Speaker
Description
Visual operators (e.g. edge detectors) are classically modelled using small circuits involving canonical computations, such as template-matching and gain control. Circuit models explain many aspects of the empirical descriptors that are used to characterize local visual operators, from sensitivity to noise-based estimates of perceptual kernels. Notwithstanding their utility, these models fail to provide a unified framework encompassing the variety of effects observed experimentally, such as the impact of contrast, SNR, and attention on the above descriptors. My goal is to start with a simple, plausible geometrical representation of the perceptual operation carried out by the observer, and to show that this representation is sufficiently expressive to capture a wide range of empirical effects associated with elementary visual computations. The resulting framework offers a new perspective on specific empirical descriptors, such as perceptual kernels and their second-order variants. For example, it relates these descriptors to notions of flatness and curvature in perceptual space. More generally, it suggests an intuitive geometrical model in which perception acts as a surveyor charting a sensory landscape of the external world: intrinsic factors, like attention, control the ability of the surveyor to accurately measure the landscape, while extrinsic factors, like stimulus contrast, shape the geometry of the landscape itself.