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Reduced-reference image quality assessment using a wavelet-domain natural image statistic model
Zhou Wang; Simoncelli, E.P.
INSPEC:9775344
ISSN: 0277-786x
CID: 367342
Machine learning applied to perception: Decision-images for gender classification
Chapter by: Wichmann, Felix A.; Graf, Arnulf B A; Simoncelli, Eero P.; Bülthoff, Heinrich H.; Scholkopf, Bernhard
in: Advances in Neural Information Processing Systems by
[S.l.] : Neural information processing systems foundation, 2005
pp. ?-?
ISBN: 9780262195348
CID: 2872942
Constraining a bayesian model of human visual speed perception
Chapter by: Stocker, Alan A.; Simoncelli, Eero P.
in: Advances in Neural Information Processing Systems by
[S.l.] : Neural information processing systems foundation, 2005
pp. ?-?
ISBN: 9780262195348
CID: 2872932
Locally adaptive multiscale contrast optimization
Chapter by: Bonnier, N.; Simoncelli, Eero P
in: 2005 International Conference on Image Processing by
Piscataway, N.J. : Institute of Electrical and Electronics Engineers, 2005
pp. 949-952
ISBN: 0 7803 9134 9
CID: 371712
Nonlinear Image Representation via Local Multiscale Orientation
Hammond, David K; Simoncelli, Eero p
[s.l.] : Courant Institute, 2005
Extent: 10 p.
ISBN: n/a
CID: 378382
Structural approaches to image quality assessment
Chapter by: Wang, Z; Bovik, A.C.; Simoncelli, Eero P
in: Handbook of image and video processing by Bovik, Alan C. [Eds]
Amsterdam ; Boston, MA : Elsevier Academic Press, c2005
pp. 961-974
ISBN: 0121197921
CID: 370562
Translation insensitive image similarity in complex wavelet domain
Wang, Zhou; Simoncelli, Eero P
INSPEC:8548656
ISSN: 1520-6149
CID: 2030982
Statistical modeling of photographic images
Chapter by: Simoncelli, Eero P
in: Handbook of image and video processing by Bovik, Alan C. [Eds]
Amsterdam ; Boston, MA : Elsevier Academic Press, c2005
pp. 431-441
ISBN: 0121197921
CID: 370652
An adaptive linear system framework for image distortion analysis
Chapter by: Zhou Wang; Simoncelli, Eero P
in: 2005 International Conference on Image Processing by
Piscataway, N.J. : Institute of Electrical and Electronics Engineers, 2005
pp. 1160-1163
ISBN: 0-7803-9134-9
CID: 371722
Maximum likelihood estimation of a stochastic integrate-and-fire neural encoding model
Paninski, Liam; Pillow, Jonathan W; Simoncelli, Eero P
We examine a cascade encoding model for neural response in which a linear filtering stage is followed by a noisy, leaky, integrate-and-fire spike generation mechanism. This model provides a biophysically more realistic alternative to models based on Poisson (memoryless) spike generation, and can effectively reproduce a variety of spiking behaviors seen in vivo. We describe the maximum likelihood estimator for the model parameters, given only extracellular spike train responses (not intracellular voltage data). Specifically, we prove that the log-likelihood function is concave and thus has an essentially unique global maximum that can be found using gradient ascent techniques. We develop an efficient algorithm for computing the maximum likelihood solution, demonstrate the effectiveness of the resulting estimator with numerical simulations, and discuss a method of testing the model's validity using time-rescaling and density evolution techniques
PMID: 15516273
ISSN: 0899-7667
CID: 143596