Clusterability and Clustering of Images and Other "Real" High-Dimensional Data.

Clustering a high-dimensional data set is known to be very difficult. In this paper, we show that this is not the case when the points to cluster correspond to images. More specifically, image data sets are shown to have a lot of structures, so much, so that projecting the set onto a random 1D linear subspace is likely to uncover a binary grouping among the images.

Pose-free structure from motion using depth from motion constraints.

Structure from motion (SFM) is the problem of recovering the geometry of a scene from a stream of images taken from unknown viewpoints. One popular approach to estimate the geometry of a scene is to track scene features on several images and reconstruct their position in 3-D. During this process, the unknown camera pose must also be recovered.

Hardware-friendly descreening.

Conventional electrophotographic printers tend to produce Moiré artifacts when used for printing images scanned from printed material such as books and magazines. We propose a novel noniterative, nonlinear, and space-variant descreening filter that removes a wide range of Moiré-causing screen frequencies in a scanned document while preserving image sharpness and edge detail. This filter is inspired by Perona-Malik's anisotropic diffusion equation.