Try a new search

Format these results:

Searched for:

in-biosketch:yes

person:eps2

Total Results:

237


Statistically driven sparse image approximation

Chapter by: Figueras i Ventura, R.M.; Simoncelli, Eero P
in: Proceedings 2007 IEEE International Conference on Image Processing, ICIP 2007 by
Piscataway, NJ IEEE Service Center, 2007
pp. 461-464
ISBN: 978-1-4244-1436-9
CID: 371682

A machine learning framework for adaptive combination of signal denoising methods

Chapter by: Hammond, D.K.; Simoncelli, Eero P
in: Proceedings 2007 IEEE International Conference on Image Processing, ICIP 2007 by
Piscataway, NJ IEEE Service Center, 2007
pp. 29-32
ISBN: 978-1-4244-1436-9
CID: 371692

Empirical Bayes Least Squares Estimation without an Explicit Prior

Raphan, Martin; Simoncelli, Eero P
[s.l.] : Courant Institute, 2007
Extent: 17 p.
ISBN: n/a
CID: 379352

Optimal denoising in redundant bases

Chapter by: Raphan, M.; Simoncelli, Eero P
in: Proceedings 2007 IEEE International Conference on Image Processing, ICIP 2007 by
Piscataway, NJ IEEE Service Center, 2007
pp. 113-116
ISBN: 978-1-4244-1436-9
CID: 371702

Image statistics and modeling

Simoncelli, Eero P
San Rafael : Morgan & Claypool, 2007
Extent: 1 v.
ISBN: 9781598292268
CID: 367602

Optimal denoising in redundant bases

Chapter by: Raphan, Martin; Simoncelli, Eero P.
in: Proceedings - International Conference on Image Processing, ICIP by
[S.l.] : Neural information processing systems foundation, 2006
pp. ?-?
ISBN: 9781424414376
CID: 2872982

How MT cells analyze the motion of visual patterns

Rust, Nicole C; Mante, Valerio; Simoncelli, Eero P; Movshon, J Anthony
Neurons in area MT (V5) are selective for the direction of visual motion. In addition, many are selective for the motion of complex patterns independent of the orientation of their components, a behavior not seen in earlier visual areas. We show that the responses of MT cells can be captured by a linear-nonlinear model that operates not on the visual stimulus, but on the afferent responses of a population of nonlinear V1 cells. We fit this cascade model to responses of individual MT neurons and show that it robustly predicts the separately measured responses to gratings and plaids. The model captures the full range of pattern motion selectivity found in MT. Cells that signal pattern motion are distinguished by having convergent excitatory input from V1 cells with a wide range of preferred directions, strong motion opponent suppression and a tuned normalization that may reflect suppressive input from the surround of V1 cells
PMID: 17041595
ISSN: 1097-6256
CID: 112984

Spike-triggered neural characterization

Schwartz, Odelia; Pillow, Jonathan W; Rust, Nicole C; Simoncelli, Eero P
Response properties of sensory neurons are commonly described using receptive fields. This description may be formalized in a model that operates with a small set of linear filters whose outputs are nonlinearly combined to determine the instantaneous firing rate. Spike-triggered average and covariance analyses can be used to estimate the filters and nonlinear combination rule from extracellular experimental data. We describe this methodology, demonstrating it with simulated model neuron examples that emphasize practical issues that arise in experimental situations
PMID: 16889482
ISSN: 1534-7362
CID: 143606

Quality-aware images

Wang, Zhou; Wu, Guixing; Sheikh, Hamid Rahim; Simoncelli, Eero P; Yang, En-Hui; Bovik, Alan Conrad
We propose the concept of quality-aware image, in which certain extracted features of the original (high-quality) image are embedded into the image data as invisible hidden messages. When a distorted version of such an image is received, users can decode the hidden messages and use them to provide an objective measure of the quality of the distorted image. To demonstrate the idea, we build a practical quality-aware image encoding, decoding and quality analysis system, which employs: 1) a novel reduced-reference image quality assessment algorithm based on a statistical model of natural images and 2) a previously developed quantization watermarking-based data hiding technique in the wavelet transform domain
PMID: 16764291
ISSN: 1057-7149
CID: 143603

Sensory adaptation within a Bayesian framework for perception

Stocker, A.A.; Simoncelli, Eero P
ORIGINAL:0008281
ISSN: 1049-5258
CID: 371212