3,434 research outputs found
Improved Shape Parameter Estimation in K Clutter with Neural Networks and Deep Learning
The discrimination of the clutter interfering signal is
a current problem in modern radars’ design, especially in coastal
or offshore environments where the histogram of the background
signal often displays heavy tails. The statistical characterization
of this signal is very important for the cancellation of sea clutter,
whose behavior obeys a K distribution according to the commonly
accepted criterion. By using neural networks, the authors
propose a new method for estimating the K shape parameter,
demonstrating its superiority over the classic alternative based on
the Method of Moments. Whereas both solutions have a similar
performance when the entire range of possible values of the shape
parameter is evaluated, the neuronal alternative achieves a much
more accurate estimation for the lower Fig.s of the parameter. This
is exactly the desired behavior because the best estimate occurs
for the most aggressive states of sea clutter. The final design,
reached by processing three different sets of computer generated
K samples, used a total of nine neural networks whose contribution
is synthesized in the final estimate, thus the solution can be
interpreted as a deep learning approximation. The results are to
be applied in the improvement of radar detectors, particularly for
maintaining the operational false alarm probability close to the
one conceived in the design
Recovering 6D Object Pose and Predicting Next-Best-View in the Crowd
Object detection and 6D pose estimation in the crowd (scenes with multiple
object instances, severe foreground occlusions and background distractors), has
become an important problem in many rapidly evolving technological areas such
as robotics and augmented reality. Single shot-based 6D pose estimators with
manually designed features are still unable to tackle the above challenges,
motivating the research towards unsupervised feature learning and
next-best-view estimation. In this work, we present a complete framework for
both single shot-based 6D object pose estimation and next-best-view prediction
based on Hough Forests, the state of the art object pose estimator that
performs classification and regression jointly. Rather than using manually
designed features we a) propose an unsupervised feature learnt from
depth-invariant patches using a Sparse Autoencoder and b) offer an extensive
evaluation of various state of the art features. Furthermore, taking advantage
of the clustering performed in the leaf nodes of Hough Forests, we learn to
estimate the reduction of uncertainty in other views, formulating the problem
of selecting the next-best-view. To further improve pose estimation, we propose
an improved joint registration and hypotheses verification module as a final
refinement step to reject false detections. We provide two additional
challenging datasets inspired from realistic scenarios to extensively evaluate
the state of the art and our framework. One is related to domestic environments
and the other depicts a bin-picking scenario mostly found in industrial
settings. We show that our framework significantly outperforms state of the art
both on public and on our datasets.Comment: CVPR 2016 accepted paper, project page:
http://www.iis.ee.ic.ac.uk/rkouskou/6D_NBV.htm
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