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Determining the rigid transformation relating 2D images to known 3D geometry is a classical problem in photogrammetry and computer vision. Heretofore, the best methods for solving the problem have relied on iterative optimization methods which cannot be proven to converge and/or which do not eeectively account for the orthonor-mal structure of rotation(More)
A fundamental open problem in computer vision-determining pose and correspondence between two sets of points in space-is solved with a novel, robust and easily implementable algorithm. The technique works on noisy point sets that may be of unequal sizes and may differ by non-rigid transformations. A 2D variation calculates the pose between point sets(More)
In the pursuit of peripheral neural representations of shape for the sense of touch, a series of two- and three-dimensional objects were stroked across the fingerpad of the anesthetized monkey and responses evoked in cutaneous mechanoreceptive primary afferent nerve fibers recorded. Responses of slowly adapting fibers (SAs) and rapidly adapting fibers (RAs)(More)
We study the relation of neural development, organization, and activity to behavior. We provide a model of the locomotive oscillator, a neural system supplying alternating stimulation to extensor and flexor muscles creating an oscillatory motion. We propose a protocol by which this neural system starting from unstructured, unconnected neural populations(More)
We present a Mean Field Theory method for locating two-dimensional objects that have undergone rigid transformations. The resulting algorithm is a form of coarse-to-fine correlation matching. We first consider problems of matching synthetic point data, and derive a point matching objective function. A tractable line segment matching objective function is(More)
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