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pro vyhledávání: '"Vacher, Jonathan"'
Autor:
Vacher, Jonathan, Mamassian, Pascal
Publikováno v:
The Twelfth International Conference on Learning Representations. 2024
Perception is often viewed as a process that transforms physical variables, external to an observer, into internal psychological variables. Such a process can be modeled by a function coined perceptual scale. The perceptual scale can be deduced from
Externí odkaz:
http://arxiv.org/abs/2310.11759
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 human perceptual segmentation, and recent progress in machine learning has
Externí odkaz:
http://arxiv.org/abs/2301.07807
Texture synthesis models are important tools for understanding visual processing. In particular, statistical approaches based on neurally relevant features have been instrumental in understanding aspects of visual perception and of neural coding. New
Externí odkaz:
http://arxiv.org/abs/2006.03698
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 order to classify spatially adjacent data points similarly, it is commo
Externí odkaz:
http://arxiv.org/abs/1905.10629
Autor:
Vacher, Jonathan
Le but de cette thèse est de proposer une modélisation mathématique des stimulations visuelles afin d'analyser finement des données expérimentales en psychophysique et en électrophysiologie. Plus précis\'ement, afin de pouvoir exploiter des te
Externí odkaz:
http://www.theses.fr/2017PSLED005/document
Visual segmentation is a key perceptual function that partitions visual space and allows for detection, recognition and discrimination of objects in complex environments. The processes underlying human segmentation of natural images are still poorly
Externí odkaz:
http://arxiv.org/abs/1806.00111
Publikováno v:
In Neural Networks May 2022 149:107-123
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 model. The present study details the complete formulation of such a gener
Externí odkaz:
http://arxiv.org/abs/1611.01390
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 model specifically crafted to probe motion perception. In that context,
Externí odkaz:
http://arxiv.org/abs/1511.02705
Publikováno v:
In IFAC PapersOnLine 2018 51(16):259-264