Aselsan Elektronik Sanayi Ve Ticaret Anonim Sirketi

Ozgur Yilmaz

Mustafa Yagcioglu

Erkan Yavuz

Erkan Okuyan

Selcuk Sandikci

Recurrent neural networks are powerful tools for handling incomplete data problems in machine learning thanks to their significant generative capabilities. However, the computational demand for algorithms to work in real time applications requires specialized hardware and software solutions. We disclose a method for adding recurrent processing capabilities into a feedforward network without sacrificing much from computational efficiency. We assume a mixture model and generate samples of the last hidden layer according to the class decisions of the output layer, modify the hidden layer activity using the samples, and propagate to lower layers. For an incomplete data problem, the iterative procedure emulates feedforward-feedback loop, filling-in the missing hidden layer activity with meaningful representations.

Method for pseudo-recurrent processing of data using a feedforward neural network architecture

The present invention relates to a simple and effective system and method for measuring camera based tracker system accuracy, especially for a helmet-mounted tracker system, utilizing Coordinate Measuring Machine (CMM). The method comprises the steps of; computing spatial relation between tracked object and calibration pattern using CMM; computing relation between reference camera and tracker camera; computing relation between reference camera and calibration pattern; computing ground truth relation between tracker camera and tracked object; obtaining actual tracker system results; comparing these results with the ground truth relations and finding accuracy of the tracker system; recording accuracy results; testing if the accuracy results is a new calculation required. The system comprises; a reference camera; a calibration pattern visible by reference camera; a camera spatial relation computation unit; a relative spatial relation computation unit a memory unit; a spatial relation comparison unit.

System and method for measuring tracker system accuracy

A method for optimizing fiducial marker and camera positions/orientations is realized to simulate camera and fiducial positions and pose estimation algorithm to find best possible marker/camera placement comprises the steps of: acquiring mesh data representing possible camera positions and feasible orientation boundaries of cameras on the environment of tracked object; acquiring mesh data representing possible active marker positions and feasible orientation placements of markers on a tracked object; pose data representing possible poses of tracked object under working conditions; initializing the control parameter for camera placement; create initial solution strings for camera placement; solving marker placement problem for the current camera placement; evaluating the quality of the current LED and camera placement taking pose coverage, pose accuracy, number of placed markers, number of placed camera etc. into account; determining if a stopping criterion is satisfied.

System and method for optimizing fiducial marker and camera positions/orientations

The present invention relates to the field of computation and simulation and covers methods to optimize the fiducial marker positions in optical object tracking systems, by simulating the visibility. The method for optimizing tracker system which is realized to simulate camera and fiducial positions and pose estimation algorithm parameters to optimize the system comprises the steps of; acquire mesh data representing possible active marker positions and orientations on a tracked object, pose data representing possible poses of tracked object, camera positions and orientations; compute visibility of each node from all camera viewports and generate a visibility value list; select the node with highest visibility count as a marker placement node; remove nodes closer to the selected node than a threshold; remove the pose(s) having a predetermined number of selected nodes; does percentage of all poses have predetermined number of selected nodes?; project selected node positions on the image plane of each camera viewport and calculate the pose of the mesh using the tracker algorithm to be optimized; calculate pose error and pose coverage by comparing algorithm results with initial data; record and output results; and select among the results a parameter set satisfying at least one constraint.

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Ankara, Turkey

Ozgur Yilmaz