Automatic Stopping Criterion Research Articles

RF-Net 2: fast inference of virus reassortment and hybridization networks.

A phylogenetic network is a powerful model to represent entangled evolutionary histories with both divergent (speciation) and convergent (e.g. hybridization, reassortment, recombination) evolution. The standard approach to inference of hybridization networks is to (i) reconstruct rooted gene trees and (ii) leverage gene tree discordance for network inference. Recently, we introduced a method called RF-Net for accurate inference of virus reassortment and hybridization networks from input gene trees in the presence of errors commonly found in phylogenetic trees. While RF-Net demonstrated the ability to accurately infer networks with up to four reticulations from erroneous input gene trees, its application was limited by the number of reticulations it could handle in a reasonable amount of time. This limitation is particularly restrictive in the inference of the evolutionary history of segmented RNA viruses such as influenza A virus (IAV), where reassortment is one of the major mechanisms shaping the evolution of these pathogens. Here, we expand the functionality of RF-Net that makes it significantly more applicable in practice. Crucially, we introduce a fast extension to RF-Net, called Fast-RF-Net, that can handle large numbers of reticulations without sacrificing accuracy. In addition, we develop automatic stopping criteria to select the appropriate number of reticulations heuristically and implement a feature for RF-Net to output error-corrected input gene trees. We then conduct a comprehensive study of the original method and its novel extensions and confirm their efficacy in practice using extensive simulation and empirical IAV evolutionary analyses. RF-Net 2 is available at https://github.com/flu-crew/rf-net-2. Supplementary data are available at Bioinformatics online.

A Robust Deconvolution Method of Airborne LiDAR Waveforms for Dense Point Clouds Generation in Forest

The generation of dense and accurate point clouds from airborne light detection and ranging (LiDAR) waveform data is crucial to forest inventory. This work proposes a deconvolution method with: 1) an automatic stopping criterion to differentiate near-adjacent targets and 2) an iterative false subwaveform removal algorithm to remove outliers caused by noise. Synthetic waveforms with different overlap rates were processed using the proposed method, the Gaussian decomposition (GD) method, and the Richardson Lucy (RL) deconvolution method. Results showed that: 1) the number of subwaveforms detected by the proposed method is 9% higher than that of the RL and 20% higher than that of the GD when the overlap rates are larger than 0.6 and 2) the proposed method is of the smallest ground and peak distance errors. Results of the indoor experiment also show that the proposed method is superior in finding near targets meanwhile leading to small ground and peak distance error. Furthermore, the proposed method was tested by airborne waveforms from the Dagujia forest farm. The point cloud density acquired by the proposed method is 3% and 35% higher than that by the RL and GD method. Fewer outliers are produced by the proposed method. The number of individual trees extracted from the proposed point clouds is 22%, 51%, and 57% greater than those extracted from the RL, the GD, and the reference point clouds using the canopy height model-based method. Best individual tree extraction result is produced by the proposed method, especially for an area with small trees.

Unveiling water dynamics in fuel cells from time-resolved tomographic microscopy data

X-ray dynamic tomographic microscopy offers new opportunities in the volumetric investigation of dynamic processes. Due to data complexity and their sheer amount, extraction of comprehensive quantitative information remains challenging due to the intensive manual interaction required. Particularly for dynamic investigations, these intensive manual requirements significantly extend the total data post-processing time, limiting possible dynamic analysis realistically to a few samples and time steps, hindering full exploitation of the new capabilities offered at dedicated time-resolved X-ray tomographic stations. In this paper, a fully automatized iterative tomographic reconstruction pipeline (rSIRT-PWC-DIFF) designed to reconstruct and segment dynamic processes within a static matrix is presented. The proposed algorithm includes automatic dynamic feature separation through difference sinograms, a virtual sinogram step for interior tomography datasets, time-regularization extended to small sub-regions for increased robustness and an automatic stopping criterion. We demonstrate the advantages of our approach on dynamic fuel cell data, for which the current data post-processing pipeline heavily relies on manual labor. The proposed approach reduces the post-processing time by at least a factor of 4 on limited computational resources. Full independence from manual interaction additionally allows straightforward up-scaling to efficiently process larger data, extensively boosting the possibilities in future dynamic X-ray tomographic investigations.

