3D Edge Detection Based on Boolean Functions and Local Operators

Edge detection is one of the most commonly used operations in image processing and computer vision areas. Edges correspond to the boundaries between regions in an image, which are useful for object segmentation and recognition tasks. This work presents a novel method for 3D edge detection based on Boolean functions and local operators, which is an extension of the 2D edge detector introduced by Vemis et al. [Signal Processing45(2), 161–172 (1995)] The proposed method is composed of two main steps. An adaptive binarization process is initially applied to blocks of the image and the resulting binary map is processed with a set of Boolean functions to identify edge points within the blocks. A global threshold, calculated to estimate image intensity variation, is then used to reduce false edges in the image blocks. The proposed method is compared to other 3D gradient filters: Canny, Monga–Deriche, Zucker–Hummel and Sobel operators. Experimental results demonstrate the effectiveness of the proposed technique when applied to several 3D synthetic and real data sets.

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The Automatic Non-Negative Matrix Factorization of the Hierarchy Clustering Method

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.325-326.1489 ◽

2013 ◽

Vol 325-326 ◽

pp. 1489-1492

Author(s):

Tie Qi Li ◽

Wen Shuo Zhang

Keyword(s):

Matrix Factorization ◽

Vector Space Model ◽

Real Data ◽

Data Sets ◽

Text Data ◽

Space Model ◽

Hierarchical Relations ◽

Weight Calculation ◽

Novel Method ◽

Non Negative Matrix Factorization

People in such huge information how to find useful information becomes a problem. In order to deal with hierarchical relations in text data, a novel method, called automatic non-negative matrix factorization of the hierarchy clustering, is proposed for the text mining. We use the vector space model as the research foundation, mainly discusses the feature selection and weight calculation two problems. The experimental results on the real data sets demonstrate that our method outperforms, on average, all the other 6 methods.

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Finding Patterns in Signals Using Lossy Text Compression

Algorithms ◽

10.3390/a12120267 ◽

2019 ◽

Vol 12 (12) ◽

pp. 267 ◽

Cited By ~ 2

Author(s):

Liat Rozenberg ◽

Sagi Lotan ◽

Dan Feldman

Keyword(s):

Autonomous Robots ◽

Real Data ◽

Micro Air Vehicles ◽

Data Sets ◽

Text Compression ◽

Run Length ◽

Reverse Engineer ◽

Home Work ◽

Noisy Signals ◽

Novel Method

Whether the source is autonomous car, robotic vacuum cleaner, or a quadcopter, signals from sensors tend to have some hidden patterns that repeat themselves. For example, typical GPS traces from a smartphone contain periodic trajectories such as “home, work, home, work, ⋯”. Our goal in this study was to automatically reverse engineer such signals, identify their periodicity, and then use it to compress and de-noise these signals. To do so, we present a novel method of using algorithms from the field of pattern matching and text compression to represent the “language” in such signals. Common text compression algorithms are less tailored to handle such strings. Moreover, they are lossless, and cannot be used to recover noisy signals. To this end, we define the recursive run-length encoding (RRLE) method, which is a generalization of the well known run-length encoding (RLE) method. Then, we suggest lossy and lossless algorithms to compress and de-noise such signals. Unlike previous results, running time and optimality guarantees are proved for each algorithm. Experimental results on synthetic and real data sets are provided. We demonstrate our system by showing how it can be used to turn commercial micro air-vehicles into autonomous robots. This is by reverse engineering their unpublished communication protocols and using a laptop or on-board micro-computer to control them. Our open source code may be useful for both the community of millions of toy robots users, as well as for researchers that may extend it for further protocols.

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INTRINSIC DIMENSIONALITY ESTIMATION WITHIN NEIGHBORHOOD CONVEX HULL

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001409007016 ◽

2009 ◽

Vol 23 (01) ◽

pp. 31-44 ◽

Cited By ~ 2

Author(s):

CHUN-GUANG LI ◽

JUN GUO ◽

BO XIAO

Keyword(s):

Convex Hull ◽

General Framework ◽

Data Distribution ◽

Real Data ◽

High Dimensional ◽

Data Sets ◽

Data Set ◽

Novel Method ◽

Linear Reconstruction ◽

Intrinsic Dimensionality

In this paper, a novel method to estimate the intrinsic dimensionality of high-dimensional data set is proposed. Based on neighborhood information, our method calculates the non-negative locally linear reconstruction coefficients from its neighbors for each data point, and the numbers of those dominant positive reconstruction coefficients are regarded as a faithful guide to the intrinsic dimensionality of data set. The proposed method requires no parametric assumption on data distribution and is easy to implement in the general framework of manifold learning. Experimental results on several synthesized data sets and real data sets have shown the benefits of the proposed method.

