Shimon Ullman

Ullman studies vision processing by both humans and machines, with the goal of understanding the mechanisms of the human visual system, and how to construct artificial systems with visual capabilities. In work on motion perception, he developed a computational theory for the recovery of the three-dimensional shape of objects from their projected image motion. He introduced the study of visual routines, which are sequential program-like visual operations, and a related model of cortical model using a counter-stream structure. Together, they act as a bridge between low-level and high-level perceptual properties. A part of this work included the idea of a visual saliency map in visual system to regulate selective spatial attention. He also models the way the brain carries out visual processing at high levels, to obtain object recognition and general scene understanding. In addition to modeling visual recognition in the adult brain, he developed computational models of learning during early development, showing how knowledge of the world emerges from the combination of innate mechanisms and visual experience.