This paper presents a complete analytical characterization of a large class of central and non-central imaging devices dubbed linear cameras by Ponce [9]. Pajdla [7] has shown tha...
We present an imaging framework to acquire 3D surface scans at ultra high-resolutions (exceeding 600 samples per mm2 ). Our approach couples a standard structured-light setup and ...
Zheng Lu, Yu-Wing Tai, Moshe Ben-Ezra, Michael Bro...
For object category recognition to scale beyond a small number of classes, it is important that algorithms be able to learn from a small amount of labeled data per additional clas...
Kevin Tang, Marshall Tappen, Rahul Sukthankar, Chr...
Modeling moving and deforming objects requires capturing as much information as possible during a very short time. When using off-the-shelf hardware, this often hinders the resolu...
Food recognition is difficult because food items are deformable objects that exhibit significant variations in appearance. We believe the key to recognizing food is to exploit the...
Shulin Yang, Mei Chen, Dean Pomerleau, Rahul Sukth...
Dynamic programming (DP) has been a useful tool for a variety of computer vision problems. However its application is usually limited to problems with a one dimensional or low tre...
Graph cuts methods are at the core of many state-of-theart algorithms in computer vision due to their efficiency in computing globally optimal solutions. In this paper, we solve t...
We present a passive computer vision method that exploits existing mapping and navigation databases in order to automatically create 3D building models. Our method defines a gramm...
We present an active learning framework to simultaneously learn appearance and contextual models for scene understanding tasks (multi-class classification). Existing multi-class a...
We present an algorithm that learns invariant features from real data in an entirely unsupervised fashion. The principal benefit of our method is that it can be applied without hu...