Nested Basin-Sampling

We report an embarrassingly parallel method for the evaluation of thermodynamic properties over an energy landscape exhibiting broken ergodicity, nested is the likelihood of the observed data D givenbasin-sampling (NBS). We also introduce the No Galilean U-Turn Sampler (NoGUTS), a new sampling scheme based on the No U-Turn Sampler (NUTS) introduced by Hoffman and Gelman (2014) that works with the Galilean Monte Carlo scheme introduced by Betancourt (2012) to aid the efficient generation of new live points. NoGUTS can be thought of as a form of reflective slice sampling with an automatic stopping criterion. We apply this approach to a benchmark atomic cluster of 31 Lennard-Jones atoms, which exhibits a low temperature solid-solid heat capacity peak. The calculated heat capacity is compared with results generated by parallel tempering (PT), basin-sampling parallel tempering (BSPT), and standard nested sampling (NS) simulations. NBS reproduces the full heat capacity curve predicted by PT and BSPT, while the NS calculation with similar computational cost fails to resolve the low-temperature solid-solid phase transition.

An Automatic Stopping Criterion for Contrast Enhancement Using Multi-scale Top-Hat Transformation

Image contrast enhancement is frequently referred to as one of the most important issues in image processing because it is a necessary pre-processing step in many computer vision and image processing algorithms. Contrast enhancement is normally required to increase the quality of low contrast images by expanding the dynamic range of input gray level. However, image contrast enhancement without disturbing other parameters of the image is one of the difficult tasks in image processing. To efficiently enhance images, algorithms based on multi-scale top-hat morphological transform (MSTH) have been proposed. However, scale selection to stop the algorithm is very subjective and empirical. In order to automatically select the iterations number required by MSTH algorithm, an automatic stopping criterion based on the contrast improvement ratio revisited is proposed in this paper.

Sampling-based incremental information gathering with applications to robotic exploration and environmental monitoring

We propose a sampling-based motion-planning algorithm equipped with an information-theoretic convergence criterion for incremental informative motion planning. The proposed approach allows dense map representations and incorporates the full state uncertainty into the planning process. The problem is formulated as a constrained maximization problem. Our approach is built on rapidly exploring information-gathering algorithms and benefits from the advantages of sampling-based optimal motion-planning algorithms. We propose two information functions and their variants for fast and online computations. We prove an information-theoretic convergence for an entire exploration and information-gathering mission based on the least upper bound of the average map entropy. A natural automatic stopping criterion for information-driven motion control results from the convergence analysis. We demonstrate the performance of the proposed algorithms using three scenarios: comparison of the proposed information functions and sensor configuration selection, robotic exploration in unknown environments, and a wireless signal strength monitoring task in a lake from a publicly available dataset collected using an autonomous surface vehicle.

Adaptive stopping criterion for iterative linear solvers combined with anisotropic mesh adaptation, application to convection-dominated problems

In this work we propose an automatic adaptive stopping criterion integrated with the anisotropic mesh adaptation procedure, thus requiring no additional computational cost. Using information from this procedure we provide control for the linear solver convergence, stopping the iterative solution when the algebraic error is lower than the estimated discretization error. We apply this framework to steady and unsteady convection–diffusion problems, using a stabilized finite element formulation. The proposed method proves to be an effective cost-free strategy to reduce the number of iterations needed, without spoiling the accuracy of the solution.

Blind Restoration Algorithm Using Residual Measures for Motion-Blurred Noisy Images

Abstract Image de-blurring is an inverse problem whose intent is to recover an image from the image affected badly with different environmental conditions. Usually, blurring can happen in various ways; however, de-blurring from a motion problem with or without noise can pose an important problem that is difficult to solve with less computation task. The quality of the restored image in iterative methods of blind motion de-blurring depends on the regularization parameter and the iteration number, which can be automatically or manually stopped. Blind de-blurring and restoration employing image de-blurring and whiteness measures are proposed in this paper to automatically decide the number of iterations. The technique has three modules, namely image de-blurring module, whiteness measures module, and image estimation module. New whiteness measures of hole entropy and mean-square contingency coefficient have been proposed in the whiteness measures module. Initially, the blurred image is de-blurred by the employment of edge responses and image priors using point-spread function. Later, whiteness measures are computed for the de-blurred image and, finally, the best image is selected. The results are obtained for all eight whiteness measures by employing evaluation metrics of increase in signal-to-noise ratio (ISNR), mean-square error, and structural similarity index. The results are obtained from standard images, and performance analysis is made by varying parameters. The obtained results for synthetically blurred images are good even under a noisy condition with ΔISNR average values of 0.3066 dB. The proposed whiteness measures seek a powerful solution to iterative de-blurring algorithms in deciding automatic stopping criteria.

