9,262 research outputs found
Estimation of the normalized coherency matrix through the SIRV model. Application to high resolution POLSAR data
8 pagesInternational audienceIn the context of non-Gaussian polarimetric clutter models, this paper presents an application of the recent advances in the field of Spherically Invariant Random Vectors (SIRV) modelling for coherency matrix estimation in heterogeneous clutter. The complete description of the POLSAR data set is achieved by estimating the span and the normalized coherency independently. The normalized coherency describes the polarimetric diversity, while the span indicates the total received power. The main advantages of the proposed Fixed Point estimator are that it does not require any "a priori" information about the probability density function of the texture (or span) and it can be directly applied on adaptive neighbourhoods. Interesting results are obtained when coupling this Fixed Point estimator with an adaptive spatial support based on the scalar span information. Based on the SIRV model, a new maximum likelihood distance measure is introduced for unsupervised POLSAR classification. The proposed method is tested with airborne POLSAR images provided by the RAMSES system. Results of entropy/alpha/anisotropy decomposition, followed by unsupervised classification, allow discussing the use of the normalized coherency and the span as two separate descriptors of POLSAR data sets
Scattering statistics of rock outcrops: Model-data comparisons and Bayesian inference using mixture distributions
The probability density function of the acoustic field amplitude scattered by
the seafloor was measured in a rocky environment off the coast of Norway using
a synthetic aperture sonar system, and is reported here in terms of the
probability of false alarm. Interpretation of the measurements focused on
finding appropriate class of statistical models (single versus two-component
mixture models), and on appropriate models within these two classes. It was
found that two-component mixture models performed better than single models.
The two mixture models that performed the best (and had a basis in the physics
of scattering) were a mixture between two K distributions, and a mixture
between a Rayleigh and generalized Pareto distribution. Bayes' theorem was used
to estimate the probability density function of the mixture model parameters.
It was found that the K-K mixture exhibits significant correlation between its
parameters. The mixture between the Rayleigh and generalized Pareto
distributions also had significant parameter correlation, but also contained
multiple modes. We conclude that the mixture between two K distributions is the
most applicable to this dataset.Comment: 15 pages, 7 figures, Accepted to the Journal of the Acoustical
Society of Americ
Fast and Robust Small Infrared Target Detection Using Absolute Directional Mean Difference Algorithm
Infrared small target detection in an infrared search and track (IRST) system
is a challenging task. This situation becomes more complicated when high
gray-intensity structural backgrounds appear in the field of view (FoV) of the
infrared seeker. While the majority of the infrared small target detection
algorithms neglect directional information, in this paper, a directional
approach is presented to suppress structural backgrounds and develop a more
effective detection algorithm. To this end, a similar concept to the average
absolute gray difference (AAGD) is utilized to construct a novel directional
small target detection algorithm called absolute directional mean difference
(ADMD). Also, an efficient implementation procedure is presented for the
proposed algorithm. The proposed algorithm effectively enhances the target area
and eliminates background clutter. Simulation results on real infrared images
prove the significant effectiveness of the proposed algorithm.Comment: The Final version (Accepted in Signal Processing journal
SFNet: Learning Object-aware Semantic Correspondence
We address the problem of semantic correspondence, that is, establishing a
dense flow field between images depicting different instances of the same
object or scene category. We propose to use images annotated with binary
foreground masks and subjected to synthetic geometric deformations to train a
convolutional neural network (CNN) for this task. Using these masks as part of
the supervisory signal offers a good compromise between semantic flow methods,
where the amount of training data is limited by the cost of manually selecting
point correspondences, and semantic alignment ones, where the regression of a
single global geometric transformation between images may be sensitive to
image-specific details such as background clutter. We propose a new CNN
architecture, dubbed SFNet, which implements this idea. It leverages a new and
differentiable version of the argmax function for end-to-end training, with a
loss that combines mask and flow consistency with smoothness terms.
Experimental results demonstrate the effectiveness of our approach, which
significantly outperforms the state of the art on standard benchmarks.Comment: cvpr 2019 oral pape
Convolutional neural network architecture for geometric matching
We address the problem of determining correspondences between two images in
agreement with a geometric model such as an affine or thin-plate spline
transformation, and estimating its parameters. The contributions of this work
are three-fold. First, we propose a convolutional neural network architecture
for geometric matching. The architecture is based on three main components that
mimic the standard steps of feature extraction, matching and simultaneous
inlier detection and model parameter estimation, while being trainable
end-to-end. Second, we demonstrate that the network parameters can be trained
from synthetically generated imagery without the need for manual annotation and
that our matching layer significantly increases generalization capabilities to
never seen before images. Finally, we show that the same model can perform both
instance-level and category-level matching giving state-of-the-art results on
the challenging Proposal Flow dataset.Comment: In 2017 IEEE Conference on Computer Vision and Pattern Recognition
(CVPR 2017
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