Automatic Parameter Estimation Research Articles

A Polarimetric Extension of Low-Rank Plus Sparse Decomposition and Radon Transform for Ship Wake Detection in Synthetic Aperture Radar Images

In past research, the problem of obtaining stable motion estimation of maritime targets in sea clutter making wake structure detection and reconnaissance difficult has been tackled. This new research presents an upgrade for automatic estimation of maritime target motion parameters by evaluating the generated Kelvin waves detected in synthetic aperture radar (SAR) images. The algorithm consists in considering the polarimetric (Pol) information of SAR images and evaluating a multiple-channel and dual-stage Pol low-rank plus sparse decomposition (Pol-LRSD) assisted by Radon transform (RT) for clutter reduction, sparse object detection, precise wake inclination estimation, and targets classification. This upgraded algorithm is based on Pol robust principal component analysis (Pol-RPCA) implemented by convex programming. This upgraded Pol-LRSD algorithm permits the extrapolation of the Pol signature of sparse objects of interest consisting of the maritime targets and the Kelvin pattern from the unchanging low-rank background. Pol-RPCA and RT methods applied to Pol SAR surveillance permit more precise detection and segmentation of maritime targets.

On-Barn Pig Weight Estimation Based on Body Measurements by Structure-from-Motion (SfM).

Information on the body shape of pigs is a key indicator to monitor their performance and health and to control or predict their market weight. Manual measurements are among the most common ways to obtain an indication of animal growth. However, this approach is laborious and difficult, and it may be stressful for both the pigs and the stockman. The present paper proposes the implementation of a Structure from Motion (SfM) photogrammetry approach as a new tool for on-barn animal reconstruction applications. This is possible also to new software tools allowing automatic estimation of camera parameters during the reconstruction process even without a preliminary calibration phase. An analysis on pig body 3D SfM characterization is here proposed, carried out under different conditions in terms of number of camera poses and animal movements. The work takes advantage of the total reconstructed surface as reference index to quantify the quality of the achieved 3D reconstruction, showing how as much as 80% of the total animal area can be characterized.

Measuring transducers for optical diagnostic system with multifunctional unitary photovoltaic converters

Modern measuring transducers for optical diagnostic system should perform automatic parameter estimation of optical signal and automatic switching between different energetic and optical sensitivity ranges. Traditional solution of this problem lies in the field of multi-sensory systems, complex optical schemes and complex signal processing algorithms. The paper aims at the development of new measuring transducers for optical diagnostic system on a basis of multifunctional unitary photovoltaic converters built on semiconductors with low-concentration deep dopants that form multiple energy levels for different charge states in the band gap. Relative complexity of physical processes accompanying the recharge of several energy levels of multiply-charged deep dopant makes it possible to realize the multifunctionality of a photoelectric converter albeit simple sensor design.The proposed unitary photovoltaic converters proved to have extended functional characteristics and increased ranges of energetic characteristic (by dozens dB) and spectral sensitivity characteristic with possible shifts of red margin by 2 to 4 μm in the spectral sensitivity range of 1–10 μm. Energetic and spectral sensitivity characteristic ranges could be switched either by measurement signal itself or by additional control inputs. Possible materials for resistive or barrier photovoltaic converter structure are Germanium, Silicon, А3В5 systems and other semiconductors including that compatible with «non-silicon» technologies and structures on sapphire substrate.

