Local Adaptive Algorithm Research Articles

ЗАСТОСУВАННЯ ЛОКАЛЬНО-АДАПТИВНОЇ МІРІАДНОЇ ФІЛЬТРАЦІЇ ДЛЯ КОМПЛЕКСНОЇ МОДЕЛІ ОДНОМІРНОГО СИГНАЛУ

Local adaptive algorithms of myriad filtering with adaptation of a sample myriad linearity parameter K depending upon local estimates of signal scale and "hard" switching of parameters of sliding window lengths and coefficients that influence on parameter K are proposed. Statistical estimates of filters quality are obtained using criterion of minimum mean-square error for a complex model of one-dimensional signal that includes elementary signals such as a constant signal, step edge, piecewise linear fragments, linearly increasing and decreasing signals, a peak-like maximum, a piecewise function that consists of constant signal and polynomial curve, a parabolic maximum under conditions of additive Gaussian noise with zero mean and different variances and possible spikes presence. It is shown that the proposed myriad locally-adaptive filters can preserve fragments of rapid changing of signal as step edge and other discontinues due to high dynamic properties in nonlinear mode of myriad filtering and small length of the sliding window and can effectively suppress noise while processing fragments of linear signal behavior and polynomial curves by adjusting the parameter K to a linear mode and increasing the window length. Having high efficiency for all fragments of the complex signal, one of the proposed algorithms provides practically optimal noise suppression at the fragment of linear change of the signal, the other one provides higher quality of step-like and constant signals processing. As a result of application of the proposed myriad locally-adaptive filters, improvement of integral and local performance indicators is shown in comparison to the high effective non-linear locally adaptive algorithm that adaptively switches the output signals between median filter with small window length and alpha-trimmed filters with middle and large windows and local-adaptive myriad algorithm with adaptation of the linearity parameter K which are used for the considered test complex signal. Due to the use of multithreading in programing for parallel calculations, all the considered nonlinear algorithms have possibility to be implemented in real time. The more appropriate algorithm for calculating a sample myriad is the algorithm of minimization of myriad cost function based on a numerical Newton technique because of its best performance in step-like signal fragments and best robustness. In order to ensure better spike removal, it is expedient to pre-process the signal by robust myriad filter.

A local adaptive density-based algorithm for clustering polygonal buildings in urban block polygons

Building clustering is an important task that should be performed prior to building generalization operations. One of the most common approaches for building clustering is the use of density-based algorithms. Current density-based algorithms encounter problems in detecting accurate clusters in a region with varying density. To overcome this problem, a new density-based spatial clustering algorithm, local-adaptive DBSCAN (LA-DBSCAN), which can cluster polygonal buildings in urban blocks with noise and non-uniform density, is developed. The advantage of LA-DBSCAN is that it can select parameters that are adaptive to different local situations. To evaluate the performance of the proposed model, the complete building generalization process is implemented using four datasets at 1:25k scale. An evaluation of the results allowed us to conclude that the LA-DBSCAN algorithm yields more homogeneous and accurate results than the DBSCAN algorithm. Thus, the presented approach is beneficial for the detection of building patterns and the generalization.

Research on Crack Detection Algorithm of the Concrete Bridge Based on Image Processing

The appearance and development of cracks in the concrete bridge will seriously affect the safe use of bridge buildings. In order to better satisfy the crack detection requirement, this paper comes up with an image preprocessing scheme combining multiple adaptive filtering and contrast enhancement based on the image processing technology of concrete crack, which can improve the removal effect of background noise and obtain the characteristic vein information of tiny cracks. Then we designed a local adaptive algorithm of Otsu threshold segmentation and integrated with modified Sobel operator for removing isolated noise spots, so as to extract the crack edge information and improve the positioning accuracy of the crack boundary. Furthermore, according to the image feature of the bridge crack edge, the target crack is identified as well as classified and the feature data is calculated. The results of case analysis show that the data processing precision of the detection algorithm can reach 0.02mm, which can satisfy the actual engineering detection requirements of concrete bridge crack.

