In the task of visual object categorization, semantic context can play the very important role of reducing ambiguity in objects' visual appearance. In this work we propose to...
Andrew Rabinovich, Andrea Vedaldi, Carolina Galleg...
This paper introduces a uniform statistical framework for both 3-D and 2-D object recognition using intensity images as input data. The theoretical part provides a mathematical too...
In principle, the recovery and reconstruction of a 3D object from its 2D view projections require the parameterisation of its shape structure and surface re ectance properties. Exp...
We propose a method to learn heterogeneous models of object classes for visual recognition. The training images contain a preponderance of clutter and learning is unsupervised. Ou...
Significant appearance changes of objects under different orientations could cause loss of tracking, "drifting." In this paper, we present a collaborative tracking framew...