Early Stopping Criterion Combining Probability Density Function with Validation Error for Improving the Generalization Capability of the Backpropagation Neural Network

A new early stopping criterion that combines the probability density function error and the validation error was designed to evaluate a training process. In this new criterion, the real status of the backpropagation neural network (BPNN) was jointly evaluated by the validation error, the probability density error of the traditional training data, and the unlabeled data that was not considered in the traditional training process to determine the appropriate stopping time. The proposed approach was tested on three classes of automatic stopping criteria. In addition, it was compared with the stopping criteria based solely on the validation error and the probability density error with the use of four different data sets selected from the UCI Machine Learning Repository. Results showed that the new early stopping criterion is beneficial for enhancing the generalization capability of the BPNN, demonstrating an improved forecasting precision by 2%-3%.

Perception of noise and global illumination: Toward an automatic stopping criterion based on SVM

Unbiased global illumination methods based on stochastical techniques provide photorealistic images. However, they are prone to noise that can only be reduced by increasing the number of processed samples. The problem of finding the number of samples that are required in order to ensure that most observers cannot perceive any noise is still an open issue. In this article, we address this problem focusing on visual perception of noise. However, rather than using known perceptual models, we investigate the use of learning approaches classically used in the field of Artificial Intelligence. Hence, we propose to use such approaches to create a model which is able to learn which image highlights perceptual noise. The learning is performed through the use of a database of examples based on experimentations of noise perception with human users. This model can then be used in any progressive stochastic global illumination method in order to find the visual convergence threshold of different parts of an input image.

GUI design exploration software for microwave antennas

Abstract Optimizers in commercial electromagnetic (EM) simulation software packages are the main tools for performing antenna design exploration today. However, these general purpose optimizers are facing challenges in optimization efficiency, supported optimization types and usability for antenna experts without deep knowledge on optimization. Aiming to fill the gaps, a new antenna design exploration tool, called Antenna Design Explorer (ADE), is presented in this paper. The key features are: (1) State-of-the-art antenna design exploration methods are selected and embedded, addressing efficient antenna optimization (critical but unable to be solved by existing tools) and multiobjective antenna optimization (not available in most existing tools); (2) Human-computer interaction for the targeted problem is studied, addressing various usability issues for antenna design engineers, such as automatic algorithmic parameter setting and interactive stopping criteria; (3) Compatibility with existing tools is studied and ADE is able to co-work with existing EM simulators and optimizers, combining advantages. A case study verifies the advantages of ADE. Highlights A new antenna design exploration tool, called Antenna Design Explorer (ADE), is presented in this paper. State-of-the-art antenna design exploration methods are selected and embedded, addressing efficient antenna optimization (critical but difficult to be solved by existing tools) and multiobjective antenna optimization (not available in most existing tools). Human-computer interaction for the targeted problem is studied, addressing various usability issues for antenna design engineers. Compatibility with existing tools is studied and ADE is able to co-work with existing EM simulators and optimizers, combining advantages.

SDME Quality Measure based Stopping Criteria for Iterative Deblurring Algorithms

Deblurring from motion problem with or without noise is ill-posed inverse problem and almost all inverse problem require some sort of parameter selection. Quality of restored image in iterative motion deblurring is dependent on optimal stopping point or regularization parameter selection. At optimal point reconstructed image is best matched to original image and for other points either data mismatch occurs and over smoothing is resulted. The methods used for optimal parameter selection are formulated based on correct estimation of noise variance or with restrictive assumption on noise. Some methods involved heavy computation and produce delay in final output. In this paper we propose the method which calculate visual image quality of reconstructed image with the help of Second derivative like measure of enhancement (SDME) and helps to efficiently decide optimal stopping condition which has been checked for leading image deblurring algorithm. It do not require any estimation of noise variance or no heavy computation are needed. Simulation has been done for various images including standard images for different degradation and noise condition. For test leading deblurring algoritham of Blind and Semi-Blind deblurring of Natural Images using Alternate direction method of minimizer (ADMM) is considered. The obtained results for synthetically blurred images are good even under noisy condition with ? ISNR average values 0.2914 dB. The proposed whiteness measures seek powerful solution to iterative deblurring algorithms in deciding automatic stopping criteria.

