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Found 7 papers, 1 papers with code
Perceptual Scales Predicted by Fisher Information Metrics
Here, we demonstrate the value of measuring the perceptual scale of classical (spatial frequency, orientation) and less classical physical variables (interpolation between textures) by embedding it in recent probabilistic modeling of perception.
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.
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.
Bayesian Modeling of Motion Perception using Dynamical Stochastic Textures
We use the dynamic texture model to psychophysically probe speed perception in humans using zoom-like changes in the spatial frequency content of the stimulus.
Biologically Inspired Dynamic Textures for Probing Motion Perception
We study here the principled construction of a generative model specifically crafted to probe motion perception.
