Automatic Threshold Selection Method Research Articles

Research on extreme value estimations and dynamic coefficients of wheel load for high-speed railway based on generalized Pareto distribution theory

The design value of wheel load is one of the key design parameters for high-speed railway ballastless track structure, which essentially belongs to the extreme estimation of wheel load. Based on the generalized Pareto distribution theory, this article carries on the research on the extreme value estimations of wheel load excited by whole wavelength range irregularity, which provides a basis for the design of ballastless track structure. When using automatic threshold selection method, sample declustering can improve the independence between data. The shape parameter screening method is proposed for the extreme value estimations of wheel load, and the corresponding sample size and shape parameter range for each cluster are determined. A reasonable sample size is determined, and the mean of the extreme estimations of wheel load is used as the final extreme estimation of wheel load. The dynamic coefficients that match the design life of the ballastless track structure are obtained.

Automatic threshold selection method using exponential Renyi entropy under multi-scale product in stationary wavelet domain

Automatic threshold selection method using exponential Renyi entropy under multi-scale product in stationary wavelet domain

Objective Evaluation of Clutter Suppression for Micro-Doppler Spectrograms of Hand Gesture/Sign Language Based on Pseudo-Reference Image

Gesture and sign language recognition technology enables machines to understand the meaning of human hand movements. In human-computer interaction, it is expected that gesture/sign language recognition technologies will overcome equipment size and application environment constraints; in information communication, gesture/sign language recognition technology will assist healthy people in more easily entering the world of deaf people and better understanding and meeting their inner emotional needs; and in patient monitoring, gesture/sign language recognition technologies will detect abnormal behavior in the elderly or patients and reduce possible safety issues. As a result, it has significant implications for both research and broad application. Because the radar sensor can work normally in a wide range of illumination and weather conditions, as well as penetrate the shelter to receive the moving object echo signal and preserve individual privacy, it is becoming increasingly popular in a variety of recognition tasks. A primary step in using radar sensors for gesture/sign language recognition is to suppress clutter to highlight useful motion information. To evaluate the clutter suppression effect, however, an objective metric is required. We present an objective assessment metric based on the pseudo-reference image and an automatic threshold selection method based on Otsu for clutter suppression, as well as subjective and objective experiments demonstrating their effectiveness and universality in gesture/sign language recognition. Notably, our proposed metric can be used for any recognition task that uses micro-Doppler spectrograms as the dataset.

An automatic three-way clustering method based on sample similarity

The three-way clustering is an extension of traditional clustering by adding the concept of fringe region, which can effectively solve the problem of inaccurate decision-making caused by inaccurate information or insufficient data in traditional two-way clustering methods. The existing three-way clustering works often select the appropriate number of clusters and the thresholds for three-way partition according to subjective tuning. However, the method of fixing the number of clusters and the thresholds of the partition cannot automatically select the optimal number of clusters and partition thresholds for different data sets with different sizes and densities. To address the above problem, this paper proposed an improved three-way clustering method. First, we define the roughness degree by introducing the sample similarity to measure the uncertainty of the fringe region. Moreover, based on the roughness degree, we define a novel partitioning validity index to measure the clustering partitions and propose an automatic threshold selection method. Second, based on the concept of sample similarity, we introduce the intra-class similarity and the inter-class similarity to describe the quantitative change of the relationship between the sample and the clusters, and define a novel clustering validity index to measure the clustering performance under different numbers of clusters through the integration of the above two kinds of similarities. Furthermore, we propose an automatic cluster number selection method. Finally, we give an automatic three-way clustering approach by combining the proposed threshold selection method and the cluster number selection method. The comparison experiments demonstrate the effectiveness of our proposal.