A PHOTOGRAMMETRY-BASED STRUCTURE FROM MOTION ALGORITHM USING ROBUST ITERATIVE BUNDLE ADJUSTMENT TECHNIQUES

Abstract. The purpose of this paper is the presentation of a novel algorithm for automatic estimation of the exterior orientation parameters of image datasets, which can be applied in the case that the scene depicted in the images has a planar surface (e.g., roof of a building). The algorithm requires the measurement of four coplanar ground control points (GCPs) in only one image. It uses a template matching method combined with a homography-based technique for transfer of the GCPs in another image, along with an incremental photogrammetry-based Structure from Motion (SfM) workflow, coupled with robust iterative bundle adjustment methods that reject any remaining outliers, which have passed through the checks and geometric constraints imposed during the image matching procedure. Its main steps consist of (i) determination of overlapping images without the need for GPS/INS data; (ii) image matching and feature tracking; (iii) estimation of the exterior orientation parameters of a starting image pair; and (iv) photogrammetry-based SfM combined with iterative bundle adjustment methods. A developed software solution implementing the proposed algorithm was tested using a set of UAV oblique images. Several tests were performed for the assessment of the errors and comparisons with well-established commercial software were made, in terms of automation and correctness of the computed exterior orientation parameters. The results show that the estimated orientation parameters via the proposed solution have comparable accuracy with those ones computed through the commercial software using the highest possible accuracy settings; in addition, double manual work was required by the commercial software compared to the proposed solution.

Hydrogeological Study of the Glacial—Fluvioglacial Territory of Grandate (Como, Italy) and Stochastical Modeling of Groundwater Rising

On November 2014, the Municipality of Grandate, near Lake Como, had to deal with a great emergency that was caused by the flooding of factory undergrounds. The authors realized a hydrogeological study to understand the causes of groundwater flooding and to prepare a pre-feasibility study concerning possible actions for groundwater control. The hydrogeological structure is rather complex and required time-consuming reconstruction of the conceptual site model. A transient numerical model was developed to analyse the system behaviour in different scenarios. The flow model was calibrated in a steady and unsteady-state using the automatic calibration code Model-Independent Parameter Estimation (PEST). The study demonstrated that the reason for floods was mainly due to the concurrence of three causes: (1) the hydrogeological structure of the area was recognized as a stagnation zone, (2) groundwater rising, and (3) extremely heavy rainfall in 2014. Through the PEST RandPar function, 100 random rainfall scenarios were generated starting from rainfall data for the last 20 years. The model was used to run 100 1-year long simulations considering the probability distribution of recharge related to the 100 randomly generated rainfall scenarios. Through collecting the piezometric heads that resulted from the simulations, monthly probability curves of groundwater exceeding a threshold level were obtained. The results provided an occurrence probability of groundwater level exceeding the underground structures level between 12% and 15%.

DETERMINING GEOMETRIC PARAMETERS OF AGRICULTURAL TREES FROM LASER SCANNING DATA OBTAINED WITH UNMANNED AERIAL VEHICLE

Abstract. The estimation of dendrometric parameters has become an important issue for agriculture planning and for the efficient management of orchards. Airborne Laser Scanning (ALS) data is widely used in forestry and many algorithms for automatic estimation of dendrometric parameters of individual forest trees were developed. Unfortunately, due to significant differences between forest and fruit trees, some contradictions exist against adopting the achievements of forestry science to agricultural studies indiscriminately. In this study we present the methodology to identify individual trees in apple orchard and estimate heights of individual trees, using high-density LiDAR data (3200 points/m2) obtained with Unmanned Aerial Vehicle (UAV) equipped with Velodyne HDL32-E sensor. The processing strategy combines the alpha-shape algorithm, principal component analysis (PCA) and detection of local minima. The alpha-shape algorithm is used to separate tree rows. In order to separate trees in a single row, we detect local minima on the canopy profile and slice polygons from alpha-shape results. We successfully separated 92 % of trees in the test area. 6 % of trees in orchard were not separated from each other and 2 % were sliced into two polygons. The RMSE of tree heights determined from the point clouds compared to field measurements was equal to 0.09 m, and the correlation coefficient was equal to 0.96. The results confirm the usefulness of LiDAR data from UAV platform in orchard inventory.

All-Optical Electrophysiology for High-Throughput Functional Characterization of a Human iPSC-Derived Motor Neuron Model of ALS.