GT-RootS: An integrated software for automated root system measurement from high-throughput phenotyping platform images

GT-RootS (Global Traits of Root System) is an automated Java-based open-source solution we are developing for processing root system images provided by the Rhizoscope, a CIRAD phenotyping platform dedicated to dense cereal plants. Two types of use are proposed. The fully-automated mode applies a predefined standard processing pipeline to a preselected set of images while the semi-automated mode allows the user to interactively check and correct intermediate processing results to a specific image. In both cases, GT-RootS combines a local adaptive thresholding algorithm and a similarity indicator to automatically separate the root system from a complex background without user intervention. A covering house-shaped polygon is then defined in the axis system of the root ellipse from vertical weighted density profiles. This canonical shape is composed of both upper trapezoid and lower rectangular compartments from which upper and lower heights, global width and local offset, root system cone angulation and spatial densities can be easily evaluated and displayed. GT-RootS measurements were compared both to expert evaluations and to two other estimation methods on a set of 64 images of a dense Japonica rice root system of 30-days-old plants. We demonstrate also that GT-RootS satisfies the requirements of high-throughput analyses: short processing time (around 30 images per hour on a low-end computer), measurement accuracy and repeatability, and user bias eradication.

Correction: A parallel spatiotemporal saliency and discriminative online learning method for visual target tracking in aerial videos.

[This corrects the article DOI: 10.1371/journal.pone.0192246.].

A parallel spatiotemporal saliency and discriminative online learning method for visual target tracking in aerial videos.

Visual tracking in aerial videos is a challenging task in computer vision and remote sensing technologies due to appearance variation difficulties. Appearance variations are caused by camera and target motion, low resolution noisy images, scale changes, and pose variations. Various approaches have been proposed to deal with appearance variation difficulties in aerial videos, and amongst these methods, the spatiotemporal saliency detection approach reported promising results in the context of moving target detection. However, it is not accurate for moving target detection when visual tracking is performed under appearance variations. In this study, a visual tracking method is proposed based on spatiotemporal saliency and discriminative online learning methods to deal with appearance variations difficulties. Temporal saliency is used to represent moving target regions, and it was extracted based on the frame difference with Sauvola local adaptive thresholding algorithms. The spatial saliency is used to represent the target appearance details in candidate moving regions. SLIC superpixel segmentation, color, and moment features can be used to compute feature uniqueness and spatial compactness of saliency measurements to detect spatial saliency. It is a time consuming process, which prompted the development of a parallel algorithm to optimize and distribute the saliency detection processes that are loaded into the multi-processors. Spatiotemporal saliency is then obtained by combining the temporal and spatial saliencies to represent moving targets. Finally, a discriminative online learning algorithm was applied to generate a sample model based on spatiotemporal saliency. This sample model is then incrementally updated to detect the target in appearance variation conditions. Experiments conducted on the VIVID dataset demonstrated that the proposed visual tracking method is effective and is computationally efficient compared to state-of-the-art methods.

Efficient local monitoring approach for the task of background subtraction

We present in this paper a novel and efficient method that will significantly reduce GMM drawbacks in the presence of complex and dynamic scene. The main idea is to combine global and local features to remove local variations and the instant variations in the brightness that, in most cases, decrease the performance of background subtraction models. The first step is to divide the extracted frames into several equal size blocks. Then, we apply an adaptive local monitoring algorithm for each block to control local variation using Pearson similarity measurement. When a significant environment changes are detected in one or more blocks, the parameters of GMM assigned to these blocks are updated and the parameters of the rest remain the same. We also proposed merging adjacent and invariant blocks to reduce processing time and splitting the blocks that have an intense movement to improve accuracy. Experimental results on several datasets demonstrate that the proposed approach is effective and efficient under the common problems found in background modeling, outperforming the most referred state-of-the-art background subtraction methods.

High-order adaptive Gegenbauer integral spectral element method for solving non-linear optimal control problems

In this work, we propose an adaptive spectral element algorithm for solving non-linear optimal control problems. The method employs orthogonal collocation at the shifted Gegenbauer–Gauss points combined with very accurate and stable numerical quadratures to fully discretize the multiple-phase integral form of the optimal control problem. The proposed algorithm relies on exploiting the underlying smoothness properties of the solutions for computing approximate solutions efficiently. In particular, the method brackets discontinuities and ‘points of nonsmoothness’ through a novel local adaptive algorithm, which achieves a desired accuracy on the discrete dynamical system equations by adjusting both the mesh size and the degree of the approximating polynomials. A rigorous error analysis of the developed numerical quadratures is presented. Finally, the efficiency of the proposed method is demonstrated on three test examples from the open literature.

