Header logo is


2018


no image
Poster Abstract: Toward Fast Closed-loop Control over Multi-hop Low-power Wireless Networks

Mager, F., Baumann, D., Trimpe, S., Zimmerling, M.

Proceedings of the 17th ACM/IEEE Conference on Information Processing in Sensor Networks (IPSN), pages: 158-159, Porto, Portugal, April 2018 (poster)

ics

DOI Project Page [BibTex]

2018


DOI Project Page [BibTex]


no image
Representation of sensory uncertainty in macaque visual cortex

Goris, R., Henaff, O., Meding, K.

Computational and Systems Neuroscience (COSYNE) 2018, March 2018 (poster)

ei

[BibTex]

[BibTex]


no image
Generalized phase locking analysis of electrophysiology data

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

7th AREADNE Conference on Research in Encoding and Decoding of Neural Ensembles, 2018 (poster)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


no image
Photorealistic Video Super Resolution

Pérez-Pellitero, E., Sajjadi, M. S. M., Hirsch, M., Schölkopf, B.

Workshop and Challenge on Perceptual Image Restoration and Manipulation (PIRM) at the 15th European Conference on Computer Vision (ECCV), 2018 (poster)

ei

[BibTex]

[BibTex]


no image
Retinal image quality of the human eye across the visual field

Meding, K., Hirsch, M., Wichmann, F. A.

14th Biannual Conference of the German Society for Cognitive Science (KOGWIS 2018), 2018 (poster)

ei

[BibTex]

[BibTex]

1999


no image
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)

ei

[BibTex]

1999


[BibTex]


no image
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)

ei

[BibTex]

[BibTex]


no image
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.

ei

[BibTex]

[BibTex]


no image
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.

ei

[BibTex]


no image
Advances in Kernel Methods - Support Vector Learning

Schölkopf, B., Burges, C., Smola, A.

MIT Press, Cambridge, MA, 1999 (book)

ei

[BibTex]

[BibTex]


no image
test jon
(book)

[BibTex]