Hierarchical conditional random fields have been successfully applied to object segmentation. One reason is their ability to incorporate contextual information at different scales....
Xavier Boix, Josep M. Gonfaus, Joost van de Weijer...
Many feature detection algorithms rely on the choice of scale. In this paper, we complement standard scaleselection algorithms with spatial regularization. To this end, we formula...
Given a set of algorithms, which one(s) should you apply to, i) compute optical flow, or ii) perform feature matching? Would looking at the sequence in question help you decide? I...
Many computer vision problems rely on computing histogram-based objective functions with a sliding window. A main limiting factor is the high computational cost. Existing computat...
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...