We explore the use of clouds as a form of structured lighting to capture the 3D structure of outdoor scenes observed over time from a static camera. We derive two cues that relate ...
We introduce a novel data-driven mean-shift belief propagation
(DDMSBP) method for non-Gaussian MRFs, which
often arise in computer vision applications. With the aid
of scale sp...
Vision-based road detection is important in different areas of
computer vision such as autonomous driving, car collision warning
and pedestrian crossing detection. However, curre...
Although not commonly used, correlation filters can track complex objects through rotations, occlusions and other distractions at over 20 times the rate of current state-ofthe-ar...
David Bolme, J Ross Beveridge, Bruce Draper, Yui M...
Despite impressive progress in people detection the performance on challenging datasets like Caltech Pedestrians or TUD-Brussels is still unsatisfactory. In this work we show that...
Stefan Walk, Nikodem Majer, Konrad Schindler, Bern...
There has been a growing interest in exploiting contextual information in addition to local features to detect and localize multiple object categories in an image. Context models ...
Myung Jin Choi, Joseph Lim, Antonio Torralba, Alan...
Interpolated images have data redundancy, and special correlation exists among neighboring pixels, which is a crucial clue in digital forensics. We design a neural network based f...
This paper introduces a method to correct over-exposure in an existing photograph by recovering the color and lightness separately. First, the dynamic range of well exposed region...
Detecting objects in cluttered scenes and estimating articulated human body parts are two challenging problems in computer vision. The difficulty is particularly pronounced in ac...
We formulate a layered model for object detection and multi-class segmentation. Our system uses the output of a bank of object detectors in order to define shape priors for suppo...
Yi Yang, Sam Hallman, Deva Ramanan, Charless Fowlk...