the retinal image also has high contrast (with an emmetropic eye, more than 60% of the contrast of the 8-c/deg fundamental ) and the mean luminance is not large (24.5 cd/m2 on our display). We have used standard 2-AFC experiments to measure the detectability of an 8-c/deg pulse train against the background of an identical pulse train of different contrasts. An unusually large improvement in detetectability was measured, the pedestal effect or "dipper," and the dipper was unusually broad. The implications of these results will be discussed.

[BibTex]

[BibTex]


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Apprentissage Automatique et Simplicite

Bousquet, O.

Biologische Kybernetik, 1999, In french (diplomathesis)

PostScript [BibTex]

PostScript [BibTex]


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Classification on proximity data with LP-machines

Graepel, T., Herbrich, R., Schölkopf, B., Smola, A., Bartlett, P., Müller, K., Obermayer, K., Williamson, R.

In Artificial Neural Networks, 1999. ICANN 99, 470, pages: 304-309, Conference Publications , IEEE, 9th International Conference on Artificial Neural Networks, 1999 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Kernel-dependent support vector error bounds

Schölkopf, B., Shawe-Taylor, J., Smola, A., Williamson, R.

In Artificial Neural Networks, 1999. ICANN 99, 470, pages: 103-108 , Conference Publications , IEEE, 9th International Conference on Artificial Neural Networks, 1999 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Linear programs for automatic accuracy control in regression

Smola, A., Schölkopf, B., Rätsch, G.

In Artificial Neural Networks, 1999. ICANN 99, 470, pages: 575-580 , Conference Publications , IEEE, 9th International Conference on Artificial Neural Networks, 1999 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Regularized principal manifolds.

Smola, A., Williamson, R., Mika, S., Schölkopf, B.

In Lecture Notes in Artificial Intelligence, Vol. 1572, 1572, pages: 214-229 , Lecture Notes in Artificial Intelligence, (Editors: P Fischer and H-U Simon), Springer, Berlin, Germany, Computational Learning Theory: 4th European Conference, 1999 (inproceedings)

[BibTex]

[BibTex]


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Entropy numbers, operators and support vector kernels.

Williamson, R., Smola, A., Schölkopf, B.

In Lecture Notes in Artificial Intelligence, Vol. 1572, 1572, pages: 285-299, Lecture Notes in Artificial Intelligence, (Editors: P Fischer and H-U Simon), Springer, Berlin, Germany, Computational Learning Theory: 4th European Conference, 1999 (inproceedings)

[BibTex]

[BibTex]


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Sparse kernel feature analysis

Smola, A., Mangasarian, O., Schölkopf, B.

(99-04), Data Mining Institute, 1999, 24th Annual Conference of Gesellschaft f{\"u}r Klassifikation, University of Passau (techreport)

PostScript [BibTex]

PostScript [BibTex]


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Machine Learning and Language Acquisition: A Model of Child’s Learning of Turkish Morphophonology

Altun, Y.

Middle East Technical University, Ankara, Turkey, 1999 (mastersthesis)

[BibTex]


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Is the Hippocampus a Kalman Filter?

Bousquet, O., Balakrishnan, K., Honavar, V.

In Proceedings of the Pacific Symposium on Biocomputing, 3, pages: 619-630, Proceedings of the Pacific Symposium on Biocomputing, 1999 (inproceedings)

[BibTex]

[BibTex]


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A Comparison of Artificial Neural Networks and Cluster Analysis for Typing Biometrics Authentication

Maisuria, K., Ong, CS., Lai, .

In unknown, pages: 9999-9999, International Joint Conference on Neural Networks, 1999 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Implications of the pedestal effect for models of contrast-processing and gain-control

Wichmann, F., Henning, G.

OSA Conference Program, pages: 62, 1999 (poster)

Abstract
Understanding contrast processing is essential for understanding spatial vision. Pedestal contrast systematically affects slopes of functions relating 2-AFC contrast discrimination performance to pedestal contrast. The slopes provide crucial information because only full sets of data allow discrimination among contrast-processing and gain-control models. Issues surrounding Weber's law will also be discussed.

[BibTex]


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Entropy numbers, operators and support vector kernels.

Williamson, R., Smola, A., Schölkopf, B.

In Advances in Kernel Methods - Support Vector Learning, pages: 127-144, (Editors: B Schölkopf and CJC Burges and AJ Smola), MIT Press, Cambridge, MA, 1999 (inbook)

[BibTex]

[BibTex]


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Advances in Kernel Methods - Support Vector Learning

Schölkopf, B., Burges, C., Smola, A.

