We propose a moving objects segmentation method for color image sequences based on the piecewise constant Mumford-Shah model (also known as the C-V model) solving by the semi-implicit additive operator splitting (AOS) scheme, which is unconditionally stable, fast, and easy to implement. The method first uses the Gaussian mixture model for background modeling and then subtracts the background to obtain the moving regions that are the handling objects of our method. As a result of the introduction of the AOS scheme, we could use a rather large time step and still maintain the stability of the evolution process. Additionally, the method can easily be parallelized because the AOS scheme decomposes the equations into a sequence of one-dimensional (1-D) systems. The experimental results demonstrate that under real moving objects video tests, the AOS scheme accelerates the evolution of the curve and significantly reduces the number of iterations, and also demonstrates the validity of our method.
In this paper, we proposed a method is used to estimate an aircraft's own position in flight depending on acquisition
and tracking an enlarged landmark in an image sequence. Acquisition operation is based on matching 2D template
with a 2D scene of the landmark area generated by the aircraft seeker. After the landmark location is acquired,
tracking the center point of the landmark maintains the slant range estimate between the sensor and the landmark
which is calculated depending on data of three successive positions from the measured IMU/ GPS information. Then,
we obtain the absolute position of the aircraft in the world coordinate system (WCS) with the transformation from
the landmark coordinate system (LCS) into the WCS. Finally, bias errors of the Inertial Navigation are corrected for
navigation of the aircraft.