We describe a (meta) formalism for defining a variety of (object oriented) data models in a unified framework based on a variation of first-order logic. As specific example we use...
We address the problem of unsupervised learning of complex articulated object models from 3D range data. We describe an algorithm whose input is a set of meshes corresponding to d...
In statistics, mixture models consisting of several component subpopulations are used widely to model data drawn from heterogeneous sources. In this paper, we consider maximum lik...
Active learning and crowdsourcing are promising ways to efficiently build up training sets for object recognition, but thus far techniques are tested in artificially controlled ...
This paper presents a framework for data modeling ntic abstraction of image/video data. The framework is based on spatio-temporalinformation associated with salient objects in an ...
Young Francis Day, Serhan Dagtas, Mitsutoshi Iino,...