MIT Press, Cambridge, MA, 1999 (book)

[BibTex]

[BibTex]


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Fisher discriminant analysis with kernels

Mika, S., Rätsch, G., Weston, J., Schölkopf, B., Müller, K.

In Proceedings of the 1999 IEEE Signal Processing Society Workshop, 9, pages: 41-48, (Editors: Y-H Hu and J Larsen and E Wilson and S Douglas), IEEE, Neural Networks for Signal Processing IX, 1999 (inproceedings)

DOI [BibTex]

DOI [BibTex]

1998


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Book Review: An Introduction to Fuzzy Logic for Practical Applications

Peters, J.

K{\"u}nstliche Intelligenz (KI), 98(4):60-60, November 1998 (article)

[BibTex]

1998

[BibTex]


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Navigation mit Schnappschüssen

Franz, M., Schölkopf, B., Mallot, H., Bülthoff, H., Zell, A.

In Mustererkennung 1998, pages: 421-428, (Editors: P Levi and R-J Ahlers and F May and M Schanz), Springer, Berlin, Germany, 20th DAGM-Symposium, October 1998 (inproceedings)

Abstract
Es wird ein biologisch inspirierter Algorithmus vorgestellt, mit dem sich ein Ort wiederfinden l{\"a}sst, an dem vorher eine 360-Grad-Ansicht der Umgebung aufgenommen wurde. Die Zielrichtung wird aus der Verschiebung der Bildposition der umgebenden Landmarken im Vergleich zum Schnappschuss berechnet. Die Konvergenzeigenschaften des Algorithmus werden mathematisch untersucht und auf mobilen Robotern getestet.

PDF Web [BibTex]

PDF Web [BibTex]


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Where did I take that snapshot? Scene-based homing by image matching

Franz, M., Schölkopf, B., Bülthoff, H.

Biological Cybernetics, 79(3):191-202, October 1998 (article)

Abstract
In homing tasks, the goal is often not marked by visible objects but must be inferred from the spatial relation to the visual cues in the surrounding scene. The exact computation of the goal direction would require knowledge about the distances to visible landmarks, information, which is not directly available to passive vision systems. However, if prior assumptions about typical distance distributions are used, a snapshot taken at the goal suffices to compute the goal direction from the current view. We show that most existing approaches to scene-based homing implicitly assume an isotropic landmark distribution. As an alternative, we propose a homing scheme that uses parameterized displacement fields. These are obtained from an approximation that incorporates prior knowledge about perspective distortions of the visual environment. A mathematical analysis proves that both approximations do not prevent the schemes from approaching the goal with arbitrary accuracy, but lead to different errors in the computed goal direction. Mobile robot experiments are used to test the theoretical predictions and to demonstrate the practical feasibility of the new approach.

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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On a Kernel-Based Method for Pattern Recognition, Regression, Approximation, and Operator Inversion

Smola, A., Schölkopf, B.

Algorithmica, 22(1-2):211-231, September 1998 (article)

Abstract
We present a kernel-based framework for pattern recognition, regression estimation, function approximation, and multiple operator inversion. Adopting a regularization-theoretic framework, the above are formulated as constrained optimization problems. Previous approaches such as ridge regression, support vector methods, and regularization networks are included as special cases. We show connections between the cost function and some properties up to now believed to apply to support vector machines only. For appropriately chosen cost functions, the optimal solution of all the problems described above can be found by solving a simple quadratic programming problem.

PDF DOI [BibTex]


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The moon tilt illusion

Schölkopf, B.

Perception, 27(10):1229-1232, August 1998 (article)

Abstract
Besides the familiar moon illusion [eg Hershenson, 1989 The Moon illusion (Hillsdale, NJ: Lawrence Erlbaum Associates)], wherein the moon appears bigger when it is close to the horizon, there is a less known illusion which causes the moon‘s illuminated side to appear turned away from the direction of the sun. An experiment documenting the effect is described, and a possible explanation is put forward.

Web DOI [BibTex]

Web DOI [BibTex]


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Characterization of the oligomerization defects of two p53 mutants found in families with Li-Fraumeni and Li-Fraumeni-like syndrome.

Davison, T., Yin, P., Nie, E., Kay, C., CH, ..

