Massachusetts Institute of Technology

Shell Oil Comapny

John William Fisher, Iii

Guy Rosman

William T Freeman

Daniela Rus

Frederic Durand

Neal Wadhwa

John J Leonard

Oral Buyukozturk

Theodericus Johannes Henricus Smit

Justin Gejune Chen

Jason Chang

Zoran Dzunic

James Long

Reza Mohammadi Ghazi

Sergio Daniel Kapusta

Julian Straub

Hossein Mobahi

Oren Freifeld

Structural health monitoring (SHM) is essential but can be expensive to perform. In an embodiment, a method includes sensing vibrations at a plurality of locations of a structure by a plurality of time-synchronized sensors. The method further includes determining a first set of dependencies of all sensors of the time-synchronized sensors at a first sample time to any sensors of a second sample time, and determining a second set of dependencies of all sensors of the time-synchronized sensors at the second sample time to any sensors of a third sample time. The second sample time is later than the first sample time, and the third sample time is later than the second sample time. The method then determines whether the structure has changed if the first set of dependencies is different from the second set of dependencies. Therefore, automated SHM can ensure safety at a lower cost to building owners.

Motion sensing wi-fi sensor networks for continuous 3D modeling and prediction of facility responses to disturbances

A scanner system is configured for acquiring three dimensional image information of an object. The scanner includes a projector, a camera, a graphics processing device, and a processor. The projector projects one of several pre-defined patterns upon the object. The camera captures an image from the object, which is received by the processor. The processor approximates mutual information from the object and the pattern using the graphics processing device, and selects a second pattern for projecting on the object.

System and method for adaptive range 3D scanning

In one embodiment, a method of identifying the dominant orientations of a scene comprises representing a scene as a plurality of directional vectors. The scene may comprise a three-dimensional representation of a scene, and the plurality of directional vectors may comprise a plurality of surface normals. The method further comprises determining, based on the plurality of directional vectors, a plurality of orientations describing the scene. The determined plurality of orientations explains the directionality of the plurality of directional vectors. In certain embodiments, the plurality of orientations may have independent axes of rotation. The plurality of orientations may be determined by representing the plurality of directional vectors as lying on a mathematical representation of a sphere, and inferring the parameters of a statistical model to adapt the plurality of orientations to explain the positioning of the plurality of directional vectors lying on the mathematical representation of the sphere.

System and method for extracting dominant orientations from a scene

In an approach for sampling a distribution of segmentations of an image, the segmentations are based on a level set function. For each sample of the distribution, the sampling includes the following, for each of multiple iterations: randomly selecting a set of locations of the level set function, determining a domain of allowed perturbations of the level set function at the selected set of locations, and randomly accepting a perturbation of the level set function according to a criterion corresponding to a biased distribution over the domain of allowed perturbations of the level set function, wherein the bias is selected to increase a probability of accepting the perturbation. The sampling also includes determining the sample of the distribution of segmentations according to a perturbed level set function determined in a final iteration of the multiple iterations for the sample.

Efficient MCMC sampling with implicit shape representations

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Chestnut Hill, MA, United States of America

John William Fisher, Iii