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Found 9 papers, 1 papers with code
Table tennis ball spin estimation with an event camera
In table tennis, the combination of high velocity and spin renders traditional low frame rate cameras inadequate for quickly and accurately observing the ball's logo to estimate the spin due to the motion blur.
Spiking Neural Networks for Fast-Moving Object Detection on Neuromorphic Hardware Devices Using an Event-Based Camera
Next to this comparison of SNN solutions for robots, we also show that an SNN on a neuromorphic edge device is able to run in real-time in a closed loop robotic system, a table tennis robot in our use case.
A multi-modal table tennis robot system
Therefore, we introduced a novel, and more accurate spin estimation approach.
eWand: A calibration framework for wide baseline frame-based and event-based camera systems
The standard approach is to use a printed pattern with known geometry to estimate the intrinsic and extrinsic parameters of the cameras.
SpinDOE: A ball spin estimation method for table tennis robot
Using our algorithm, the ball's orientation can be estimated with a mean error of 2. 4{\deg} and the spin estimation has an relative error lower than 1%.
Real-time event simulation with frame-based cameras
However, due to the computational complexity of the simulation, the event streams of existing simulators cannot be generated in real-time but rather have to be pre-calculated from existing video sequences or pre-rendered and then simulated from a virtual 3D scene.
Unbiased split variable selection for random survival forests using maximally selected rank statistics
However, instead of the conditional Monte-Carlo approach used in conditional inference forests, p-value approximations are employed.
ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R
We introduce the C++ application and R package ranger.
On the use of Harrell's C for clinical risk prediction via random survival forests
In simulation studies and with the help of two medical data sets we demonstrate that the accuracy of RSF predictions, as measured by Harrell's C, can be improved if the log-rank statistic is replaced by the C index for node splitting.
