2,644 research outputs found
Performance Evaluation of Vision-Based Algorithms for MAVs
An important focus of current research in the field of Micro Aerial Vehicles
(MAVs) is to increase the safety of their operation in general unstructured
environments. Especially indoors, where GPS cannot be used for localization,
reliable algorithms for localization and mapping of the environment are
necessary in order to keep an MAV airborne safely. In this paper, we compare
vision-based real-time capable methods for localization and mapping and point
out their strengths and weaknesses. Additionally, we describe algorithms for
state estimation, control and navigation, which use the localization and
mapping results of our vision-based algorithms as input.Comment: Presented at OAGM Workshop, 2015 (arXiv:1505.01065
Precise Particle Tracking Against a Complicated Background: Polynomial Fitting with Gaussian Weight
We present a new particle tracking software algorithm designed to accurately
track the motion of low-contrast particles against a background with large
variations in light levels. The method is based on a polynomial fit of the
intensity around each feature point, weighted by a Gaussian function of the
distance from the centre, and is especially suitable for tracking endogeneous
particles in the cell, imaged with bright field, phase contrast or fluorescence
optical microscopy. Furthermore, the method can simultaneously track particles
of all different sizes, and allows significant freedom in their shape. The
algorithm is evaluated using the quantitative measures of accuracy and
precision of previous authors, using simulated images at variable
signal-to-noise ratios. To these we add a new test of the error due to a
non-uniform background. Finally the tracking of particles in real cell images
is demonstrated. The method is made freely available for non-commencial use as
a software package with a graphical user-inferface, which can be run within the
Matlab programming environment
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