activity
20182021
most citedEfficient Spatially Adaptive Convolution and Correlation

2 citations · 2 across the 1 of their papers we have counts for

collaborators

5 papers

cs.CV2021

Field Convolutions for Surface CNNs

Thomas W. Mitchel, Vladimir G. Kim, Michael Kazhdan

We present a novel surface convolution operator acting on vector fields that is based on a simple observation: instead of combining neighboring features with respect to a single co…

cs.CV20202 cited

Efficient Spatially Adaptive Convolution and Correlation

Thomas W. Mitchel, Benedict Brown, David Koller +3

Fast methods for convolution and correlation underlie a variety of applications in computer vision and graphics, including efficient filtering, analysis, and simulation. However, s…

cs.CV2019

Quotienting Impertinent Camera Kinematics for 3D Video Stabilization

Thomas W. Mitchel, Christian Wuelker, Jin Seob Kim +2

With the recent advent of methods that allow for real-time computation, dense 3D flows have become a viable basis for fast camera motion estimation. Most importantly, dense flows a…

cs.CV2018

Signal Alignment for Humanoid Skeletons via the Globally Optimal Reparameterization Algorithm

Thomas W. Mitchel, Sipu Ruan, Gregory S. Chirikjian

The general ability to analyze and classify the 3D kinematics of the human form is an essential step in the development of socially adept humanoid robots. A variety of different ty…

cs.CV2018

The Globally Optimal Reparameterization Algorithm: an Alternative to Fast Dynamic Time Warping for Action Recognition in Video Sequences

Thomas Mitchel, Sipu Ruan, Yixin Gao +1

Signal alignment has become a popular problem in robotics due in part to its fundamental role in action recognition. Currently, the most successful algorithms for signal alignment…