Iterative illumination correction with implicit regularization

This paper presents a retrospective algorithm for correcting the uneven illumination field in microscopy images. The illumination field is iteratively made uniform using an increasing sequence of bivariate polynomials. At each iteration, the least squares problem of fitting a 2-D polynomial to a sampled image is solved by using QR decomposition with column pivoting, where image samples are obtained by dynamic programming or watershed transform. This incremental scheme allows the smoothness constraint of the estimated bias field to be implicitly satisfied. The proper number of iterations is determined by an automatic stopping criterion. The experimental results show the effectiveness of the proposed approach when compared to a set of different well-established methods.

Improved spherical deconvolution to solve fiber crossing in diffusion-weighted MR Imaging.

An improved spherical deconvolution algorithm to solve fiber crossing in diffusion magnetic resonance imaging is here presented. The introduction of a regularization parameter on the reconstruction of the fibers directions allows to consider the deconvolution as a constrained least squares problem and enforces the normalization of the reconstructed directions. Moreover a new automatic stopping criterion is implemented which allows to push the algorithm to convergence. These modifications improve significantly the performance of the algorithm, decreasing the resolution limit and reconstructing better profiles of the fibers.

An optimal partial differential equations-based stopping criterion for medical image Denoising

Improving the quality of medical images at pre- and post-surgery operations are necessary for beginning and speeding up the recovery process. Partial differential equations-based models have become a powerful and well-known tool in different areas of image processing such as denoising, multiscale image analysis, edge detection and other fields of image processing and computer vision. In this paper, an algorithm for medical image denoising using anisotropic diffusion filter with a convenient stopping criterion is presented. In this regard, the current paper introduces two strategies: utilizing the efficient explicit method due to its advantages with presenting impressive software technique to effectively solve the anisotropic diffusion filter which is mathematically unstable, proposing an automatic stopping criterion, that takes into consideration just input image, as opposed to other stopping criteria, besides the quality of denoised image, easiness and time. Various medical images are examined to confirm the claim.

R.massmov: an open-source landslide model for dynamic early warning systems

This paper illustrates the main characteristics of the newly developed landslide model r.massmov, which is based on the shallow water equations, and is capable of simulating the landslide propagation over complex topographies. The model is the result of the reimplementation of the MassMov2D into the free and open-source GRASS GIS with a series of enhancements aiming at allowing its possible integration into innovative early warning monitoring systems and specifically into Web processing services. These improvements, finalized at significantly reducing computational times, include the introduction of a new automatic stopping criterion, fluidization process algorithm, and the parallel computing. Moreover, the results of multi-spatial resolution analysis conducted on a real case study located in the southern Switzerland are presented. In particular, this analysis, composed by a sensitivity analysis and calibration process, allowed to evaluate the model capabilities in simulating the phenomenon at different input data resolution. The results illustrate that the introduced modifications lead to important reductions in the computational time (more than 90 % faster) and that, using the lower dataset resolution capable of guaranteeing reliable results, the model can be run in about 1 s instead of the 3.5 h required by previous model with not optimized dataset resolution. Aside, the results of the research are a series of new GRASS GIS modules for conducting sensitivity analysis and for calibration. The latter integrates the automated calibration program “UCODE” with any GRASS raster module. Finally, the research workflow presented in this paper illustrates a best practice in applying r.massmov in real case applications.

