249 research outputs found
Digital holography-based 3D particle localization for single-molecule tweezer techniques
We present a three-dimensional (3D) imaging technique for the fast tracking of microscopic objects in a fluid environment. Our technique couples digital holographic microscopy with three-dimensional localization via parabolic masking. Compared with existing approaches, our method reconstructs 3D volumes from single-plane images, which greatly simplifies image acquisition, reduces the demand on microscope hardware, and facilitates tracking higher densities of microscopic particles while maintaining similar levels of precision. We demonstrate utility of this method in magnetic tweezer experiments, opening their use to multiplexed single-molecule force spectroscopy assays, which were previously limited by particle crowding and fast dissociation times. We propose that our technique will also be useful in other applications that involve the tracking of microscopic objects in three dimensions, such as studies of microorganism motility and 3D flow characterization of microfluidic devices
Automatic Action Annotation in Weakly Labeled Videos
Manual spatio-temporal annotation of human action in videos is laborious,
requires several annotators and contains human biases. In this paper, we
present a weakly supervised approach to automatically obtain spatio-temporal
annotations of an actor in action videos. We first obtain a large number of
action proposals in each video. To capture a few most representative action
proposals in each video and evade processing thousands of them, we rank them
using optical flow and saliency in a 3D-MRF based framework and select a few
proposals using MAP based proposal subset selection method. We demonstrate that
this ranking preserves the high quality action proposals. Several such
proposals are generated for each video of the same action. Our next challenge
is to iteratively select one proposal from each video so that all proposals are
globally consistent. We formulate this as Generalized Maximum Clique Graph
problem using shape, global and fine grained similarity of proposals across the
videos. The output of our method is the most action representative proposals
from each video. Our method can also annotate multiple instances of the same
action in a video. We have validated our approach on three challenging action
datasets: UCF Sport, sub-JHMDB and THUMOS'13 and have obtained promising
results compared to several baseline methods. Moreover, on UCF Sports, we
demonstrate that action classifiers trained on these automatically obtained
spatio-temporal annotations have comparable performance to the classifiers
trained on ground truth annotation
Localization and tracking of parameterized objects in point clouds
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 43-46).This thesis focuses on object recognition and tracking from three dimensional point cloud renderings of dense range and bearing data. Sensors like laser range-finders and depth cameras have become increasingly popular in autonomous robotic applications. A common task is to locate and track specific objects of interest located somewhere in the point cloud. This often introduces a tedious network of heuristics to build objects from identified primitives or an intractable high dimensional search space. Through a parameterized object model and certain relaxation functions, a likelihood based view of the data can be used to accomplish these goals with increased performance and reliability. Improvements in mathematics and convergence properties have shown that this method can be realized in real time.by Robert Truax.S.M
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