scholarly journals Depth-Image Segmentation Based on Evolving Principles for 3D Sensing of Structured Indoor Environments

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4395
Author(s):  
Miloš Antić ◽  
Andrej Zdešar ◽  
Igor Škrjanc
Keyword(s):  
Image Segmentation ◽  
High Performance ◽  
Average Rate ◽  
Principal Component ◽  
Point Clouds ◽  
Data Locality ◽  
Noise Model ◽  
Depth Image ◽  
Indoor Environments ◽  
Depth Sensors

This paper presents an approach of depth image segmentation based on the Evolving Principal Component Clustering (EPCC) method, which exploits data locality in an ordered data stream. The parameters of linear prototypes, which are used to describe different clusters, are estimated in a recursive manner. The main contribution of this work is the extension and application of the EPCC to 3D space for recursive and real-time detection of flat connected surfaces based on linear segments, which are all detected in an evolving way. To obtain optimal results when processing homogeneous surfaces, we introduced two-step filtering for outlier detection within a clustering framework and considered the noise model, which allowed for the compensation of characteristic uncertainties that are introduced into the measurements of depth sensors. The developed algorithm was compared with well-known methods for point cloud segmentation. The proposed approach achieves better segmentation results over longer distances for which the signal-to-noise ratio is low, without prior filtering of the data. On the given database, an average rate higher than 90% was obtained for successfully detected flat surfaces, which indicates high performance when processing huge point clouds in a non-iterative manner.

scholarly journals Automated Measurement of Heart Girth for Pigs Using Two Kinect Depth Sensors

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3848
Author(s):  
Xinyue Zhang ◽  
Gang Liu ◽  
Ling Jing ◽  
Siyao Chen
Keyword(s):  
Mirror Symmetry ◽  
Point Cloud ◽  
Point Clouds ◽  
Depth Image ◽  
View Point ◽  
Important Indicator ◽  
Depth Sensors ◽  
Starting Point ◽  
Feature Based ◽  
Girth Measurement

The heart girth parameter is an important indicator reflecting the growth and development of pigs that provides critical guidance for the optimization of healthy pig breeding. To overcome the heavy workloads and poor adaptability of traditional measurement methods currently used in pig breeding, this paper proposes an automated pig heart girth measurement method using two Kinect depth sensors. First, a two-view pig depth image acquisition platform is established for data collection; the two-view point clouds after preprocessing are registered and fused by feature-based improved 4-Point Congruent Set (4PCS) method. Second, the fused point cloud is pose-normalized, and the axillary contour is used to automatically extract the heart girth measurement point. Finally, this point is taken as the starting point to intercept the circumferential perpendicular to the ground from the pig point cloud, and the complete heart girth point cloud is obtained by mirror symmetry. The heart girth is measured along this point cloud using the shortest path method. Using the proposed method, experiments were conducted on two-view data from 26 live pigs. The results showed that the heart girth measurement absolute errors were all less than 4.19 cm, and the average relative error was 2.14%, which indicating a high accuracy and efficiency of this method.

scholarly journals Automatic Extraction of Indoor Structural Information from Point Clouds

2021 ◽  
Vol 13 (23) ◽  
pp. 4930
Author(s):  
Dongyang Cheng ◽  
Junchao Zhang ◽  
Dangjun Zhao ◽  
Jianlai Chen ◽  
Di Tian
Keyword(s):  
Nearest Neighbor ◽  
Structural Information ◽  
Ground Plane ◽  
Principal Component ◽  
Point Clouds ◽  
Maximum Error ◽  
Two Dimensions ◽  
Dimensional Structure ◽  
Indoor Environments ◽  
Structure Information

We propose an innovative method with which to extract building interior structure information automatically, including ceiling, floor, and wall. Our approach outperforms previous methods in the following respects. First, we propose an approach based on principal component analysis (PCA) to find the ground plane, which is regarded as the new Cartesian plane. Second, to reduce the complexity of data processing, the data are projected into two dimensions and transformed into a binary image via the operation of an improved radius outlier removal (ROR) filter. Third, a traditional thinning algorithm is adopted to extract the image skeleton. Then, we propose a method for calculating slope through the nearest neighbor point. Moreover, the line is represented with the slopes to obtain information pertaining to the interior planes. Finally, the outline of the line is restored to a three-dimensional structure. The proposed method is evaluated in multiple scenarios, and the results show that the method is accurate (the maximum error of 0.03 m was in three scenarios) in indoor environments.