A Comparative Study of Water Indices and Image Classification Algorithms for Mapping Inland Surface Water Bodies Using Landsat Imagery

A comparative study of water indices and image classification algorithms for mapping inland water bodies using Landsat imagery was carried out through obtaining 24 high-resolution (≤5 m) and cloud-free images archived in Google Earth with the same (or ±1 day) acquisition dates as the Landsat-8 OLI images over 24 selected lakes across the globe, and developing a method to generate the alternate ground truth data from the Google Earth images for properly evaluating the Landsat image classification results. In addition to the commonly used green band-based water indices, Landsat-8 OLI’s ultra-blue, blue, and red band-based water indices were also tested in this research. Two unsupervised (the zero-water index threshold H0 method and Otsu’s automatic threshold selection method) and one supervised (the k-nearest neighbor (KNN) method) image classification algorithms were employed for conducting the image classification. Through comparing a total of 2880 Landsat image classification results with the alternate ground truth data, this study showed that (1) it is not necessary to use some supervised image classification methods for extracting water bodies from Landsat imagery given the high computational cost associated with the supervised image classification algorithms; (2) the unsupervised classification algorithms such as the H0 and Otsu methods could achieve comparable accuracy as the KNN method, although the H0 method produced more large error outliers than the Otsu method, thus the Otsu method is better than the H0 method; and (3) the ultra-blue band-based AWEInsuB is the best water index for the H0 method, and the ultra-blue band-based MNDWI2uB is the best water index for both the Otsu and KNN methods.

Automatic defect identification technology of digital image of pipeline weld

Digital image of pipeline weld is an important basis for the reliability management of pipeline welds. However, the error rate of artificial discrimination is high. In order to increase the defect identification accuracy of digital image of pipeline weld, we adopted several methods (e.g. multiple edge detection, detection channel and threshold segmentation) to carry out image processing on the image defects of pipeline welds. Then, a defect characteristic database on the digital images of pipeline welds was constructed, including grayscale difference, equivalent area (S/C), circularity, entropy, correlation and other parameters. Furthermore, a multi-classifier construction (SVM) model was established. Thus, the classification and evaluation on the defects in the digital images of pipeline welds were realized. Finally, an automatic defect identification software for digital image of pipeline weld was developed and verified on site. And the following research results were obtained. First, after image processing, the edge detection results obtained by Canny and other algorithms are satisfactory when there is no noise. In the case of noise, however, pseudo-edge emerges in the detection results. In this case, the automatic threshold selection method shall be adopted to detect the image edge to obtain the rational threshold. Second, there are 14 parameters in the defect characteristic database, including shape characteristic, lamination characteristic and image length pixel. Third, by virtue of the SVM classification model, the shape characteristics of each type of defect can be clarified, and the defect characteristics can be identified, such as crack, slag inclusion, air hole, incomplete penetration, non-fusion and strip. Based on field application, the following results were obtained. First, this automatic defect identification technology is applicable to quality identification and evaluation of various defects in pipeline welds. Second, its identification accuracy is higher than 90%. Third, by virtue of this technology, automatic defect identification and evaluation of digital image of pipeline weld is realized. In conclusion, these research results help to ensure the safe operation of pipelines.

Enhanced normalized difference index for impervious surface area estimation at the plateau basin scale

Impervious surface (IS) area is an important indicator of ecological environment condition in the basin. We propose an index for IS extraction [i.e., enhanced normalized difference impervious surfaces index (ENDISI)] by integrating the spectrum character of Landsat-operational land imager (OLI) images, and an automatic threshold selection method using the generalized Gaussian model. Dianchi and Erhai Basin are employed as study areas to test the ENDISI method at the plateau basin scale. The results show that: (1) the ENDISI can reduce the impacts of arid land, bare rock, and bare soil on IS extraction effectively; (2) ENDISI had a much higher separability degree between ISs and pervious surfaces compared with normalized difference built-up index, modified normalized difference IS index, and combinational biophysical composition index; and (3) the overall accuracy and kappa coefficient values of IS extraction via automatic threshold selection exceed 93.9% and 82.4%, respectively. Therefore, the ENDISI can serve as an effective index algorithm for rapid and high-precision IS extraction at the plateau basin scale.