Moving distance measurement for hydraulic support based on fruit fly optimization algorithm

Due to the inaccurate and unreliable moving distance measurement of the hydraulic support in mines, a method based on the random circle detection (RCD) algorithm and the fruit fly optimization algorithm (FOA) is proposed. According to the changing center and radium of the circle on the support, the relative position of adjacent supports is acquired by the camera. The noise of the collected image is moved, and the edge feature is protected using a bilateral filter. A local adaptive threshold algorithm is used for binary processing of the image. Then, RCD is used to detect the contour, which is similar to the circle. A method to detect the circle based on FOA is used to accurately detect the circle. Subsequently, the relative distance is calculated according to the change of the circle. Finally, the accuracy and reliability of the proposed method are verified though the experiment.

Local adaptive weighted tracking via inverse structure sparse representation

Traditional sparse representation tracker use simple grayscale characteristics in calculating sparse coefficient, which is easily affected by the heavy occlusions and deformation. To this end, a local adaptive weighting algorithm is put forward to increase degree of differentiation between the candidate targets affected by shade, deformation, etc and not affected by the shade, deformation, etc. In addition, the general sparse representation algorithm use a small number of target templates to build a complete dictionary, which unable to get a better sparse coefficient. Inverse structure sparse representation algorithm, using the candidate target which contains rich target and background features to build a complete dictionary to reconstruct the target template under the condition of the same dimension target template better sparse coefficient can be obtained, is proposed. Experiments show that the proposed algorithm in the small differences between target and background or serious barrier, deformation, can better track the target.

A Novel Adaptive Local Dimming Backlight Control Chip Design Based on Gaussian Distribution for Liquid Crystal Displays

In this paper, a novel hardware-oriented adaptive local dimming backlight control algorithm is proposed for very large-scale integration implementation. The proposed algorithm includes a novel dimming algorithm to save energy of the liquid crystal display (LCD) backlight module and a novel contrast enhancement algorithm to compensate the dimming of brightness in displaying images. In order to improve the power-saving rates and image quality, a block-based model and a Gaussian distribution model were added. After obtaining a clipped point by the Gaussian distribution model, a pixel compensator was used to compensate the brightness value of each pixel to satisfy the human vision. In the meantime, a power width modulation module produced power control signal for the LCD backlight module. To meet the performance standards for displaying video in real time, the proposed algorithm was implemented by an eight-stage pipelined architecture. In addition, a new color-space YEF transform composed of integer adders and shifters was used to realize the grayscale converter to replace floating point multipliers. Moreover, the high-complexity operations, such as square root, multiplication, and division, required for the Gaussian distribution model were simplified by using a lookup table technique, which could reduce the hardware cost of the proposed design efficiently. This design was fabricated by a TSMC 0.18-μm CMOS process, and the chip area is 630 105 μm2. Compared with previous local dimming backlight control algorithms, this work improved the average peak signal to noise ratio by 6.89 dB and saved 7.61% more backlight power consumption than previous studies.

Multi-mode clustering model for hierarchical wireless sensor networks

The topology management, i.e., clusters maintenance, of wireless sensor networks (WSNs) is still a challenge due to its numerous nodes, diverse application scenarios and limited resources as well as complex dynamics. To address this issue, a multi-mode clustering model (M2CM) is proposed to maintain the clusters for hierarchical WSNs in this study. In particular, unlike the traditional time-trigger model based on the whole-network and periodic style, the M2CM is proposed based on the local and event-trigger operations. In addition, an adaptive local maintenance algorithm is designed for the broken clusters in the WSNs using the spatial–temporal demand changes accordingly. Numerical experiments are performed using the NS2 network simulation platform. Results validate the effectiveness of the proposed model with respect to the network maintenance costs, node energy consumption and transmitted data as well as the network lifetime.

Discrete-Time Optimal Control via Local Policy Iteration Adaptive Dynamic Programming.

In this paper, a discrete-time optimal control scheme is developed via a novel local policy iteration adaptive dynamic programming algorithm. In the discrete-time local policy iteration algorithm, the iterative value function and iterative control law can be updated in a subset of the state space, where the computational burden is relaxed compared with the traditional policy iteration algorithm. Convergence properties of the local policy iteration algorithm are presented to show that the iterative value function is monotonically nonincreasing and converges to the optimum under some mild conditions. The admissibility of the iterative control law is proven, which shows that the control system can be stabilized under any of the iterative control laws, even if the iterative control law is updated in a subset of the state space. Finally, two simulation examples are given to illustrate the performance of the developed method.

