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 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???
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 -