Adaptive Douglas-Peucker Algorithm With Automatic Thresholding for AIS-Based Vessel Trajectory Compression

Automatic identification system (AIS) is an important part of perfecting terrestrial networks, radar systems and satellite constellations. It has been widely used in vessel traffic service system to improve navigational safety. Following the explosion in vessel AIS data, the issues of data storing, processing, and analysis arise as emerging research topics in recent years. Vessel trajectory compression is used to eliminate the redundant information, preserve the key features, and simplify information for further data mining, thus correspondingly improving data quality and guaranteeing accurate measurement for ensuring navigational safety. It is well known that trajectory compression quality significantly depends on the threshold selection. We propose an Adaptive Douglas-Peucker (ADP) algorithm with automatic thresholding for AIS-based vessel trajectory compression. In particular, the optimal threshold is adaptively calculated using a novel automatic threshold selection method for each trajectory, as an improvement and complement of original Douglas-Peucker (DP) algorithm. It is developed based on the channel and trajectory characteristics, segmentation framework, and mean distance. The proposed method is able to simplify vessel trajectory data and extract useful information effectively. The time series trajectory classification and clustering are discussed and analysed based on ADP algorithm in this paper. To verify the reasonability and effectiveness of the proposed method, experiments are conducted on two different trajectory data sets in inland waterway of Yangtze River for trajectory classification based on the nearest neighbor classifier, and for trajectory clustering based on the spectral clustering. Comprehensive results demonstrate that the proposed algorithm can reduce the computational cost while ensuring the clustering and classification accuracy.

A Modified Normalized Difference Impervious Surface Index (MNDISI) for Automatic Urban Mapping from Landsat Imagery

Impervious surface area (ISA) is a key factor for monitoring urban environment and land development. Automatic mapping of impervious surfaces has attracted growing attention in recent years. Spectral built-up indices are considered promising to map ISA distributions due to their easy, parameter-free implementations. This study explores the potentials of impervious surface indices for ISA mapping from Landsat imagery using a case study area in Boston, USA. A modified normalized difference impervious surface index (MNDISI) is proposed, and a Gaussian-based automatic threshold selection method is used to identify the optimal MNDISI threshold for delineating impervious surfaces from background features. To evaluate its effectiveness, comparison analysis is conducted between MNDISI and the original NDISI using Landsat images from three sensors (TM/ETM+/OLI-TIRS) acquired in four seasons. Our results suggest that built-up indices are sensitive to image seasonality, and summer is the best time phase for ISA mapping. With reduced uncertainties from automatic threshold selection, the MNDISI extracts impervious surfaces from all Landsat images in summer with an overall accuracy higher than 87% and an overall Kappa coefficient higher than 0.74. The proposed method is superior to previous index-based ISA mapping from the enhanced thermal integration and automatic threshold selection. The ISA maps from the TM, ETM+ and OLI-TIRS images are not significantly different. With enlarged data pool when all Landsat sensors are considered and automation of threshold selection proposed in this study, the MNDISI could be an effective built-up index for rapid and automatic ISA mapping at regional and global scales.

A new automatic threshold selecting criteria for spectroscopy data processing

A nonparametric and unsupervised method of automatic threshold selection to eliminate noise for spectroscopy data processing is described in this paper. A detecting scheme, named bi-trapezoid criteria, is devised where the threshold is selected according to the turning corner present on the uprising intensity trace. This selecting procedure is very simple, utilizing only basic summation on the sorted intensity sequence to optimize threshold for distinguishing between noises and signals. This approach is effective in selecting appropriate value to filter noise when the distribution of noises is not preconditioned or the gap between signals and noises is not obvious. Testing on both artificial and authentic data under specific quantitative evaluation condition shows that this new method performs better than previous ones.