SummaryHuman induced pluripotent stem cell (iPSC)-derived neurons are an attractive substrate for modeling disease, yet the heterogeneity of these cultures presents a challenge for functional characterization by manual patch-clamp electrophysiology. Here, we describe an optimized all-optical electrophysiology, “Optopatch,” pipeline for high-throughput functional characterization of human iPSC-derived neuronal cultures. We demonstrate the method in a human iPSC-derived motor neuron (iPSC-MN) model of amyotrophic lateral sclerosis (ALS). In a comparison of iPSC-MNs with an ALS-causing mutation (SOD1 A4V) with their genome-corrected controls, the mutants showed elevated spike rates under weak or no stimulus and greater likelihood of entering depolarization block under strong optogenetic stimulus. We compared these results with numerical simulations of simple conductance-based neuronal models and with literature results in this and other iPSC-based models of ALS. Our data and simulations suggest that deficits in slowly activating potassium channels may underlie the changes in electrophysiology in the SOD1 A4V mutation.

Efficient generalized Golub–Kahan based methods for dynamic inverse problems

We consider efficient methods for computing solutions to and estimating uncertainties in dynamic inverse problems, where the parameters of interest may change during the measurement procedure. Compared to static inverse problems, incorporating prior information in both space and time in a Bayesian framework can become computationally intensive, in part, due to the large number of unknown parameters. In these problems, explicit computation of the square root and/or inverse of the prior covariance matrix is not possible, so we consider efficient, iterative, matrix-free methods based on the generalized Golub–Kahan bidiagonalization that allow automatic regularization parameter and variance estimation. We demonstrate that these methods for dynamic inversion can be more flexible than standard methods and develop efficient implementations that can exploit structure in the prior, as well as possible structure in the forward model. Numerical examples from photoacoustic tomography, space-time deblurring, and passive seismic tomography demonstrate the range of applicability and effectiveness of the described approaches. Specifically, in passive seismic tomography, we demonstrate our approach on both synthetic and real data. To demonstrate the scalability of our algorithm, we solve a dynamic inverse problem with approximately measurements and 7.8 million unknowns in under 40 s on a standard desktop.

Low-Rank Plus Sparse Decomposition and Localized Radon Transform for Ship-Wake Detection in Synthetic Aperture Radar Images

The problem in obtaining stable motion estimation of maritime targets is that sea clutter makes wake structure detection and reconnaissance difficult. This letter presents a complete procedure for the automatic estimation of maritime target motion parameters by evaluating the generated Kelvin waves detected in synthetic aperture radar (SAR) images. The algorithm consists in evaluating a dual-stage low-rank plus sparse decomposition (LRSD) assisted by Radon transform (RT) for clutter reduction, sparse object detection, precise wake inclination estimation, and Kelvin wave spectral analysis. The algorithm is based on the robust principal component analysis (RPCA) implemented by convex programming. The LRSD algorithm permits the extrapolation of sparse objects of interest consisting of the maritime targets and the Kelvin pattern from the unchanging low-rank background. This dual-stage RPCA and RT applied to SAR surveillance permits fast detection and enhanced motion parameter estimation of maritime targets.

Polarization defogging method for color image based on automatic estimation of global parameters

Polarization defogging method for color image based on automatic estimation of global parameters

Reply to Comment on “Automatic estimation of aquifer parameters using long-term water supply pumping and injection records”: paper published in Hydrogeology Journal (2016) 24: 1443–1461, by Ning Luo and Walter A. Illman

Reply to Comment on “Automatic estimation of aquifer parameters using long-term water supply pumping and injection records”: paper published in Hydrogeology Journal (2016) 24: 1443–1461, by Ning Luo and Walter A. Illman

Comment on “Automatic estimation of aquifer parameters using long-term water supply pumping and injection records”: paper published in Hydrogeology Journal (2016) 24: 1443–1461, by Ning Luo and Walter A. Illman

Comment on “Automatic estimation of aquifer parameters using long-term water supply pumping and injection records”: paper published in Hydrogeology Journal (2016) 24: 1443–1461, by Ning Luo and Walter A. Illman

