Muralidhara Subbarao

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A method is described for selecting the optimal focus measure with respect to grey-level noise from a given set of focus measures in passive autofocusing and depth-from-focus applications. The method is based on two new metrics that have been deened for estimating the noise-sensitivity of diierent focus measures. The rst metric { the Autofocusing(More)
In the case of machine vision : : : one ought to understand image formation if one wishes to recover information about the world from images. Abstract The image of a scene formed by an optical system such as a lens contains both photometric and geometric information about the scene. `Inverse Optics' is the problem of recovering this information from a set(More)
State University of New York at Stony Brook Stony Brook, New York 11794-2350 Stony Brook Computer Vision Laboratory Tech. Report No. 91.07.03 July 3, 1991 Spatial-Domain Convolution/Deconvolution Transform Muralidhara Subbarao Abstract A new linear transform is de ned for real valued functions which can be expanded in Taylor series. For an image which can(More)
A major source of three-dimensional (3D) information about objects in the world is available to the observer in the form of time-varying imagery. Relative motion between textured objects and the observer generates a time-varying optic array at the image, from which image motion of contours, edge fragments, and feature points can be extracted. These dynamic(More)
A new method is described for interpreting image flow (or optical flow) in a small field of view produced by a rigidly moving curved surface. The equations relating the shape and motion of the surface to the image flow are formulated. These equations are solved to obtainexplicit analytic expressions for the motion, orientation, and curvatures of the surface(More)