Learning from streams of evolving and unbounded data is an important problem, for example in visual surveillance or internet scale data. For such large and evolving real-world data...
Chen Change Loy, Timothy M. Hospedales, Tao Xiang,...
Detecting abnormalities in video is a challenging problem since the class of all irregular objects and behaviors is infinite and thus no (or by far not enough) abnormal training sa...
We present a novel method to upsample mobile LiDAR data using panoramic images collected in urban environments. Our method differs from existing methods in the following aspects: ...
Ruisheng Wang, Jeff Bach, Jane Macfarlane, Frank P...
Crowd-sourcing tools such as Mechanical Turk are popular for annotation of large scale image data sets. Typically, these annotations consist of bounding boxes or coarse outlines o...
The use of depth is becoming increasingly popular in real-time computer vision applications. However, when using real-time stereo for depth, the quality of the disparity image is ...
We present a novel algorithm that takes as input an uncalibrated unordered set of spherical panoramic images and outputs their relative pose up to a global scale. The panoramas co...
The growing ubiquity of cameras in hand-held devices and the prevalence of electronic displays in signage creates a novel framework for wireless communications. Traditionally, the...
Wenjia Yuan, Kristin J. Dana, Ashwin Ashok, Marco ...
We present a novel algorithm, Compact Kd-Trees (CompactKdt), that achieves state-of-the-art performance in searching large scale object image collections. The algorithm uses an or...