Section: New Results

Analysis and Simulation

Visual Features in the Perception of Liquids [7]

Perceptual constancy—identifying surfaces and objects across large image changes—remains an important challenge for visual neuroscience. Liquids are particularly challenging because they respond to external forces in complex, highly variable ways, presenting an enormous range of images to the visual system. To achieve constancy, the brain must perform a causal inference that disentangles the liquid’s viscosity from external factors—like gravity and object interactions—that also affect the liquid’s behavior. Here, we tested whether the visual system estimates viscosity using “midlevel” features that respond more to viscosity than other factors. Our findings demonstrate that the visual system achieves constancy by representing stimuli in a multidimensional feature space—based on complementary, midlevel features—which successfully cluster very different stimuli together and tease similar stimuli apart, so that viscosity can be read out easily.

Teaching Spatial Augmented Reality: a Practical Assignment for Large Audiences [13]

We conceived a new methodology to teach spatial augmented reality in a practical assignment to large audiences. Our approach does not require specific equipment such as video projectors while teaching the principal topics and difficulties involved in spatial augmented reality applications, and especially calibration and tracking. The key idea is to set up a scene graph consisting of a 3D scene with a simulated projector that "projects" content onto a virtual representation of the real-world object. For illustrating the calibration, we simplify the intrinsic parameters to using the field of view, both for the camera and the projector. For illustrating the tracking, instead of relying on specific hardware or software, we exploit the relative transformations in the scene graph.