Speech enhancement using robust estimators and rank-order statistics

Purpose The purpose of the present work is to design robust estimators for speech enhancement by incorporation of calculation rank-order statistics and locally-adaptive neighborhoods. The proposed estimators are able to increase the speech quality of a noisy signal, to preserve better speech intelligibility, and to introduce less artifacts comparing with known speech enhancement estimators. Design/methodology/approach We design a novel speech enhancement algorithm based on rank-order statistics and local adaptive signal processing to improve the accuracy of existing speech enhancement estimators, in terms of speech quality, intelligibility, and introduction of artificial artifacts. Findings We found that by using the proposed estimators for speech enhancement we obtain a better adaptation to nonstationary characteristics of speech and noise processes comparing with that of known speech enhancement estimators. The proposed algorithm increases speech quality, preserves better speech intelligibility, and introduces less artifacts comparing with known speech enhancement estimators. Research limitations/implications The proposed approach for speech enhancement is a locally-adaptive signal processing performed for each element of a noisy speech signal. Thus, the main limitation of the proposed approach is an increase of computational complexity compared with that of nonadaptive conventional techniques. Practical implications In order to perform real-time speech enhancement with the proposed approach, it is recommended to use a digital system with a fast processor. Another option is by using a parallel architecture such as a FPGA. Originality/value We propose a novel local-adaptive algorithm for robust speech enhancement by incorporation of calculation of rank-order statistics and local-adaptive neighborhoods. The proposed algorithm is able to adjust itself in response to changes in the statistical properties of ambience noise.

A local anisotropic adaptive algorithm for the solution of low-Mach transient combustion problems

A novel numerical algorithm for the simulation of transient combustion problems at low Mach and moderately high Reynolds numbers is presented. These problems are often characterized by the existence of a large disparity of length and time scales, resulting in the development of directional flow features, such as slender jets, boundary layers, mixing layers, or flame fronts. This makes local anisotropic adaptive techniques quite advantageous computationally. In this work we propose a local anisotropic refinement algorithm using, for the spatial discretization, unstructured triangular elements in a finite element framework. For the time integration, the problem is formulated in the context of semi-Lagrangian schemes, introducing the semi-Lagrange-Galerkin (SLG) technique as a better alternative to the classical semi-Lagrangian (SL) interpolation. The good performance of the numerical algorithm is illustrated by solving a canonical laminar combustion problem: the flame/vortex interaction. First, a premixed methane-air flame/vortex interaction with simplified transport and chemistry description (Test I) is considered. Results are found to be in excellent agreement with those in the literature, proving the superior performance of the SLG scheme when compared with the classical SL technique, and the advantage of using anisotropic adaptation instead of uniform meshes or isotropic mesh refinement. As a more realistic example, we then conduct simulations of non-premixed hydrogen-air flame/vortex interactions (Test II) using a more complex combustion model which involves state-of-the-art transport and chemical kinetics. In addition to the analysis of the numerical features, this second example allows us to perform a satisfactory comparison with experimental visualizations taken from the literature.

Identifying critical areas to reduce bycatch of coastal common bottlenose dolphins Tursiops truncatus in artisanal fisheries of the subtropical western South Atlantic

Many anthropogenic actions have an impact on coastal dolphins, with bycatch being one of the main threats. We describe the distribution patterns of common bottlenose dolphins Tursiops truncatus and periods of higher entanglement risk by the artisanal gillnet fishery in the Patos Lagoon estuary and along the adjacent coast of southern Brazil. A total of 136 dolphin groups and 187 gillnets were encountered in 69 surveys conducted between September 2006 and July 2009. Data were analyzed in relation to environmental, spatial and temporal variables using generalized additive models and a spatially adaptive local smoothing algorithm for model selec- tion. In both areas, dolphin densities increased as distance to the estuary mouth decreased. For the estuary area, water salinity and temperature influenced dolphin distribution. Along the adjacent coast, dolphin densities were higher with distance to shore as well as in the north area during the warm period. Patterns of dolphin distribution were probably a response to the presence of pre- ferred prey or avoidance of human-related disturbance. Kernel density showed that fishing effort was distributed along the entire surveyed area inside the estuary, while along the adjacent coast it was higher in the south compared to the north area in the warm period. The overlap between gillnets and dolphins increased considerably from the cold (33.8%) to the warm (48.6%) period. Seasonal variation in fishing effort and distribution affect the overlap and the risk of dolphin entanglement. Based on the findings of this study, a fishing exclusion area aimed at reducing bycatch was established by the Brazilian Environmental Agency.

