Auditory motion perception emerges from successive sound localizations integrated over time.

with V. Roggerone, C. Tarlao and C Guastavino.

  1. Roggerone, V., Vacher, J., Tarlao, C. & Guastavino, C. Auditory motion perception emerges from successive sound localizations integrated over time. Scientific Reports 9, 16437 (2019).

Contributed talk at ECVP !

An ideal observer model for grouping and contour integration in natural images. Check the slides of the talk here. Joint work with Pascal Mamassian and Ruben Coen-Cagli

Spatial context in images modulates visual perception reflecting optimization to the statistics of natural images. Popular models based on divisive normalization (ratio between target and context features) offer a link between optimal coding principles and contextual modulation in cortex. Here, we apply this framework to perceptual grouping in natural images, and more specifically to contour integration. First, we show that a successful model of image statistics based on normalization (termed Gaussian Scale Mixture, GSM) learns dependencies between colinear features in natural images. We then show analytically that image regions with strong normalization are statistical outliers of the model, and use this insight to distinguish high-salience regions due to high contrast from those corresponding to contours. To this aim, we extend the model to a mixture of GSMs, with each component encoding a specific orientation and curvature. Our model performs competitively on contour detection in natural images from the BSD500 database. We further evaluate our model by comparing its performance on iso-oriented Gabor elements embedded in Gabor noise (‘snakes’) to the psychophysics literature. Our model thus serves as an ideal observer for contour integration and a basis for future experiments on natural image segmentation.

Combining mixture models with linear mixing updates: multilayer image segmentation and synthesis

with R. Coen-Cagli.

  1. Vacher, J. & Coen-Cagli, R. Combining mixture models with linear mixing updates: multilayer image segmentation and synthesis. arXiv preprint arXiv:1905.10629 (2019).

Probabilistic Model of Visual Segmentation

with P. Mamassian and R. Coen-Cagli.

  1. Vacher, J., Mamassian, P. & Coen-Cagli, R. Probabilistic Model of Visual Segmentation. arXiv preprint arXiv:1806.00111 (2019).

Bayesian modeling of motion perception using dynamical stochastic textures

with A. I. Meso, L. Perrinet and G. Peyré.

  1. Vacher, J., Meso, A. I., Perrinet, L. U. & Peyré, G. Bayesian modeling of motion perception using dynamical stochastic textures. Neural computation 30, 3355–3392 (2018).

Pagination