The Utilization of Ftir (Fourier Transform Infra-Red) Method Combined with Chemometrics For Authentication of Indonesian Coffee Powder

2017 ◽  
Vol 9 (3) ◽  
Author(s):  
Firmansyah A. ◽  
Winingsih W. ◽  
Soebara Y S
Keyword(s):  
Fourier Transform ◽  
Natural Product ◽  
High Performance ◽  
Principal Component ◽  
Raw Material ◽  
Attenuated Total Reflectance ◽  
Coffee Bean ◽  
Product Analysis ◽  
Total Reflectance ◽  
And Cluster Analysis

Analysis of natural product remain challenging issues for analytical chemist, since natural products are complicated system of mixture. The most popular methods of choice used for quality control of raw material and finished product are high performance liquid chromatography (HPLC), gas chromatography (GC) and mass spectrometry (MS). The utilization of FTIR-ATR (Fourier Transform Infrared-Attenuated Total Reflectance) method in natural product analysis is still limited. This study attempts to expand the use of FTIR spectroscopy in authenticating Indonesian coffee powder.The coffee samples studied were taken from nine regions in Indonesia, namely Aceh Gayo, Flores, Kintamani, Mandheling, Papua, Sidikalang, Toraja, Kerinci and Lampung.The samples in the form of coffee bean from various regions were powdered . The next step conducted was to determine the spectrum using the FTIR-ATR (Attenuated Total Reflectance) using ZnSe crystal of 8000 resolution. Spectrum samples, then, were analyzed using chemometrics. The utilized chemometric model was the principal component analysis (PCA) and cluster analysis (CA). Based on the chemometric analysis, there are similarities between Aceh Gayo coffee with Toraja coffee, Mandailing coffee, Kintamani coffee and Flores coffee. Sidikalang coffee has a similarity to Flores coffee; Papua coffee has a similarity to Sidikalang coffee; Lampung coffee has a similarity to Sidikalang coffee, while Kerinci coffee has a similarity to Papua coffee.

scholarly journals Adsorption Mechanism of Patulin from Apple Juice by Inactivated Lactic Acid Bacteria Isolated from Kefir Grains

Toxins ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 434
Author(s):  
Pascaline Bahati ◽  
Xuejun Zeng ◽  
Ferdinand Uzizerimana ◽  
Ariunsaikhan Tsoggerel ◽  
Muhammad Awais ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
High Performance ◽  
Apple Juice ◽  
Adsorption Mechanism ◽  
Health Hazard ◽  
Principal Component ◽  
Poor Quality ◽  
Potential Health ◽  
Potential Health Hazard ◽  
Kefir Grains

In the food industry, microbiological safety is a major concern. Mycotoxin patulin represents a potential health hazard, as it is heat-resistant and may develop at any stage during the food chain, especially in apple-based products, leading to severe effects on human health, poor quality products, and profit reductions. The target of the study was to identify and characterize an excellent adsorbent to remove patulin from apple juice efficiently and to assess its adsorption mechanism. To prevent juice fermentation and/or contamination, autoclaving was involved to inactivate bacteria before the adsorption process. The HPLC (high-performance liquid chromatography) outcome proved that all isolated strains from kefir grains could reduce patulin from apple juice. A high removal of 93% was found for juice having a 4.6 pH, 15° Brix, and patulin concentration of 100 μg/L by Lactobacillus kefiranofacien, named JKSP109, which was morphologically the smoothest and biggest of all isolates in terms of cell wall volume and surface area characterized by SEM (Scanning electron microscopy) and TEM (transmission electron microscopy). C=O, OH, C–H, and N–O were the main functional groups engaged in patulin adsorption indicated by FTIR (Fourier transform–infrared). E-nose (electronic nose) was performed to evaluate the aroma quality of the juices. PCA (Principal component analysis) results showed that no significant changes occurred between control and treated juice.

scholarly journals Parametric Surface Modelling for Tea Leaf Point Cloud Based on Non-Uniform Rational Basis Spline Technique

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1304
Author(s):  
Wenchao Wu ◽  
Yongguang Hu ◽  
Yongzong Lu
Keyword(s):  
Point Cloud ◽  
Principal Component ◽  
Point Clouds ◽  
Parametric Surface ◽  
B Spline ◽  
Surface Modelling ◽  
Modelling Method ◽  
3D Architecture ◽  
Tea Leaf ◽  
Fitting Error

