TY - GEN

T1 - Learning to perceive transparency from the statistics of natural scenes

AU - Levin, Anat

AU - Zoniet, Assaf

AU - Weiss, Yair

PY - 2003

Y1 - 2003

N2 - Certain simple images are known to trigger a percept of transparency: the input image I is perceived as the sum of two images I(x,y) = I1(x,y) +I2(x,y). This percept is puzzling. First, why do we choose the "more complicated" description with two images rather than the "simpler" explanation I(x,y) = I1(x,y) + 0? Second, given the infinite number of ways to express I as a sum of two images, how do we compute the "best" decomposition? Here we suggest that transparency is the rational percept of a system that is adapted to the statistics of natural scenes. We present a probabilistic model of images based on the qualitative statistics of derivative filters and "corner detectors" in natural scenes and use this model to find the most probable decomposition of a novel image. The optimization is performed using loopy belief propagation. We show that our model computes perceptually "correct" decompositions on synthetic images and discuss its application to real images.

AB - Certain simple images are known to trigger a percept of transparency: the input image I is perceived as the sum of two images I(x,y) = I1(x,y) +I2(x,y). This percept is puzzling. First, why do we choose the "more complicated" description with two images rather than the "simpler" explanation I(x,y) = I1(x,y) + 0? Second, given the infinite number of ways to express I as a sum of two images, how do we compute the "best" decomposition? Here we suggest that transparency is the rational percept of a system that is adapted to the statistics of natural scenes. We present a probabilistic model of images based on the qualitative statistics of derivative filters and "corner detectors" in natural scenes and use this model to find the most probable decomposition of a novel image. The optimization is performed using loopy belief propagation. We show that our model computes perceptually "correct" decompositions on synthetic images and discuss its application to real images.

UR - http://www.scopus.com/inward/record.url?scp=84858739586&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84858739586

SN - 0262025507

SN - 9780262025508

T3 - Advances in Neural Information Processing Systems

BT - Advances in Neural Information Processing Systems 15 - Proceedings of the 2002 Conference, NIPS 2002

PB - Neural information processing systems foundation

T2 - 16th Annual Neural Information Processing Systems Conference, NIPS 2002

Y2 - 9 December 2002 through 14 December 2002

ER -