Automatic estimation of olive tree dendrometric parameters based on airborne laser scanning data using alpha-shape and principal component analysis

The aim of this study is to present an automatic approach for olive tree dendrometric parameter estimation from airborne laser scanning (ALS) data. The proposed method is based on a unique combination of the alpha-shape algorithm applied to normalized point cloud and principal component analysis. A key issue of the alpha-shape algorithm is to define the α parameter, as it directly affects the crown delineation results. We propose to adjust this parameter based on a group of representative trees in an orchard for which the classical field measurements were performed. The best value of the α parameter is one whose correlation coefficient of dendrometric parameters between field measurements and estimated values is the highest. We determined crown diameters as principal components of ALS points representing a delineated crown. The method was applied to a test area of an olive orchard in Spain. The tree dendrometric parameters estimated from ALS data were compared with field measurements to assess the quality of the developed approach. We found the method to be equally good or even superior to previously investigated semi-automatic methods. The average error is 19% for tree height, 53% for crown base height, and 13% and 9% for the length of the longer diameter and perpendicular diameter, respectively.

Blind Modulation Classification for Cognitive Satellite in the Spectral Coexistence Context

The scarcity of the usable satellite spectrum has led to a need for advanced communication techniques. Among them, multisatellite networks have been promoted for their ability to reuse the frequency bands in a more aggressive manner. This is made possible by sharing both the spectral and spatial degrees of freedom. Meanwhile, the concept of cognitive satellite was deeply investigated to make the best of these novel architectures. In this context, very few blind signal processing methods were proposed to tackle the co-channel interference issues entailed by such systems. Herein, we develop a novel low-complexity algorithm for blind automatic modulation classification and parameters estimation in the strongly interfering scenario. The proposed method relies on an Analytical study of the M ${\text{th}}$ -Power nonlinear Transformation (A $M$ PT) of the co-channel mixture. Theoretical analysis and extensive simulations show the effectiveness of the proposed method in various scenarios.

Automatic estimation of bias parameters of RPC model and ground coordinates from multi-directional satellite images

Advanced Land Observing Satellite (ALOS)/Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) measured triplet images at forward, nadir and backward view directions, and the digital surface model (DSM) is generated from set of triplet images. Existing methods generate multiple DSMs from individual triplet images, and eliminate the effects of outliers by taking average. The proposed method in this paper solves multiple observation equations from all triplet images, and simultaneously determines bias parameters contaminated in the rational polynomial coefficient (RPC) model and ground coordinates. The experimental results showed that errors of the coordinates estimated by using multi-temporal triplet images were almost as small as those estimated with manually determined bias parameters in case of 4 or more sets of triplet images. As a result, we conclude that the proposed method is effective for stably generating accurate DSMs from multi-temporal triplet images.

Improved dynamical scaling analysis using the kernel method for nonequilibrium relaxation.

The dynamical scaling analysis for the Kosterlitz-Thouless transition in the nonequilibrium relaxation method is improved by the use of Bayesian statistics and the kernel method. This allows data to be fitted to a scaling function without using any parametric model function, which makes the results more reliable and reproducible and enables automatic and faster parameter estimation. Applying this method, the bootstrap method is introduced and a numerical discrimination for the transition type is proposed.

An FPGA implementation of a tone mapping algorithm with a halo-reducing filter

In this paper, we present a real-time hardware implementation of an exponent-based tone mapping algorithm of Hore et al., that uses both local and global image information for improving the contrast and increasing the brightness of tone-mapped images. Although there are several tone mapping algorithms available in the literature, most of them require manual tuning of their rendering parameters. However, in our implementation, the algorithm has an embedded automatic key parameter estimation block that controls the brightness of the tone-mapped images. We also present the implementation of a Gaussian-based halo-reducing filter. The hardware implementation is described in Verilog and synthesized for a field programmable gate array device. Experimental results performed on different wide dynamic range images show that we are able to get images which are of good visual quality and have good brightness and contrast. The good performance of our hardware architecture is also confirmed quantitatively with the high peak signal-to-noise ratio and structural similarity index.