Plant leaf 3D architecture changes during growth and shows sensitive response to environmental stresses. In recent years, acquisition and segmentation methods of leaf point cloud developed rapidly, but 3D modelling leaf point clouds has not gained much attention. In this study, a parametric surface modelling method was proposed for accurately fitting tea leaf point cloud. Firstly, principal component analysis was utilized to adjust posture and position of the point cloud. Then, the point cloud was sliced into multiple sections, and some sections were selected to generate a point set to be fitted (PSF). Finally, the PSF was fitted into non-uniform rational B-spline (NURBS) surface. Two methods were developed to generate the ordered PSF and the unordered PSF, respectively. The PSF was firstly fitted as B-spline surface and then was transformed to NURBS form by minimizing fitting error, which was solved by particle swarm optimization (PSO). The fitting error was specified as weighted sum of the root-mean-square error (RMSE) and the maximum value (MV) of Euclidean distances between fitted surface and a subset of the point cloud. The results showed that the proposed modelling method could be used even if the point cloud is largely simplified (RMSE < 1 mm, MV < 2 mm, without performing PSO). Future studies will model wider range of leaves as well as incomplete point cloud.

scholarly journals Individual Tree Extraction from Terrestrial LiDAR Point Clouds Based on Transfer Learning and Gaussian Mixture Model Separation

2021 ◽  
Vol 13 (2) ◽  
pp. 223
Author(s):  
Zhenyang Hui ◽  
Shuanggen Jin ◽  
Dajun Li ◽  
Yao Yevenyo Ziggah ◽  
Bo Liu
Keyword(s):  
Transfer Learning ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Principal Component ◽  
Point Clouds ◽  
Gaussian Mixture ◽  
Individual Tree ◽  
Terrestrial Lidar ◽  
Initial Segmentation ◽  
Tree Extraction

Individual tree extraction is an important process for forest resource surveying and monitoring. To obtain more accurate individual tree extraction results, this paper proposed an individual tree extraction method based on transfer learning and Gaussian mixture model separation. In this study, transfer learning is first adopted in classifying trunk points, which can be used as clustering centers for tree initial segmentation. Subsequently, principal component analysis (PCA) transformation and kernel density estimation are proposed to determine the number of mixed components in the initial segmentation. Based on the number of mixed components, the Gaussian mixture model separation is proposed to separate canopies for each individual tree. Finally, the trunk stems corresponding to each canopy are extracted based on the vertical continuity principle. Six tree plots with different forest environments were used to test the performance of the proposed method. Experimental results show that the proposed method can achieve 87.68% average correctness, which is much higher than that of other two classical methods. In terms of completeness and mean accuracy, the proposed method also outperforms the other two methods.

scholarly journals Matching Point Clouds with STL Models by Using the Principle Component Analysis and a Decomposition into Geometric Primitives

2021 ◽  
Vol 11 (5) ◽  
pp. 2268
Author(s):  
Erika Straková ◽  
Dalibor Lukáš ◽  
Zdenko Bobovský ◽  
Tomáš Kot ◽  
Milan Mihola ◽  
...  
Keyword(s):  
Point Cloud ◽  
Principal Component ◽  
Point Clouds ◽  
Component Analysis ◽  
Language Models ◽  
Computational Time ◽  
Dominant Eigenvalue ◽  
Eigenvalues And Eigenvectors ◽  
Correct Model ◽  
Geometric Primitives

While repairing industrial machines or vehicles, recognition of components is a critical and time-consuming task for a human. In this paper, we propose to automatize this task. We start with a Principal Component Analysis (PCA), which fits the scanned point cloud with an ellipsoid by computing the eigenvalues and eigenvectors of a 3-by-3 covariant matrix. In case there is a dominant eigenvalue, the point cloud is decomposed into two clusters to which the PCA is applied recursively. In case the matching is not unique, we continue to distinguish among several candidates. We decompose the point cloud into planar and cylindrical primitives and assign mutual features such as distance or angle to them. Finally, we refine the matching by comparing the matrices of mutual features of the primitives. This is a more computationally demanding but very robust method. We demonstrate the efficiency and robustness of the proposed methodology on a collection of 29 real scans and a database of 389 STL (Standard Triangle Language) models. As many as 27 scans are uniquely matched to their counterparts from the database, while in the remaining two cases, there is only one additional candidate besides the correct model. The overall computational time is about 10 min in MATLAB.

scholarly journals A Novel Validated Injectable Colistimethate Sodium Analysis Combining Advanced Chemometrics and Design of Experiments

Molecules ◽  
2021 ◽  
Vol 26 (6) ◽  
pp. 1546
Author(s):  
Ioanna Dagla ◽  
Anthony Tsarbopoulos ◽  
Evagelos Gikas
Keyword(s):  
Design Of Experiments ◽  
High Performance ◽  
Similarity Index ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Distance Measures ◽  
Linear Regression Models ◽  
Colistimethate Sodium

