The Weizmann Institute of Science Faculty of Mathematics and Computer Science Vision and Robotics Seminar Anat Levin School of Computer Science \& Engineering Hebrew University will speak on Learning to perceive transparency from the statistics of natural scenes Abstract: 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 infnite 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, that 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 real and synthetic images. This is joint work with Assaf Zomet and Yair Weiss. The lecture will take place in the Lecture Hall, Room 1, Ziskind Building on Thursday, November 21, 2002 at 11:00