Robust statistical approaches for local planar surface fitting in 3D laser scanning data

This paper proposes robust methods for local planar surface fitting in 3D laser scanning data. Searching through the literature revealed that many authors frequently used Least Squares (LS) and Principal Component Analysis (PCA) for point cloud processing without any treatment of outliers. It is known that LS and PCA are sensitive to outliers and can give inconsistent and misleading estimates. RANdom SAmple Consensus (RANSAC) is one of the most well-known robust methods used for model fitting when noise and/or outliers are present. We concentrate on the recently introduced Deterministic Minimum Covariance Determinant estimator and robust PCA, and propose two variants of statistically robust algorithms for fitting planar surfaces to 3D laser scanning point cloud data. The performance of the proposed robust methods is demonstrated by qualitative and quantitative analysis through several synthetic and mobile laser scanning 3D data sets for different applications. Using simulated data, and comparisons with LS, PCA, RANSAC, variants of RANSAC and other robust statistical methods, we demonstrate that the new algorithms are significantly more efficient, faster, and produce more accurate fits and robust local statistics (e.g. surface normals), necessary for many point cloud processing tasks.Consider one example data set used consisting of 100 points with 20% outliers representing a plane. The proposed methods called DetRD-PCA and DetRPCA, produce bias angles (angle between the fitted planes with and without outliers) of 0.20° and 0.24° respectively, whereas LS, PCA and RANSAC produce worse bias angles of 52.49°, 39.55° and 0.79° respectively. In terms of speed, DetRD-PCA takes 0.033 s on average for fitting a plane, which is approximately 6.5, 25.4 and 25.8 times faster than RANSAC, and two other robust statistical methods, respectively. The estimated robust surface normals and curvatures from the new methods have been used for plane fitting, sharp feature preservation and segmentation in 3D point clouds obtained from laser scanners. The results are significantly better and more efficiently computed than those obtained by existing methods

Outlier detection and robust normal-curvature estimation in mobile laser scanning 3D point cloud data

This paper proposes two robust statistical techniques for outlier detection and robust saliency features, such as surface normal and curvature, estimation in laser scanning 3D point cloud data. One is based on a robust z-score and the other uses a Mahalanobis type robust distance. The methods couple the ideas of point to plane orthogonal distance and local surface point consistency to get Maximum Consistency with Minimum Distance (MCMD). The methods estimate the best-fit-plane based on most probable outlier free, and most consistent, points set in a local neighbourhood. Then the normal and curvature from the best-fit-plane will be highly robust to noise and outliers. Experiments are performed to show the performance of the algorithms compared to several existing well-known methods (from computer vision, data mining, machine learning and statistics) using synthetic and real laser scanning datasets of complex (planar and non-planar) objects. Results for plane fitting, denoising, sharp feature preserving and segmentation are significantly improved. The algorithms are demonstrated to be significantly faster, more accurate and robust. Quantitatively, for a sample size of 50 with 20% outliers the proposed MCMD_Z is approximately 5, 15 and 98 times faster than the existing methods: uLSIF, RANSAC and RPCA, respectively. The proposed MCMD_MD method can tolerate 75% clustered outliers, whereas, RPCA and RANSAC can only tolerate 47% and 64% outliers, respectively. In terms of outlier detection, for the same dataset, MCMD_Z has an accuracy of 99.72%, 0.4% false positive rate and 0% false negative rate; for RPCA, RANSAC and uLSIF, the accuracies are 97.05%, 47.06% and 94.54%, respectively, and they have misclassification rates higher than the proposed methods. The new methods have potential for local surface reconstruction, fitting, and other point cloud processing tasks

ROBUST TECHNIQUES FOR BUILDING FOOTPRINT EXTRACTION IN AERIAL LASER SCANNING 3D POINT CLOUDS

The building footprint is crucial for a volumetric 3D representation of a building that is applied in urban planning, 3D city modeling, cadastral and topographic map generation. Aerial laser scanning (ALS) has been recognized as the most suitable means of large-scale 3D point cloud data (PCD) acquisition. PCD can produce geometric detail of a scanned surface. However, it is almost impossible to get point clouds without noise and outliers. Besides, data incompleteness and occlusions are two common phenomena for PCD. Most of the existing methods for building footprint extraction employ classification, segmentation, voting techniques (e.g., Hough-Transform or RANSAC), or Principal Component Analysis (PCA) based methods. It is known that classical PCA is highly sensitive to outliers, even RANSAC which is known as a robust technique for shape detection is not free from outlier effects. This paper presents a novel algorithm that employs MCMD (maximum consistency within minimum distance), MSAC (a robust variant of RANSAC) and a robust regression to extract reliable building footprints in the presence of outliers, missing points and irregular data distributions. The algorithm is successfully demonstrated through two sets of ALS PCD

Prevalence of anopheline species and their Plasmodium infection status in epidemic-prone border areas of Bangladesh