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Transforming variables to central normality

Machine Learning ◽

10.1007/s10994-021-05960-5 ◽

2021 ◽

Author(s):

Jakob Raymaekers ◽

Peter J. Rousseeuw

Keyword(s):

Maximum Likelihood ◽

Maximum Likelihood Estimator ◽

Simulation Study ◽

Real Data ◽

Data Sets ◽

Transformation Parameter ◽

Likelihood Estimator ◽

Extensive Simulation ◽

Highly Sensitive

AbstractMany real data sets contain numerical features (variables) whose distribution is far from normal (Gaussian). Instead, their distribution is often skewed. In order to handle such data it is customary to preprocess the variables to make them more normal. The Box–Cox and Yeo–Johnson transformations are well-known tools for this. However, the standard maximum likelihood estimator of their transformation parameter is highly sensitive to outliers, and will often try to move outliers inward at the expense of the normality of the central part of the data. We propose a modification of these transformations as well as an estimator of the transformation parameter that is robust to outliers, so the transformed data can be approximately normal in the center and a few outliers may deviate from it. It compares favorably to existing techniques in an extensive simulation study and on real data.

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A New Extension of Thinning-Based Integer-Valued Autoregressive Models for Count Data

Entropy ◽

10.3390/e23010062 ◽

2020 ◽

Vol 23 (1) ◽

pp. 62

Author(s):

Zhengwei Liu ◽

Fukang Zhu

Keyword(s):

Likelihood Estimation ◽

Real Data ◽

Autoregressive Models ◽

Superior Performance ◽

Data Sets ◽

Binomial Thinning ◽

Free Case ◽

Two Parameters ◽

Conditional Maximum ◽

Thinning Operator

The thinning operators play an important role in the analysis of integer-valued autoregressive models, and the most widely used is the binomial thinning. Inspired by the theory about extended Pascal triangles, a new thinning operator named extended binomial is introduced, which is a general case of the binomial thinning. Compared to the binomial thinning operator, the extended binomial thinning operator has two parameters and is more flexible in modeling. Based on the proposed operator, a new integer-valued autoregressive model is introduced, which can accurately and flexibly capture the dispersed features of counting time series. Two-step conditional least squares (CLS) estimation is investigated for the innovation-free case and the conditional maximum likelihood estimation is also discussed. We have also obtained the asymptotic property of the two-step CLS estimator. Finally, three overdispersed or underdispersed real data sets are considered to illustrate a superior performance of the proposed model.

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Goodness-of-Fit Tests for Bivariate Time Series of Counts

Econometrics ◽

10.3390/econometrics9010010 ◽

2021 ◽

Vol 9 (1) ◽

pp. 10

Author(s):

Šárka Hudecová ◽

Marie Hušková ◽

Simos G. Meintanis

Keyword(s):

Goodness Of Fit ◽

Probability Generating Function ◽

Parametric Bootstrap ◽

Real Data ◽

Data Sets ◽

Test Statistics ◽

Finite Sample ◽

Generalized Poisson ◽

Goodness Of Fit Tests ◽

Monte Carlo Experiments

This article considers goodness-of-fit tests for bivariate INAR and bivariate Poisson autoregression models. The test statistics are based on an L2-type distance between two estimators of the probability generating function of the observations: one being entirely nonparametric and the second one being semiparametric computed under the corresponding null hypothesis. The asymptotic distribution of the proposed tests statistics both under the null hypotheses as well as under alternatives is derived and consistency is proved. The case of testing bivariate generalized Poisson autoregression and extension of the methods to dimension higher than two are also discussed. The finite-sample performance of a parametric bootstrap version of the tests is illustrated via a series of Monte Carlo experiments. The article concludes with applications on real data sets and discussion.

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TraceAll: A Real-Time Processing for Contact Tracing Using Indoor Trajectories

Information ◽

10.3390/info12050202 ◽

2021 ◽

Vol 12 (5) ◽

pp. 202

Author(s):

Louai Alarabi ◽

Saleh Basalamah ◽

Abdeltawab Hendawi ◽

Mohammed Abdalla

Keyword(s):

Infectious Diseases ◽

Infected Patient ◽

Public Health Problem ◽

Real Data ◽

Exposure Period ◽

Contact Tracing ◽

Data Sets ◽

Major Public Health Problem ◽

Real Time Processing ◽

Recent Developments

The rapid spread of infectious diseases is a major public health problem. Recent developments in fighting these diseases have heightened the need for a contact tracing process. Contact tracing can be considered an ideal method for controlling the transmission of infectious diseases. The result of the contact tracing process is performing diagnostic tests, treating for suspected cases or self-isolation, and then treating for infected persons; this eventually results in limiting the spread of diseases. This paper proposes a technique named TraceAll that traces all contacts exposed to the infected patient and produces a list of these contacts to be considered potentially infected patients. Initially, it considers the infected patient as the querying user and starts to fetch the contacts exposed to him. Secondly, it obtains all the trajectories that belong to the objects moved nearby the querying user. Next, it investigates these trajectories by considering the social distance and exposure period to identify if these objects have become infected or not. The experimental evaluation of the proposed technique with real data sets illustrates the effectiveness of this solution. Comparative analysis experiments confirm that TraceAll outperforms baseline methods by 40% regarding the efficiency of answering contact tracing queries.