An Interactive Deformable Model Segmentation Algorithm Driven by Morphological Dilations and Erosions Constrained by an Exclusion Band

This study introduces an interactive image segmentation algorithm for extraction of ill-defined edges (faint, blurred or partially broken) often observed at small-scale imaging. It is based on a simplified deformable elastic model evolution paradigm. Segmentation is achieved as a two-step region-growing, shrinking and merging simulation constrained by an exclusion band built around the edges of the regions of interest, defined from a variation image. The simulation starts from a set of unlabeled markers and the respective elastic models. During the first step, model evolution occurs entirely outside the exclusion band, driven by alternate action-reaction movements. Forward and backward movements are performed by constrained binary morphological dilations and erosions. Constraints allow controlling how far models can move through narrow gaps. At the end of the first step, models remaining from merging operations receive unique and exclusive labels. On the second and final step, models expansion occurs entirely inside the exclusion band, now driven only by binary unconstrained morphological dilations. A point where two labeled models get into contact defines an edge point. The simulation goes on until the concurrent expansion of all models comes to a complete stop. At this point, the edges of the regions-of-interest have been extracted. Interactivity introduces the possibility to correct small imperfections in the edge positioning by changing a parameter controlling action-reaction or by changing marker’s size, position and shape. Slightly inspired by traditional approaches as PDE Level-Set based curve evolution and Immersion Simulation, the algorithm presents a solution to the problem of “synchronizing the concurrent evolution of a large number of models” and an “automatic stopping criterion” for the front propagation. Integer arithmetic implementation assures linear execution time. The results obtained for real applications show that even ill-defined edges can be located with the desired accuracy, thanks to algorithm features and to the interactivity exerted by the user during the segmentation procedure.

Moving Objects Localization by Local Regions Based Level Set: Application on Urban Traffic

In this paper, a novel method for locating multiple moving objects in a video sequences captured by a stationary camera is proposed. In order to determine the precise location of the objects in an image, a new local regions based level set model is carried out. The whole process consists of two main parts: the global detection and the fine localization. During the global detection, the presence or absence of an object in an image is determined by the Mixture of Gaussians method. For the fine localization, we propose to reformulate global energies by utilizing little squared windows centered on each point over a thin band surrounding the zero level set, hence the object contour can be reshaped into small local interior and exterior regions that lead to compute a family of adaptive local energies, which enables us to well localize the moving objects. Moreover, we propose to adapt the smoothness of the contours, and the accuracy of the objects’ perimeter of different shapes with an automatic stopping criterion. The proposed method has been tested on different real urban traffic videos, and the experiment results demonstrate that our algorithm can locate effectively and accurately the moving objects; optimize the results of the localized objects and also decrease the computations load.

On the choice of the parameters for anisotropic diffusion in image processing

Anisotropic diffusion filtering is highly dependent on some crucial parameters, such as the conductance function, the gradient threshold parameter and the stopping time of the iterative process. The various alternative options at each stage of the algorithm are examined and evaluated and the best choice is selected. An automatic stopping criterion is proposed, that takes into consideration the quality of the preserved edges as opposed to just the level of smoothing achieved. The proposed scheme is evaluated with the help of real and simulated images, and compared with other state of the art schemes using objective criteria.

Direct path detection using multipath interference cancelation for communication-based positioning system

Abstract Abstract Recent development in wireless communication-based positioning technology brings significant challenge of detecting the weak signal component arriving from the direct propagation path for time-of-arrival (TOA)-based approach. Due to the common obstruction of the direct propagation path in dense multipath environments, identification of the weak direct path in these environments can be very difficult via the classical correlation-based estimator in the presence of interference from significant later arriving multipath components. A new direct path detection scheme using the multipath interference cancelation is presented in this article. For data communication purpose, an iterative estimator with improved accuracy for joint channel and data estimation is first developed; based on which, the interference of multipath components are reconstructed and subtracted from the original received signal. With the aid of the detected data, an enhanced preamble is formulated. The accuracy of direct path detection is substantially improved by using the correlation between the multipath interference-suppressed signal and the enhanced preamble. Semi-analytical expression of the performance of the iterative estimator is derived. The analysis enables the system to determine an automatic stopping criterion to reduce the computational complexity of the iterative process. The performance of the direct path detection is also analyzed in terms of the signal-to-interference-noise ratio (SINR) and compared with that of the conventional approach. Computer simulation results show the superiority of the proposed direct path detection. The accuracy of the positioning system using the proposed method is also evaluated in dense multipath environments.