Oncogene, 17(5):651-656, August 1998 (article)

Abstract
Recently two germline mutations in the oligomerization domain of p53 have been identified in patients with Li-Fraumeni and Li-Fraumeni-like Syndromes. We have used biophysical and biochemical methods to characterize these two mutants in order to better understand their functional defects and the role of the p53 oligomerization domain (residues 325-355) in oncogenesis. We find that residues 310-360 of the L344P mutant are monomeric, apparently unfolded and cannot interact with wild-type (WT) p53. The full length L344P protein is unable to bind sequence specifically to DNA and is therefore an inactive, but not a dominant negative mutant. R337C, on the other hand, can form dimers and tetramers, can hetero-oligomerize with WTp53 and can bind to a p53 consensus element. However, the thermal stability of R337C is much lower than that of WTp53 and at physiological temperatures more than half of this mutant is less than tetrameric. Thus, the R337C mutant retains some functional activity yet leads to a predisposition to cancer, suggesting that even partial inactivation of p53 oligomerization is sufficient for accelerated tumour progression.

Web [BibTex]


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Nonlinear Component Analysis as a Kernel Eigenvalue Problem

Schölkopf, B., Smola, A., Müller, K.

Neural Computation, 10(5):1299-1319, July 1998 (article)

Abstract
A new method for performing a nonlinear form of principal component analysis is proposed. By the use of integral operator kernel functions, one can efficiently compute principal components in high-dimensional feature spaces, related to input space by some nonlinear map—for instance, the space of all possible five-pixel products in 16 × 16 images. We give the derivation of the method and present experimental results on polynomial feature extraction for pattern recognition.

Web DOI [BibTex]

Web DOI [BibTex]


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SVMs — a practical consequence of learning theory

Schölkopf, B.

IEEE Intelligent Systems and their Applications, 13(4):18-21, July 1998 (article)

Abstract
My first exposure to Support Vector Machines came this spring when heard Sue Dumais present impressive results on text categorization using this analysis technique. This issue's collection of essays should help familiarize our readers with this interesting new racehorse in the Machine Learning stable. Bernhard Scholkopf, in an introductory overview, points out that a particular advantage of SVMs over other learning algorithms is that it can be analyzed theoretically using concepts from computational learning theory, and at the same time can achieve good performance when applied to real problems. Examples of these real-world applications are provided by Sue Dumais, who describes the aforementioned text-categorization problem, yielding the best results to date on the Reuters collection, and Edgar Osuna, who presents strong results on application to face detection. Our fourth author, John Platt, gives us a practical guide and a new technique for implementing the algorithm efficiently.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Support vector machines

Hearst, M., Dumais, S., Osman, E., Platt, J., Schölkopf, B.

IEEE Intelligent Systems and their Applications, 13(4):18-28, July 1998 (article)

Abstract
My first exposure to Support Vector Machines came this spring when heard Sue Dumais present impressive results on text categorization using this analysis technique. This issue's collection of essays should help familiarize our readers with this interesting new racehorse in the Machine Learning stable. Bernhard Scholkopf, in an introductory overview, points out that a particular advantage of SVMs over other learning algorithms is that it can be analyzed theoretically using concepts from computational learning theory, and at the same time can achieve good performance when applied to real problems. Examples of these real-world applications are provided by Sue Dumais, who describes the aforementioned text-categorization problem, yielding the best results to date on the Reuters collection, and Edgar Osuna, who presents strong results on application to face detection. Our fourth author, John Platt, gives us a practical guide and a new technique for implementing the algorithm efficiently.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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The connection between regularization operators and support vector kernels.

Smola, A., Schölkopf, B., Müller, K.

Neural Networks, 11(4):637-649, June 1998 (article)

Abstract
n this paper a correspondence is derived between regularization operators used in regularization networks and support vector kernels. We prove that the Green‘s Functions associated with regularization operators are suitable support vector kernels with equivalent regularization properties. Moreover, the paper provides an analysis of currently used support vector kernels in the view of regularization theory and corresponding operators associated with the classes of both polynomial kernels and translation invariant kernels. The latter are also analyzed on periodical domains. As a by-product we show that a large number of radial basis functions, namely conditionally positive definite functions, may be used as support vector kernels.

PDF DOI [BibTex]

PDF DOI [BibTex]


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Prior knowledge in support vector kernels

Schölkopf, B., Simard, P., Smola, A., Vapnik, V.

In Advances in Neural Information Processing Systems 10, pages: 640-646 , (Editors: M Jordan and M Kearns and S Solla ), MIT Press, Cambridge, MA, USA, Eleventh Annual Conference on Neural Information Processing (NIPS), June 1998 (inproceedings)

PDF Web [BibTex]

PDF Web [BibTex]


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From regularization operators to support vector kernels

Smola, A., Schölkopf, B.