Active balance of humanoids with foot positioning compensation and non-parametric adaptation

To maintain human-like active balance for a humanoid robot, this paper proposes a novel adaptive non-parametric foot positioning compensation approach that can modify predefined step position and step duration online with sensor feedback. A constrained inverted pendulum model taking into account of supporting area to CoM acceleration is used to generate offline training samples with constrained nonlinear optimization programming. To speed up real-time computation and make online model adjustable, a non-parametric regression model based on extended Gaussian Process model is applied for online foot positioning compensation. In addition, a real-time and sample-efficient local adaptation algorithm is proposed for the non-parametric model to enable online adaptation of foot positioning compensation on a humanoid system. Simulation and experiments on a full-body humanoid robot validate the effectiveness of the proposed method.

Local adaptive approach toward segmentation of microscopic images of activated sludge flocs

Activated sludge process is a widely used method to treat domestic and industrial effluents. The conditions of activated sludge wastewater treatment plant (AS-WWTP) are related to the morphological properties of flocs (microbial aggregates) and filaments, and are required to be monitored for normal operation of the plant. Image processing and analysis is a potential time-efficient monitoring tool for AS-WWTPs. Local adaptive segmentation algorithms are proposed for bright-field microscopic images of activated sludge flocs. Two basic modules are suggested for Otsu thresholding-based local adaptive algorithms with irregular illumination compensation. The performance of the algorithms has been compared with state-of-the-art local adaptive algorithms of Sauvola, Bradley, Feng, and c-mean. The comparisons are done using a number of region- and nonregion-based metrics at different microscopic magnifications and quantification of flocs. The performance metrics show that the proposed algorithms performed better and, in some cases, were comparable to the state-of the-art algorithms. The performance metrics were also assessed subjectively for their suitability for segmentations of activated sludge images. The region-based metrics such as false negative ratio, sensitivity, and negative predictive value gave inconsistent results as compared to other segmentation assessment metrics.

Local adaptive segmentation algorithm for 3-D medical image based on robust feature statistics

Medical image segmentation is of pivotal importance in computer-aided clinical diagnosis. Many factors, including noises, bias field effect, local volume effect, as well as tissue movement may affect the medical image, thus causing blurring or uneven characteristics when forming a picture. Such quality defects will inevitably impair the gray-scale difference between adjacent tissues and lead to insufficient segmentation or even leakage during tissue or organ segmentation. In the present investigation, a local adaptive segmentation algorithm for 3-D medical image based on robust feature statistics (LARFS) was proposed. By combining segmentation algorithm principles for traditional region growing (RG) and robust feature statistics (RFS), the location and neighborhood image information of input seed point can be comprehensively analyzed by LARFS. Results show that, for different segmentation objects, under controlling the input parameter of growing factor within certain range, LARFS segmentation algorithm can adapt well to the regional geometric shape. And because the robust feature statistics is applied in the contour evolution process, LARFS algorithm is not sensitive to noises and not easily influenced by image contrast and object topology. Hence, the leakage and excessive segmentation effects are ameliorated with a smooth edge, and the accuracy can be controlled within the effective error range.

A local adaptive discretization algorithm for Smoothed Particle Hydrodynamics

In this paper, an extension to the Smoothed Particle Hydrodynamics (SPH) method is proposed that allows for an adaptation of the discretization level of a simulated continuum at runtime. By combining a local adaptive refinement technique with a newly developed coarsening algorithm, one is able to improve the accuracy of the simulation results while reducing the required computational cost at the same time. For this purpose, the number of particles is, on the one hand, adaptively increased in critical areas of a simulation model. Typically, these are areas that show a relatively low particle density and high gradients in stress or temperature. On the other hand, the number of SPH particles is decreased for domains with a high particle density and low gradients. Besides a brief introduction to the basic principle of the SPH discretization method, the extensions to the original formulation providing such a local adaptive refinement and coarsening of the modeled structure are presented in this paper. After having introduced its theoretical background, the applicability of the enhanced formulation, as well as the benefit gained from the adaptive model discretization, is demonstrated in the context of four different simulation scenarios focusing on solid continua. While presenting the results found for these examples, several properties of the proposed adaptive technique are discussed, e.g. the conservation of momentum as well as the existing correlation between the chosen refinement and coarsening patterns and the observed quality of the results.