8 citations · 21 across the 6 of their papers we have counts for
6 papers
Measuring uncertainty in human visual segmentation
Jonathan Vacher, Claire Launay, Pascal Mamassian +1
Segmenting visual stimuli into distinct groups of features and visual objects is central to visual function. Classical psychophysical methods have helped uncover many rules of huma…
Texture Interpolation for Probing Visual Perception
Jonathan Vacher, Aida Davila, Adam Kohn +1
Texture synthesis models are important tools for understanding visual processing. In particular, statistical approaches based on neurally relevant features have been instrumental i…
Flexibly Regularized Mixture Models and Application to Image Segmentation
Jonathan Vacher, Claire Launay, Ruben Coen-Cagli
Probabilistic finite mixture models are widely used for unsupervised clustering. These models can often be improved by adapting them to the topology of the data. For instance, in o…
Probabilistic Model of Visual Segmentation
Jonathan Vacher, Pascal Mamassian, Ruben Coen-Cagli
Visual segmentation is a key perceptual function that partitions visual space and allows for detection, recognition and discrimination of objects in complex environments. The proce…
Bayesian Modeling of Motion Perception using Dynamical Stochastic Textures
Jonathan Vacher, Andrew Isaac Meso, Laurent U. Perrinet +1
A common practice to account for psychophysical biases in vision is to frame them as consequences of a dynamic process relying on optimal inference with respect to a generative mod…
Biologically Inspired Dynamic Textures for Probing Motion Perception
Jonathan Vacher, Andrew Meso, Laurent U Perrinet +1
Perception is often described as a predictive process based on an optimal inference with respect to a generative model. We study here the principled construction of a generative mo…