When one looks at a scene, one is immediately able to classify the edges in the scene, perceiving that this edge is shadow boundary, that edge is a crease in a surface and so on. If we consider the visual system to be working to interpret scene attributes from image data, we would say that th visual system is very good at determining the physical causes of the contours to which edges project in an image. Moreover, the visual system uses information provided by the configuration of contours in an image to help interpret scene attributes such as the shapes of surfaces. The proble of contour interpretation, which includes both of these functions, appears quite difficult when one considers that the information provided in an imag for both functions is locally ambiguous. The approach to the problem adopted by many computer vision researchers is to specify a set of constraints on contour interpretation which, when taken together, define a unique solution. A similar approach can be used to organize research into human perceptual processing of contours. Within the approach, one consider the visual system as implicitly enforcing a set of constraints in its interpretation of contours. The constraints come from two sources; the image data itself and the natura structure of the environment. The present proposal aims to analyze the constraints of both types which are potentially available to the visual system for contour interpretation and to investigate psychophysically the nature of the constraints actually used by the visual system. The research will focus on constraints on two types of contour which have received relatively little attention from vision researchers: reflectance contours, which project from discontinuities in surface reflectance, and shadow contours, which project from discontinuities in illumination in a scene. Also considered will be occluding edges, due to their importance in determining scene structure. An important aspect of the research will be the use of 3D rendering techniques to generate naturalistic images for experimental stimuli. This will allow the manipulation of independent variables in both the 3D scene domain and the 2D image domain. The ability to control the 3D structure of scenes is necessary for the investigation of what natural constraints on edge structure are assumed by the visual system since these are specified in the scene domain, not in the image domain. Th results of the investigation will provide a deeper understanding of the information available in images for contour interpretation and the ways in which this information is used by the human visual system. More broadly, the research relates to the issue of how much knowledge of environmental structure and of the image formation process is incorporated into visual system processing.

Agency
National Institute of Health (NIH)
Institute
National Eye Institute (NEI)
Type
Research Project (R01)
Project #
7R01EY009383-03
Application #
2163000
Study Section
Visual Sciences B Study Section (VISB)
Project Start
1992-08-01
Project End
1996-07-31
Budget Start
1994-08-01
Budget End
1996-07-31
Support Year
3
Fiscal Year
1994
Total Cost
Indirect Cost
Name
University of Pennsylvania
Department
Psychology
Type
Schools of Arts and Sciences
DUNS #
042250712
City
Philadelphia
State
PA
Country
United States
Zip Code
19104
Saunders, Jeffrey A; Knill, David C (2004) Visual feedback control of hand movements. J Neurosci 24:3223-34
Saunders, Jeffrey A; Knill, David C (2003) Humans use continuous visual feedback from the hand to control fast reaching movements. Exp Brain Res 152:341-52
Knill, David C (2003) Mixture models and the probabilistic structure of depth cues. Vision Res 43:831-54
Knill, D C (2001) Contour into texture: information content of surface contours and texture flow. J Opt Soc Am A Opt Image Sci Vis 18:12-35
Knill, D C (1998) Discrimination of planar surface slant from texture: human and ideal observers compared. Vision Res 38:1683-711
Jeka, J J; Schoner, G; Dijkstra, T et al. (1997) Coupling of fingertip somatosensory information to head and body sway. Exp Brain Res 113:475-83
Knill, D C; Mamassian, P; Kersten, D (1997) Geometry of shadows. J Opt Soc Am A Opt Image Sci Vis 14:3216-32
Kersten, D; Mamassian, P; Knill, D C (1997) Moving cast shadows induce apparent motion in depth. Perception 26:171-92