In Advances in Neural Information Processing Systems 10, pages: 343-349, (Editors: M Jordan and M Kearns and S Solla), MIT Press, Cambridge, MA, USA, 11th Annual Conference on Neural Information Processing (NIPS), June 1998 (inproceedings)

PDF Web [BibTex]

PDF Web [BibTex]


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Eine beweistheoretische Anwendung der

Harmeling, S.

Biologische Kybernetik, Westfälische Wilhelms-Universität Münster, Münster, May 1998 (diplomathesis)

PDF [BibTex]

PDF [BibTex]


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Qualitative Modeling for Data Miner’s Requirements

Shin, H., Jhee, W.

In Proc. of the Korean Management Information Systems, pages: 65-73, Conference on the Korean Management Information Systems, April 1998 (inproceedings)

[BibTex]

[BibTex]


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Übersicht durch Übersehen

Schölkopf, B.

Frankfurter Allgemeine Zeitung , Wissenschaftsbeilage, March 1998 (misc)

[BibTex]

[BibTex]


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Learning view graphs for robot navigation

Franz, M., Schölkopf, B., Mallot, H., Bülthoff, H.

Autonomous Robots, 5(1):111-125, March 1998 (article)

Abstract
We present a purely vision-based scheme for learning a topological representation of an open environment. The system represents selected places by local views of the surrounding scene, and finds traversable paths between them. The set of recorded views and their connections are combined into a graph model of the environment. To navigate between views connected in the graph, we employ a homing strategy inspired by findings of insect ethology. In robot experiments, we demonstrate that complex visual exploration and navigation tasks can thus be performed without using metric information.

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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Masking by plaid patterns: effects of presentation time and mask contrast

Wichmann, F., Henning, G.

pages: 115, 1. T{\"u}binger Wahrnehmungskonferenz (TWK 98), February 1998 (poster)

Abstract
Most current models of early spatial vision comprise of sets of orientation- and spatial-frequency selective filters with our without limited non-linear interactions amongst different subsets of the filters. The performance of human observers and of such models for human spatial vision were compared in experiments using maskers with two spatial frequencies (plaid masks). The detectability of horizontally orientated sinusoidal signals at 3.02 c/deg was measured in standard 2AFC-tasks in the presence of plaid patterns with two-components at the same spatial frequency as the signal but at different orientations (+/- 15, 30, 45, and 75 deg from the signal) and with varying contrasts (1.0, 6.25 and 25.0% contrast). In addition, the temporal envelope of the stimulus presentation was either a rectangular pulse of 19.7 msec duration, or a temporal Hanning window of 1497 msec.Threshold elevation varied with plaid component orientation, peaked +/- 30 deg from the signal where nearly a log unit threshold elevation for the 25.0% contrast plaid was observed. For plaids with 1.0% contrast we observed significant facilitation even with plaids whose components were 75 deg from that of the signal. Elevation factors were somewhat lower for the short stimulus presentation time but were still significant (up to a factor of 5 or 6). Despite of the simple nature of the stimuli employed in this study-sinusoidal signal and plaid masks comprised of only two sinusoids-none of the current models of early spatial vision can fully account for all the data gathered.

Web [BibTex]

Web [BibTex]


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Qualitative Modeling for Data Miner‘s Requirement

Shin, H.

Biologische Kybernetik, Hong-Ik University, Seoul, Korea, February 1998, Written in Korean (diplomathesis)

ZIP [BibTex]

ZIP [BibTex]


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No role for motion blur in either motion detection or motion based image segmentation

Wichmann, F., Henning, G.

Journal of the Optical Society of America A, 15 (2), pages: 297-306, 1998 (article)

Abstract
Determined the influence of high-spatial-frequency losses induced by motion on motion detection and on motion-based image segmentation. Motion detection and motion-based segmentation tasks were performed with either spectrally low-pass or spectrally broadband stimuli. Performance on these tasks was compared with a condition having no motion but in which form differences mimicked the perceptual loss of high spatial frequencies produced by motion. This allowed the relative salience of motion and motion-induced blur to be determined. Neither image segmentation nor motion detection was sensitive to the high-spatial-frequency content of the stimuli. Thus the change in perceptual form produced in moving stimuli is not normally used as a cue either for motion detection or for motion-based image segmentation in ordinary situations.

PDF [BibTex]

PDF [BibTex]