The work is in the continuation of my previous Postdoc. And I will be surpervised by Pascal Mamassian and Ruben Coen-Cagli (AECOM, NYC).

Visual segmentation is a core function of biological vision, key to adaptive behavior in complex environments. Early models inspired by the feedforward processing in the visual system described texture-based human segmentation as a comparison of the summary statistics of low-level image features across space. Here we consider the alternative view that, due to image ambiguity and sensory noise, perceptual segmentation requires probabilistic inference.

Visual segmentation is a core function of biological vision, key to adaptive behavior in complex environments. Foundational work identified Gestalt principles of segmentation, e.g. grouping by similarity, proximity and good continuation, and revealed that visual cortical neurons are sensitive to those cues. Early models inspired by the feedforward cortical architecture described texture-based human segmentation as the process of comparing the summary statistics of low-level visual features across space. Indeed the summary statistics representation is the most prominent model of naturalistic texture perception, yet it has been challenged precisely because it does not fully capture the influence of segmentation.

1. Le Coënt, A., Fribourg, L., Vacher, J. & Wisniewski, R. Probabilistic reachability and control synthesis for stochastic switched systems using the tamed Euler method. Nonlinear Analysis: Hybrid Systems 36, 100860 (2020).

   @article{lecoent2020probabilistic,
     title = {Probabilistic reachability and control synthesis for stochastic switched systems using the tamed Euler method},
     author = {Le Co\"ent, Adrien and Fribourg, Laurent and Vacher, Jonathan and Wisniewski, Rafael},
     journal = {Nonlinear Analysis: Hybrid Systems},
     volume = {36},
     pages = {100860},
     year = {2020},
     publisher = {Elsevier},
     doi = {10.1016/j.nahs.2020.100860}
   }