Colistimethate sodium (CMS) is widely administrated for the treatment of life-threatening infections caused by multidrug-resistant Gram-negative bacteria. Until now, the quality control of CMS formulations has been based on microbiological assays. Herein, an ultra-high-performance liquid chromatography coupled to ultraviolet detector methodology was developed for the quantitation of CMS in injectable formulations. The design of experiments was performed for the optimization of the chromatographic parameters. The chromatographic separation was achieved using a Waters Acquity BEH C8 column employing gradient elution with a mobile phase consisting of (A) 0.001 M aq. ammonium formate and (B) methanol/acetonitrile 79/21 (v/v). CMS compounds were detected at 214 nm. In all, 23 univariate linear-regression models were constructed to measure CMS compounds separately, and one partial least-square regression (PLSr) model constructed to assess the total CMS amount in formulations. The method was validated over the range 100–220 μg mL−1. The developed methodology was employed to analyze several batches of CMS injectable formulations that were also compared against a reference batch employing a Principal Component Analysis, similarity and distance measures, heatmaps and the structural similarity index. The methodology was based on freely available software in order to be readily available for the pharmaceutical industry.

scholarly journals Grill Workers Exposure to Polycyclic Aromatic Hydrocarbons: Levels and Excretion Profiles of the Urinary Biomarkers

2020 ◽  
Vol 18 (1) ◽  
pp. 230
Author(s):  
Marta Oliveira ◽  
Sílvia Capelas ◽  
Cristina Delerue-Matos ◽  
Simone Morais
Keyword(s):  
Polycyclic Aromatic Hydrocarbons ◽  
Aromatic Hydrocarbons ◽  
High Performance ◽  
Principal Component ◽  
Fold Increase ◽  
Urinary Biomarkers ◽  
Analysis Model ◽  
Exposed Workers ◽  
Polycyclic Aromatic ◽  
The Impact

Grilling activities release large amounts of hazardous pollutants, but information on restaurant grill workers’ exposure to polycyclic aromatic hydrocarbons (PAHs) is almost inexistent. This study assessed the impact of grilling emissions on total workers’ exposure to PAHs by evaluating the concentrations of six urinary biomarkers of exposure (OHPAHs): naphthalene, acenaphthene, fluorene, phenanthrene, pyrene, and benzo(a)pyrene. Individual levels and excretion profiles of urinary OHPAHs were determined during working and nonworking periods. Urinary OHPAHs were quantified by high-performance liquid-chromatography with fluorescence detection. Levels of total OHPAHs (∑OHPAHs) were significantly increased (about nine times; p ≤ 0.001) during working comparatively with nonworking days. Urinary 1-hydroxynaphthalene + 1-hydroxyacenapthene and 2-hydroxyfluorene presented the highest increments (ca. 23- and 6-fold increase, respectively), followed by 1-hydroxyphenanthrene (ca. 2.3 times) and 1-hydroxypyrene (ca. 1.8 times). Additionally, 1-hydroxypyrene levels were higher than the benchmark, 0.5 µmol/mol creatinine, in 5% of exposed workers. Moreover, 3-hydroxybenzo(a)pyrene, biomarker of exposure to carcinogenic PAHs, was detected in 13% of exposed workers. Individual excretion profiles showed a cumulative increase in ∑OHPAHs during consecutive working days. A principal component analysis model partially discriminated workers’ exposure during working and nonworking periods showing the impact of grilling activities. Urinary OHPAHs were increased in grill workers during working days.

scholarly journals SPPD: A Novel Reassembly Method for 3D Terracotta Warrior Fragments Based on Fracture Surface Information

2021 ◽  
Vol 10 (8) ◽  
pp. 525
Author(s):  
Wenmin Yao ◽  
Tong Chu ◽  
Wenlong Tang ◽  
Jingyu Wang ◽  
Xin Cao ◽  
...  
Keyword(s):  
Fracture Surface ◽  
Principal Component ◽  
Point Clouds ◽  
Three Dimension ◽  
Registration Method ◽  
Series Of Experiments ◽  
Coarse To Fine ◽  
Fine Registration ◽  
Common Situation ◽  
Better Than

As one of China′s most precious cultural relics, the excavation and protection of the Terracotta Warriors pose significant challenges to archaeologists. A fairly common situation in the excavation is that the Terracotta Warriors are mostly found in the form of fragments, and manual reassembly among numerous fragments is laborious and time-consuming. This work presents a fracture-surface-based reassembling method, which is composed of SiamesePointNet, principal component analysis (PCA), and deep closest point (DCP), and is named SPPD. Firstly, SiamesePointNet is proposed to determine whether a pair of point clouds of 3D Terracotta Warrior fragments can be reassembled. Then, a coarse-to-fine registration method based on PCA and DCP is proposed to register the two fragments into a reassembled one. The above two steps iterate until the termination condition is met. A series of experiments on real-world examples are conducted, and the results demonstrate that the proposed method performs better than the conventional reassembling methods. We hope this work can provide a valuable tool for the virtual restoration of three-dimension cultural heritage artifacts.

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