A New Variable-Regularized Transform-Domain NLMS Algorithm with Automatic Step-Size Selection for Adaptive System Identification/ Filtering

A new variable-regularized (VR) switch-mode noise-constrained (SNC) transform-domain normalized least mean squares (VR-SNC-TDNLMS) algorithm for adaptive system identification and filtering is proposed. It exploits prior knowledge of the additive noise variance and results in a generalized VR-TDNLMS algorithm with a variable step-size (VSS) for improving convergence speed. It also reduces estimation variance, sensitivity to input signal level and eigenvalue spread by means of variable-regularization and decorrelation transformation. To select the variable step-size online, the convergence behavior of the proposed algorithm is analyzed. From the mean convergence analysis, the maximum step-size (MSS) for convergence is first determined. The theoretical results suggest that improved performance can be obtained if the MSS is employed initially while the NC adaptation is adopted near convergence to reduce steady- state misadjustment. Therefore, a switch-mode scheme which employs a MSS mode together with a NC mode is incorporated to further improve its convergence speed. The mean square convergence behavior is also studied by means of a Lyapunov stability-based method to characterize its convergence condition and steady-state misadjustment. Based on the theoretical results, a new automatic threshold selection method for mode switching is developed. General recommendations for choosing other algorithmic parameters are also proposed to facilitate its online and practical usage. The proposed method is expected to find a wide range of applications in areas related to instrumentation and measurement involving low-complexity and recursive linear estimation. In particular, its potential application and effectiveness in system identification problems and several acoustic applications are demonstrated by computer simulations.

A Novel Vision Localization Method of Automated Micro-Polishing Robot

Based on photogrammetry technology, a novel localization method of micro-polishing robot, which is restricted within certain working space, is presented in this paper. On the basis of pinhole camera model, a new mathematical model of vision localization of automated polishing robot is established. The vision localization is based on the distance-constraints of feature points. The method to solve the mathematical model is discussed. According to the characteristics of gray image, an adaptive method of automatic threshold selection based on connected components is presented. The center coordinate of the feature image point is resolved by bilinear interpolation gray square weighted algorithm. Finally, the mathematical model of testing system is verified by global localization test. The experimental results show that the vision localization system in working space has high precision.

An expert system based on fuzzy entropy for automatic threshold selection in image processing

In pattern recognition and image processing, the selection of appropriate threshold is a very significant issue. Especially, the selecting gray-level thresholds is a critical issue for many pattern recognition applications. Here, the maximum fuzzy entropy and fuzzy c-partition methods are used for the aim of the gray-level automatic threshold selection method. The fuzzy theory has been successfully applied to many areas, such as image processing, pattern recognition, computer vision, medicine, control, etc. The images have some fuzziness in nature. In this study, expert maximum fuzzy-Sure entropy (EMFSE) method for the maximum fuzzy entropy and fuzzy c-partition processes in automatic threshold selection is proposed. The experimental studies were conducted on many images by testing maximum fuzzy-Sure entropy against maximum fuzzy-Shannon entropy (MFSHE), maximum fuzzy-Havrada and Charvat entropy (MFHCE) methods for selecting optimum 2-level threshold value, respectively. The obtained experimental results show that the used MFSE method is superior to other MFSHE and MFHCE methods on selecting the 2-level threshold value automatically and effectively.

Objective threshold selection procedure (OTS) for segmentation of scanning laser confocal microscope images

The determination of volumes and interface areas from confocal laser scanning microscopy (CLSM) images requires the identification of component objects by segmentation. An automated method for the determination of segmentation thresholds for CLSM imaging of biofilms was developed. The procedure, named objective threshold selection (OTS), is a three-dimensional development of the approach introduced by the popular robust automatic threshold selection (RATS) method. OTS is based on the statistical properties of local gray-values and gradients in the image. By characterizing the dependence between a volumetric feature and the intensity threshold used for image segmentation, the former can be determined with an arbitrary confidence level, with no need for user intervention. The identification of an objective segmentation procedure renders the possibility for the full automation of volume and interfacial area measurement. Images from two distinct biofilm systems, acquired using different experimental techniques and instrumental setups were segmented by OTS to determine biofilm volume and interfacial area. The reliability of measurements for each case was analyzed to identify optimal procedure for image acquisition. The automated OTS method was shown to reproduce values obtained manually by an experienced operator.

