20 June 1997 Background characterization and visualization based on visual neurophysiology
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Abstract
We present and demonstrate a method to characterize a background scene, to extrapolate the background characteristics into a specified target region, and to generate a synthetic target image with the visual characteristics of the surrounding background. The algorithm is based on a computational model of spatial pattern analysis in the front-end retinal-cortical visual system. It uses nonstationary multi-resolution spatial filtering to extrapolate the intensity and the intensity modulation amplitude of the surrounding background into the target region. The algorithm provides a method to compute the background-induced bias for use as a zero-reference in computational models of target boundary perception and shape discrimination. We demonstrate the method with a complex, heterogeneous scene containing many discrete objects and backgrounds. The contrast and texture of the visualization blends into the local background. In most cases, the target boundaries are difficult to see, and the target regions are difficult to distinguish from the background. The results provide insight into the capabilities and limitations of the underlying model to front-end human visual pattern analysis. They provide insight into scene segmentation, shape properties, and prior knowledge of scene organization and object appearance for modeling visual discrimination.
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Gary Witus, Paul G. Gottschalk, Mitchell A. Cohen, Grant R. Gerhart, Robert E. Karlsen, Thomas J. Meitzler, Richard C. Goetz, Eui Jung Sohn, Darryl Bryk, "Background characterization and visualization based on visual neurophysiology", Proc. SPIE 3062, Targets and Backgrounds: Characterization and Representation III, (20 June 1997); doi: 10.1117/12.276679; https://doi.org/10.1117/12.276679
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