628 research outputs found
Ship Wake Detection in SAR Images via Sparse Regularization
In order to analyse synthetic aperture radar (SAR) images of the sea surface,
ship wake detection is essential for extracting information on the wake
generating vessels. One possibility is to assume a linear model for wakes, in
which case detection approaches are based on transforms such as Radon and
Hough. These express the bright (dark) lines as peak (trough) points in the
transform domain. In this paper, ship wake detection is posed as an inverse
problem, which the associated cost function including a sparsity enforcing
penalty, i.e. the generalized minimax concave (GMC) function. Despite being a
non-convex regularizer, the GMC penalty enforces the overall cost function to
be convex. The proposed solution is based on a Bayesian formulation, whereby
the point estimates are recovered using maximum a posteriori (MAP) estimation.
To quantify the performance of the proposed method, various types of SAR images
are used, corresponding to TerraSAR-X, COSMO-SkyMed, Sentinel-1, and ALOS2. The
performance of various priors in solving the proposed inverse problem is first
studied by investigating the GMC along with the L1, Lp, nuclear and total
variation (TV) norms. We show that the GMC achieves the best results and we
subsequently study the merits of the corresponding method in comparison to two
state-of-the-art approaches for ship wake detection. The results show that our
proposed technique offers the best performance by achieving 80% success rate.Comment: 18 page
Image Fusion via Sparse Regularization with Non-Convex Penalties
The L1 norm regularized least squares method is often used for finding sparse
approximate solutions and is widely used in 1-D signal restoration. Basis
pursuit denoising (BPD) performs noise reduction in this way. However, the
shortcoming of using L1 norm regularization is the underestimation of the true
solution. Recently, a class of non-convex penalties have been proposed to
improve this situation. This kind of penalty function is non-convex itself, but
preserves the convexity property of the whole cost function. This approach has
been confirmed to offer good performance in 1-D signal denoising. This paper
demonstrates the aforementioned method to 2-D signals (images) and applies it
to multisensor image fusion. The problem is posed as an inverse one and a
corresponding cost function is judiciously designed to include two data
attachment terms. The whole cost function is proved to be convex upon suitably
choosing the non-convex penalty, so that the cost function minimization can be
tackled by convex optimization approaches, which comprise simple computations.
The performance of the proposed method is benchmarked against a number of
state-of-the-art image fusion techniques and superior performance is
demonstrated both visually and in terms of various assessment measures
DRAWING UP THE FINANCIAL BUDGET IN THE CASE OF A PUBLIC TRANSPORTATION COMPANY
The drawing up of the companies’ budget represents one of the mostimportant instruments through which is accomplished an efficieny management, theevaluation of companies’ performances and personnel motivatio. Public localtransportation companies usually use budgets as comparison benchmarks for theperformances they obtain in their current activity. These bidgets represent anessential part in the unfolding of each company’s activity. This paper presents thedrawing up of the financial budgets with a public local transportation company,referring to the drawing up of purchase budgets, investments budgets and cash flowsbudgets.financial management, budget, financial budget, profit center
On Solving SAR Imaging Inverse Problems Using Non-Convex Regularization with a Cauchy-based Penalty
Synthetic aperture radar (SAR) imagery can provide useful information in a
multitude of applications, including climate change, environmental monitoring,
meteorology, high dimensional mapping, ship monitoring, or planetary
exploration. In this paper, we investigate solutions to a number of inverse
problems encountered in SAR imaging. We propose a convex proximal splitting
method for the optimization of a cost function that includes a non-convex
Cauchy-based penalty. The convergence of the overall cost function optimization
is ensured through careful selection of model parameters within a
forward-backward (FB) algorithm. The performance of the proposed penalty
function is evaluated by solving three standard SAR imaging inverse problems,
including super-resolution, image formation, and despeckling, as well as ship
wake detection for maritime applications. The proposed method is compared to
several methods employing classical penalty functions such as total variation
() and norms, and to the generalized minimax-concave (GMC) penalty.
We show that the proposed Cauchy-based penalty function leads to better image
reconstruction results when compared to the reference penalty functions for all
SAR imaging inverse problems in this paper.Comment: 18 pages, 7 figure
Mitigating the effects of atmospheric distortion using DT-CWT fusion
This paper describes a new method for mitigating the effects of atmospheric distortion on observed images, particularly airborne turbulence which degrades a region of interest (ROI). In order to provide accurate detail from objects behind the dis-torting layer, a simple and efficient frame selection method is proposed to pick informative ROIs from only good-quality frames. We solve the space-variant distortion problem using region-based fusion based on the Dual Tree Complex Wavelet Transform (DT-CWT). We also propose an object alignment method for pre-processing the ROI since this can exhibit sig-nificant offsets and distortions between frames. Simple haze removal is used as the final step. The proposed method per-forms very well with atmospherically distorted videos and outperforms other existing methods. Index Terms — Image restoration, fusion, DT-CWT 1
Detection of Ship Wakes in SAR Imagery Using Cauchy Regularisation
Ship wake detection is of great importance in the characterisation of
synthetic aperture radar (SAR) images of the ocean surface since wakes usually
carry essential information about vessels. Most detection methods exploit the
linear characteristics of the ship wakes and transform the lines in the spatial
domain into bright or dark points in a transform domain, such as the Radon or
Hough transforms. This paper proposes an innovative ship wake detection method
based on sparse regularisation to obtain the Radon transform of the SAR image,
in which the linear features are enhanced. The corresponding cost function
utilizes the Cauchy prior, and on this basis, the Cauchy proximal operator is
proposed. A Bayesian method, the Moreau-Yoshida unadjusted Langevin algorithm
(MYULA), which is computationally efficient and robust is used to estimate the
image in the transform domain by minimizing the negative log-posterior
distribution. The detection accuracy of the Cauchy prior based approach is
86.7%, which is demonstrated by experiments over six COSMO-SkyMed images.Comment: 9 pages, 2 Figures and 2 Table
SVM-based texture classification in optical coherence tomography
This paper describes a new method for automated texture classification for glaucoma detection using high resolution retinal Optical Coherence Tomography (OCT). OCT is a non-invasive technique that produces cross-sectional imagery of ocular tissue. Here, we exploit information from OCT im-ages, specifically the inner retinal layer thickness and speckle patterns, to detect glaucoma. The proposed method relies on support vector machines (SVM), while principal component analysis (PCA) is also employed to improve classification performance. Results show that texture features can improve classification accuracy over what is achieved using only layer thickness as existing methods currently do. Index Terms — classification, support vector machine, optical coherence tomography, texture 1
Atmospheric turbulence mitigation for sequences with moving objects using recursive image fusion
This paper describes a new method for mitigating the effects of atmospheric
distortion on observed sequences that include large moving objects. In order to
provide accurate detail from objects behind the distorting layer, we solve the
space-variant distortion problem using recursive image fusion based on the Dual
Tree Complex Wavelet Transform (DT-CWT). The moving objects are detected and
tracked using the improved Gaussian mixture models (GMM) and Kalman filtering.
New fusion rules are introduced which work on the magnitudes and angles of the
DT-CWT coefficients independently to achieve a sharp image and to reduce
atmospheric distortion, respectively. The subjective results show that the
proposed method achieves better video quality than other existing methods with
competitive speed.Comment: IEEE International Conference on Image Processing 201
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