E
Eero P. Simoncelli
Researcher at Center for Neural Science
Publications - 273
Citations - 83270
Eero P. Simoncelli is an academic researcher from Center for Neural Science. The author has contributed to research in topics: Wavelet & Image processing. The author has an hindex of 81, co-authored 260 publications receiving 68623 citations. Previous affiliations of Eero P. Simoncelli include New York University & Stanford University.
Papers
More filters
Journal ArticleDOI
A functional and perceptual signature of the second visual area in primates
Jeremy Freeman,Jeremy Freeman,Corey M. Ziemba,David J. Heeger,David J. Heeger,Eero P. Simoncelli,J. Anthony Movshon,J. Anthony Movshon +7 more
TL;DR: A synthetic stimuli replicating the higher-order statistical dependencies found in natural texture images was constructed and used to stimulate macaque V1 and V2 neurons, revealing a particular functional role for V2 in the representation of natural image structure.
Journal ArticleDOI
Quality-aware images
TL;DR: A practical quality-aware image encoding, decoding and quality analysis system, which employs a novel reduced-reference image quality assessment algorithm based on a statistical model of natural images and a previously developed quantization watermarking-based data hiding technique in the wavelet transform domain.
Journal ArticleDOI
Vision and the statistics of the visual environment.
TL;DR: The Efficient Coding Hypothesis, which holds that the purpose of early visual processing is to produce an efficient representation of the incoming visual signal, provides a quantitative link between the statistical properties of the world and the structure of the visual system.
Journal ArticleDOI
Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Encoding Model
TL;DR: It is proved that the log-likelihood function is concave and thus has an essentially unique global maximum that can be found using gradient ascent techniques.
Journal ArticleDOI
Computational models of cortical visual processing
TL;DR: Two related models are presented that share a common structure that operates in the same way on different kinds of input, and instantiate the widely held view that computational strategies are similar throughout the cerebral cortex.