We present a manifold learning approach to dimensionality
reduction that explicitly models the manifold as a mapping
from low to high dimensional space. The manifold is
represen...
In computed tomography, direct inversion of the Radon transform requires more projections than are practical due to constraints in scan time and image accessibility. Therefore, it...
We present a method for controlling the appearance of an arbitrary 3D object using a projector and a camera. Our goal is to make one object look like another by projecting a caref...
Michael D. Grossberg, Harish Peri, Shree K. Nayar,...
We consider the structure from motion problem for a previously introduced, highly general imaging model, where cameras are modeled as possibly unconstrained sets of projection ray...
A good distance metric is crucial for unsupervised learning from high-dimensional data. To learn a metric without any constraint or class label information, most unsupervised metr...