Automatic threshold selection using histogram quantization

An automatic threshold selection method is proposed for biomedical image analysis based on a histogram coding scheme. The threshold values can be determined based on the well-known Lloyd–Max scalar quantization rule, which is optimal in the sense of achieving minimum mean-square-error distortion. An iterative self-organizing learning rule is derived to determine the threshold levels. The rule does not require any prior information about the histogram, hence is fully automatic. Experimental results show that this new approach is easy to implement yet is highly efficient, robust with respect to noise, and yields reliable estimates of the threshold levels.

On Threshold Selection Using Fuzzy Risk Criterion

A parametric and unsupervised method of automatic threshold selection for image segmentation is presented. An optimal threshold is selected by the fuzzy risk criterion, namely, so as to separate a given picture into meaningful gray level classes under the assumption of object and pixel gray level values being normally distributed. The effectiveness of the proposed approach is illustrated using some experimental images with 256 gray-levels, and the results are compared with minimum error thresholding method using the criterion functions such as uniformity, shape, and edge measures.

Computer image analysis for identification of wear particles

The design of a specialized image analyser to investigate wear particles is described. An automatic grey-level threshold selection method identifies particles from the background. The entire image is enhanced through iterative relaxation labelling. A size distribution of particles within the field of view is produced. The shape of isolated particles can be processed to reveal useful quantitative data that is not produced from human visual examination. Border curvature of these particles is examined as a means of automatic particle recognition. Various aspect ratio measurements, including Fourier descriptors, are also considered. These morphological measurements are used to classify particles into established groups. A number of descriptors such as these are used to form the foundations of a diagnostic expert system.

SPECT volume measurement using an automatic threshold selection method combined with a V filter.

The volume measurement of various organs by single photon emission computed tomography (SPECT) has generally been performed by summing up the voxels lying within the outer edge of the organ determined by thresholding as a function of the maximum reconstructed counts within a volume of interest. The optimum threshold level, however, is influenced by various factors. To eliminate this problem, Mortelmans et al. (1986) proposed to use an automatic threshold selection method (ATSM) based on discriminant criteria and to correct the errors induced by the imperfect system response using the method based on linear regression analysis, which has some problems in practical application. We tried to use ATSM combined with a V filter. The correlation coefficient (r) and the regression equation between the true (x) and the calculated volume (y) obtained by experimentation using 22 phantoms with a volume ranging between 26 ml and 380 ml were as follows: r = 0.994 and y(ml) = 1.015 x +1.09 using ATSM combined with a V filter, while r = 0.993 and y(ml) = 1.110 x +30.55 using only ATSM. The relative error between the true and the calculated volumes decreased significantly (P less than 0.01) from 37.7% +/- 26.9% to 5.5% +/- 4.2%. The results suggest that ATSM combined with a V filter is useful for correcting the errors caused by an imperfect system response, and is available and reliable for SPECT volume determination even when the organ volume is small.

Entropic thresholding, a new approach

This paper describes an automatic threshold selection method for picture segmentation. The basic concept is the definition of an anisotropy coefficient, which is related to the asymmetry of the grey-level histogram. Its use permits the derivation of the entropic thresholds, which has been successfully applied to images having various kinds of histograms. Several experimental results are presented. An extension to multithresholding is also suggested.

A new method for grey-level picture thresholding using the entropy of the histogram

This paper describes an automatic threshold selection method for picture segmentation, using the entropy of the grey level histogram. It is shown that, by an a priori maximation of an entropy determined a posteriori, a picture can successfully be thresholded into a two-level image. Several experimental results are presented to show the validity of the method. An extension to multithresholding and to multidimensional histogram processing is also discussed.