motion-clouds

Speed-Selectivity in Retinal Ganglion Cells is Sharpened by Broad Spatial Frequency, Naturalistic Stimuli
Press release Dès la rétine, le système visuel préfère des images naturelles Dans la rétine, au premier étage du traitement de l’image visuelle, on peut obtenir des représentations extrêmement fines.
Speed-Selectivity in Retinal Ganglion Cells is Sharpened by Broad Spatial Frequency, Naturalistic Stimuli
Bayesian Modeling of Motion Perception using Dynamical Stochastic Textures
Bayesian Modeling of Motion Perception using Dynamical Stochastic Textures
How the dynamics of human smooth pursuit is influenced by speed uncertainty
Voluntary tracking the moving clouds : Effects of speed variability on human smooth pursuit
Open Science
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Open Science
Voluntary tracking the moving clouds : Effects of speed variability on human smooth pursuit
Voluntary tracking the moving clouds : Effects of speed variability on human smooth pursuit
Voluntary tracking the moving clouds : Effects of speed variability on human smooth pursuit
A Mathematical Account of Dynamic Texture Synthesis for Probing Visual Perception
See a followup in Jonathan Vacher, Andrew Isaac Meso, Laurent U Perrinet, Gabriel Peyré (2018). Bayesian Modeling of Motion Perception using Dynamical Stochastic Textures. Neural Computation. Preprint PDF Cite DOI
Biologically Inspired Dynamic Textures for Probing Motion Perception
See a followup in Jonathan Vacher, Andrew Isaac Meso, Laurent U Perrinet, Gabriel Peyré (2018). Bayesian Modeling of Motion Perception using Dynamical Stochastic Textures. Neural Computation. Preprint PDF Cite DOI
Beyond simply faster and slower: exploring paradoxes in speed perception
The characteristics of microsaccadic eye movements varied with the change of strategy in a match-to-sample task
Dynamic Textures For Probing Motion Perception
See a followup in Jonathan Vacher, Andrew Isaac Meso, Laurent U Perrinet, Gabriel Peyré (2018). Bayesian Modeling of Motion Perception using Dynamical Stochastic Textures. Neural Computation. Preprint PDF Cite DOI
How and why do image frequency properties influence perceived speed?
Measuring speed of moving textures: Different pooling of motion information for human ocular following and perception
MotionClouds: Model-based stimulus synthesis of natural-like random textures for the study of motion perception
Mar 22, 2012
Motion Clouds: Model-based stimulus synthesis of natural-like random textures for the study of motion perception
MotionClouds are random dynamic stimuli optimized to study motion perception. Web-site Source code using Python. 37 citations on Google Scholar (last updated 22/10/2021) Follow-up paper Jonathan Vacher, Andrew Isaac Meso, Laurent U Perrinet, Gabriel Peyré (2018).
Motion Clouds: Model-based stimulus synthesis of natural-like random textures for the study of motion perception
Effect of image statistics on fixational eye movements
Measuring speed of moving textures: Different pooling of motion information for human ocular following and perception.
More is not always better: dissociation between perception and action explained by adaptive gain control
Band-pass motion stimuli for perception and action tasks. (a) In the space representing temporal against spatial frequency, each line going through the origin corresponds to stimuli moving at the same speed.
More is not always better: dissociation between perception and action explained by adaptive gain control
Pattern discrimination for moving random textures: Richer stimuli are more difficult to recognize
Pattern discrimination for moving random textures: Richer stimuli are more difficult to recognize
Pattern discrimination for moving random textures: Richer stimuli are more difficult to recognize
Edge statistics in natural images versus laboratory animal environments: implications for understanding lateral connectivity in V1
See a followup in Laurent U Perrinet, James A Bednar (2015). Edge co-occurrences can account for rapid categorization of natural versus animal images. Scientific Reports. Preprint PDF Cite Code DOI
Role of homeostasis in learning sparse representations
related publication : Laurent U Perrinet (2019). An adaptive homeostatic algorithm for the unsupervised learning of visual features. Vision. Preprint PDF Cite Code DOI
Role of homeostasis in learning sparse representations
Different pooling of motion information for perceptual speed discrimination and behavioral speed estimation
Dynamics of distributed 1D and 2D motion representations for short-latency ocular following
Dynamics of distributed 1D and 2D motion representations for short-latency ocular following
Dynamical Neural Networks: modeling low-level vision at short latencies
Dynamical Neural Networks (DyNNs) are a class of models for networks of neurons where particular focus is put on the role of time in the emergence of functional computational properties. The definition and study of these models involves the cooperation of a large range of scientific fields from statistical physics, probabilistic modelling, neuroscience and psychology to control theory.
Sparse Approximation of Images Inspired from the Functional Architecture of the Primary Visual Areas
relies on log-Gabor filters: Sylvain Fischer, Filip Šroubek, Laurent U Perrinet, Rafael Redondo, Gabriel Cristóbal (2007). Self-Invertible 2D Log-Gabor Wavelets. International Journal of Computer Vision. PDF Cite Code DOI Schematic structure of the primary visual cortex implemented in the present study.
Sparse Approximation of Images Inspired from the Functional Architecture of the Primary Visual Areas