This paper proposes a discriminative framework for efficiently aligning images. Although conventional Active Appearance Models (AAM)-based approaches have achieved some success, t...
This paper studies image alignment, the problem of learning a shape and appearance model from labeled data and efficiently fitting the model to a non-rigid object with large varia...
Xiaoming Liu 0002, Ting Yu, Thomas Sebastian, Pete...
We describe results on combining depth information from a laser range-finder and color and texture image cues to segment ill-structured dirt, gravel, and asphalt roads as input t...
This paper presents efficient methods to address the problem of discriminating between five facial orientations. We present the most efficient methods for this task to date, which...
We propose a novel approach to unsupervised facial image
alignment. Differently from previous approaches, that
are confined to affine transformations on either the entire
face o...