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The Flexible Burr X-G Family: Properties, Inference, and Applications in Engineering Science

Symmetry ◽

10.3390/sym13030474 ◽

2021 ◽

Vol 13 (3) ◽

pp. 474

Author(s):

Abdulhakim A. Al-Babtain ◽

Ibrahim Elbatal ◽

Hazem Al-Mofleh ◽

Ahmed M. Gemeay ◽

Ahmed Z. Afify ◽

...

Keyword(s):

Numerical Simulations ◽

Exponential Distribution ◽

Real Data ◽

Exponential Model ◽

Statistical Properties ◽

Engineering Science ◽

Data Sets ◽

Engineering Sciences ◽

General Statistical ◽

Anderson Darling

In this paper, we introduce a new flexible generator of continuous distributions called the transmuted Burr X-G (TBX-G) family to extend and increase the flexibility of the Burr X generator. The general statistical properties of the TBX-G family are calculated. One special sub-model, TBX-exponential distribution, is studied in detail. We discuss eight estimation approaches to estimating the TBX-exponential parameters, and numerical simulations are conducted to compare the suggested approaches based on partial and overall ranks. Based on our study, the Anderson–Darling estimators are recommended to estimate the TBX-exponential parameters. Using two skewed real data sets from the engineering sciences, we illustrate the importance and flexibility of the TBX-exponential model compared with other existing competing distributions.

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Calibration of Camera and Flash LiDAR System with a Triangular Pyramid Target

Applied Sciences ◽

10.3390/app11020582 ◽

2021 ◽

Vol 11 (2) ◽

pp. 582

Author(s):

Zean Bu ◽

Changku Sun ◽

Peng Wang ◽

Hang Dong

Keyword(s):

Simulated Data ◽

Real Data ◽

Calibration Method ◽

Multiple Sensors ◽

Triangular Pyramid ◽

World Coordinate System ◽

Flash Lidar ◽

Novel Method ◽

3D Information ◽

Incremental Validation

Calibration between multiple sensors is a fundamental procedure for data fusion. To address the problems of large errors and tedious operation, we present a novel method to conduct the calibration between light detection and ranging (LiDAR) and camera. We invent a calibration target, which is an arbitrary triangular pyramid with three chessboard patterns on its three planes. The target contains both 3D information and 2D information, which can be utilized to obtain intrinsic parameters of the camera and extrinsic parameters of the system. In the proposed method, the world coordinate system is established through the triangular pyramid. We extract the equations of triangular pyramid planes to find the relative transformation between two sensors. One capture of camera and LiDAR is sufficient for calibration, and errors are reduced by minimizing the distance between points and planes. Furthermore, the accuracy can be increased by more captures. We carried out experiments on simulated data with varying degrees of noise and numbers of frames. Finally, the calibration results were verified by real data through incremental validation and analyzing the root mean square error (RMSE), demonstrating that our calibration method is robust and provides state-of-the-art performance.

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Kumaraswamy Generalized Power Lomax Distributionand Its Applications

Stats ◽

10.3390/stats4010003 ◽

2021 ◽

Vol 4 (1) ◽

pp. 28-45

Author(s):

Vasili B.V. Nagarjuna ◽

R. Vishnu Vardhan ◽

Christophe Chesneau

Keyword(s):

Hazard Rate ◽

Real Data ◽

Rate Function ◽

Maximum Likelihood Estimates ◽

Parameter Estimates ◽

Parameter Distribution ◽

Data Sets ◽

Lomax Distribution ◽

Entropy Measures ◽

Modeling Behavior

In this paper, a new five-parameter distribution is proposed using the functionalities of the Kumaraswamy generalized family of distributions and the features of the power Lomax distribution. It is named as Kumaraswamy generalized power Lomax distribution. In a first approach, we derive its main probability and reliability functions, with a visualization of its modeling behavior by considering different parameter combinations. As prime quality, the corresponding hazard rate function is very flexible; it possesses decreasing, increasing and inverted (upside-down) bathtub shapes. Also, decreasing-increasing-decreasing shapes are nicely observed. Some important characteristics of the Kumaraswamy generalized power Lomax distribution are derived, including moments, entropy measures and order statistics. The second approach is statistical. The maximum likelihood estimates of the parameters are described and a brief simulation study shows their effectiveness. Two real data sets are taken to show how the proposed distribution can be applied concretely; parameter estimates are obtained and fitting comparisons are performed with other well-established Lomax based distributions. The Kumaraswamy generalized power Lomax distribution turns out to be best by capturing fine details in the structure of the data considered.

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