Search Results for author:
Found 6 papers, 1 papers with code
Measuring uncertainty in human visual segmentation
We show that image uncertainty affects measured human variability, and it influences how participants weigh different visual features.
Texture Interpolation for Probing Visual Perception
We apply our method by measuring the perceptual scale associated to the interpolation parameter in human observers, and the neural sensitivity of different areas of visual cortex in macaque monkeys.
Conditional Finite Mixtures of Poisson Distributions for Context-Dependent Neural Correlations
Parallel recordings of neural spike counts have revealed the existence of context-dependent noise correlations in neural populations.
Flexibly Regularized Mixture Models and Application to Image Segmentation
Our flexible approach can be easily generalized to adapt probabilistic mixture models to arbitrary data topologies.
Probabilistic Model of Visual Segmentation
Following this hypothesis, we propose a probabilistic generative model of visual segmentation that combines knowledge about 1) the sensitivity of neurons in the visual cortex to statistical regularities in natural images; and 2) the preference of humans to form contiguous partitions of visual space.
Statistical Models of Linear and Nonlinear Contextual Interactions in Early Visual Processing
A central hypothesis about early visual processing is that it represents inputs in a coordinate system matched to the statistics of natural scenes.