Guest Editorial: Scale-Space and Variational Methods

This special issue highlights some recent developments in the field of mathematical image processing. The emphasis of the issue is on the interplay between advanced mathematical methods (such as non-smooth convex optimization, variational methods, partial differential equations, scale-space approaches) and their application in image processing or computer vision. The special issue comprises nine papers representing state-of-the-art research on these topics as outlined below. Non-smooth and possibly non-convex optimization as well as automatic parameter estimation have become major trends of research in the community. In this context, P. Ochs, R. Ranftl, T. Brox and T. Pock propose a new approach for bilevel optimization. In “Techniques for Gradient-Based Bilevel Optimization with Non-smooth Lower Level Problems” (doi:10.1007/s10851-016-0663-7), they show how solving non-smooth optimization problems while computingoptimal regularization parameters by considering suitable nonlinear proximal distance functions. In “Convex ImageDenoising viaNon-convexRegularization withParameter Selection” (doi:10.1007/s10851-016-0655-7), A. Lanza, S. Morigi, and F. Sgallari introduce a novel

A Framework for Assessing Liquefaction Hazard for Urban Areas Based on Soil Dynamics

Soil liquefaction induced by earthquakes poses a great threat to urban areas, not only by causing direct damages to infrastructures but also by delaying rescue and relief actions. Established engineering assessment methods using indices such as factor of safety against liquefaction (FL) tend to overestimate liquefaction hazards. While the standard usage of soil dynamic analysis for liquefaction assessment is limited to a single site with detailed site information. In this paper, we propose a framework based on soil dynamics for assessing liquefaction hazard for urban areas which contain numerous sites with borehole logs only. The framework is featured by generating analysis models and by estimating material parameters automatically from available borehole information. The governing equations for soil dynamics are solved by a finite element code. Liquefaction hazard is assessed according to the solutions of excess pore water pressure for given seismic waves. As demonstrations, we show simulation results of simple shear tests and of seismic response for a target site under loadings of artificial and of actual seismic waves. The automatic model construction and parameter estimation enable this framework being applied effectively for urban-area-wide liquefaction assessment, as an alternative for the current practices based on engineering indices.

Effect of positive end-expiratory pressure on pulmonary shunt and dynamic compliance during abdominal surgery

BackgroundGeneral anaesthesia decreases pulmonary compliance and increases pulmonary shunt due to the development of atelectasis. The presence of capnoperitoneum during laparoscopic surgery may further decrease functional residual capacity, promoting an increased amount of atelectasis compared with laparotomy. The aim of this study was to evaluate the effects of different levels of positive end-expiratory pressure (PEEP) in both types of surgery and to investigate whether higher levels of PEEP should be used during laparoscopic surgery. MethodsThis prospective observational study included 52 patients undergoing either laparotomy or laparoscopic surgery. Three levels of PEEP were applied in random order: (1) zero (ZEEP), (2) 5 cmH2O and (3) 10 cmH2O. Pulmonary shunt and ventilation/perfusion mismatch were assessed by the automatic lung parameter estimator system. ResultsPulmonary shunt was similar in both groups. However, in laparotomy, a PEEP of 5 cmH2O significantly decreased shunt when compared with ZEEP (12 vs 6%; P+0.001), with additional PEEP having no further effect. In laparoscopic surgery, a significant reduction in shunt (13 vs 6%; P+0.001) was obtained only at a PEEP of 10 cmH2O. Although laparoscopic surgery was associated with a lower pulmonary compliance, increasing levels of PEEP were able to ameliorate it in both groups. ConclusionBoth surgeries have similar negative effects on pulmonary shunt, while the presence of capnoperitoneum reduced only the pulmonary compliance. It appears that a more aggressive PEEP level is required to reduce shunt and to maximize compliance in case of laparoscopic surgery.