title: Top-down learning of low-level vision tasks.
abstract: Perceptual tasks such as edge detection, image segmentation, lightness computation and estimation of three-dimensional structure are considered to be low-level or mid-level vision problems and are traditionally approached in a bottom-up, generic and hard-wired way. An alternative to this would be to take a top-down, object-class-specific and example-based approach. In this paper, we present a simple computational model implementing the latter approach. The results generated by our model when tested on edge-detection and view-prediction tasks for three-dimensional objects are consistent with human perceptual expectations. The model's performance is highly tolerant to the problems of sensor noise and incomplete input image information. Results obtained with conventional bottom-up strategies show much less immunity to these problems. We interpret the encouraging performance of our computational model as evidence in support of the hypothesis that the human visual system may learn to perform supposedly low-level perceptual tasks in a top-down fashion.
authors.affiliation: E25-201, Center for Biological and Computational Learning, Massachusetts Institute of Technology, 45 Carleton Street, Cambridge, Massachusetts 02142, USA.
authors.forename: M J
authors.initials: MJ
authors.lastname: Jones
authors.forename: P
authors.initials: P
authors.lastname: Sinha
authors.forename: T
authors.initials: T
authors.lastname: Vetter
authors.forename: T
authors.initials: T
authors.lastname: Poggio
mesh_terms: D000465:Algorithms; D005145:Face; D006801:Humans; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D014796:Visual Perception / Q000502:physiology*