9514679 GILCHRIST This research will further examine how the human visual system assigns black, white and gray values to surfaces represented in the image projected onto the retina. This problem remains unsolved, in human vision as in computer vision, because the intensity of light reflected from any given surface in the visual world, regardless of its actual shade of gray, can vary by a factor of a billion to one, depending on lighting conditions. Prior research has established that the perceived shade of gray of a surface depends on the intensity of that surface relative to that of neighboring surfaces, not its absolute intensity, and that relative intensity is just what is encoded when light from the world strikes the human retina. But this brings into focus what has been called the anchoring problem: How is an absolute or specific shade of gray assigned to these relative intensity values? The general approach of the work will be to formulate alternative potential rules the visual system might use and then to construct visual displays that produce retinal images capable of testing among these rules. For a given display, one rule might predict that a particular surface will be seen as middle gray while a competing rule might predict that the same surface will be seen as white. The display will be presented to a set of naive human observers, from whom reports will be obtained as to the apparent gray shade of the surface. They will indicate the appearance of the target surface by choosing a matching chip from a standard scale of gray chips under standard lighting conditions. The work of this project will build upon a series of findings from previous funding that have established the rules of anchoring for relatively simple visual displays. In short these rules state that, for simple images, the surface with the highest intensity will be seen as white and darker regions will be scaled relative to this standard. But relative size of regions has also been shown to be important and can override the role of the highest intensity in certain cases. Prior work along these lines has produced a model of visual functioning that has already proven to account for much of the research literature in this field. The work of this research project will be guided by contradictions and gaps in this model. An important goal will be the extension of the model to ever more complex images. The solution of this problem in human vision should make it easier to solve the problem for computer vision. ***