<p>Abstract</p> <p>Background</p> <p>Information related to malaria vectors is very limited in Bangladesh. In the changing environment and various <it>Anopheles </it>species may be incriminated and play role in the transmission cycle. This study was designed with an intention to identify anopheline species and possible malaria vectors in the border belt areas, where the malaria is endemic in Bangladesh.</p> <p>Methods</p> <p><it>Anopheles </it>mosquitoes were collected from three border belt areas (Lengura, Deorgachh and Matiranga) during the peak malaria transmission season (May to August). Three different methods were used: human landing catches, resting collecting by mouth aspirator and CDC light traps. Enzyme-linked immunosorbent assay (ELISA) was done to detect <it>Plasmodium falciparum</it>, <it>Plasmodium vivax</it>-210 and <it>Plasmodium vivax</it>-247 circumsporozoite proteins (CSP) from the collected female species.</p> <p>Results</p> <p>A total of 634 female <it>Anopheles </it>mosquitoes belonging to 17 species were collected. <it>Anopheles vagus </it>(was the dominant species (18.6%) followed by <it>Anopheles nigerrimus </it>(14.5%) and <it>Anopheles philippinensis </it>(11.0%). Infection rate was found 2.6% within 622 mosquitoes tested with CSP-ELISA. Eight (1.3%) mosquitoes belonging to five species were positive for <it>P. falciparum</it>, seven (1.1%) mosquitoes belonging to five species were positive for <it>P. vivax </it>-210 and a single mosquito (0.2%) identified as <it>Anopheles maculatus </it>was positive for <it>P. vivax</it>-247. No mixed infection was found. Highest infection rate was found in <it>Anopheles karwari </it>(22.2%) followed by <it>An. maculatus </it>(14.3%) and <it>Anopheles barbirostris </it>(9.5%). Other positive species were <it>An. nigerrimus </it>(4.4%), <it>An. vagus </it>(4.3%), <it>Anopheles subpictus </it>(1.5%) and <it>An. philippinensis </it>(1.4%). <it>Anopheles vagus </it>and <it>An. philippinensis </it>were previously incriminated as malaria vector in Bangladesh. In contrast, <it>An. karwari</it>, <it>An. maculatus</it>, <it>An. barbirostris</it>, <it>An. nigerrimus </it>and <it>An. subpictus </it>had never previously been incriminated in Bangladesh.</p> <p>Conclusion</p> <p>Findings of this study suggested that in absence of major malaria vectors there is a possibility that other <it>Anopheles </it>species may have been playing role in malaria transmission in Bangladesh. Therefore, further studies are required with the positive mosquito species found in this study to investigate their possible role in malaria transmission in Bangladesh.</p

Antimicrobial activity of endophytic fungi isolated from the mangrove plant Sonneratia apetala (Buch.-Ham) from the Sundarbans mangrove forest

Endophytic fungi reside in the intercellular space of plant nourished by the plant. In return, they provide bioactive molecules which can play critical roles on plant defense system. Fifty six endophytes were isolated from the leaves, root, bark and fruits of Sonneratia apetala, a pioneer mangrove plant in the Sundarbans, Bangladesh. A total of 56 isolates were obtained and 12 different species within 8 genera were identified using morphological and molecular characteristics. Antimicrobial activity of Ethyl Acetate (EtOAc) and Methanolic (MeOH) extracts of these 12 different species were analyzed by resazurin assay and the Minimum Inhibitory Concentrations (MICs) were determined. The fungal extracts showed antimicrobial activities against more than one tested bacterium or fungus among 5 human pathogenic microbes, i.e. Escherichia coli NCTC 12241, Staphylococcus aureus NCTC 12981, Micrococcus lutus NCTC 7508, Pseudomonas aeruginosa NCTC 7508 and Candida albicans ATCC 90028. Overall, Methanolic extracts showed greater activity than that of Ethyl Acetate extracts. Of the isolates identified, Colletotrichum gloeosporioides, Aspergillus niger and Fusarium equiseti were the most active isolates and showed activity against microorganisms under investigation. Methanolic extracts of C. gloeosporioides and A. niger showed the lowest MIC (0.0024 mg/mL) against Pseudomonas aeruginosa. The study indicates that endophytic fungi isolated from S. apetala species posses potential antimicrobial properties, which could be further investigated

Robust and Diagnostic Statistics: A Few Basic Concepts in Mobile Mapping Point Cloud Data Analysis

It is impractical to imagine point cloud data obtained from laser scanner based mobile mapping systems without outliers. The presence of outliers affects the most often used classical statistical techniques used in laser scanning point cloud data analysis and hence the consequent results of point cloud processing are inaccurate and non-robust. Therefore, it is necessary to use robust and/or diagnostic statistical methods for reliable estimates, modelling, fitting and feature extraction. In spite of the limitations of classical statistical methods, an extensive literature search shows not much use of robust techniques in point cloud data analysis. This paper presents the basic ideas on mobile mapping technology and point cloud data, investigates outlier problems and presents some applicable robust and diagnostic statistical approaches. Importance and performance of robust and diagnostic techniques are shown for planar surface fitting and surface segmentation by using several mobile mapping real point cloud data examples

Robust Locally Weighted Regression Techniques for Ground Surface Points Filtering in Mobile Laser Scanning Three Dimensional Point Cloud Data

This paper introduces robust algorithms for extracting the ground points in laser scanning 3-D point cloud data. Global polynomial functions have been used for filtering algorithms for point cloud data; however, it is not suitable as it may lead to bias for the filtering algorithms and can cause misclassification errors when many different objects are present. In this paper, robust statistical approaches are coupled with locally weighted 2-D regression that fits without any predefined global function for the variables of interest. Algorithms are performed iteratively on 2-D profiles: $x-z$ and $y-z$. The $z$ (elevation) values are robustly down weighted based on the residuals for the fitted points. The new set of down-weighted $z$ values, along with the corresponding $x$ (or $y$) values, is used to get a new fit for the lower surface level. The process of fitting and down weighting continues until the difference between two consecutive fits is insignificant. The final fit is the required ground level, and the ground surface points are those that fall within the ground level and the level after adding some threshold value with the ground level for $z$ values. Experimental results are compared with the recently proposed segmentation method through simulated and real mobile laser scanning point clouds from urban areas that include many objects that appear in road scenes such as short walls, large buildings, electric poles, signposts, and cars. Results show that the proposed robust methods efficiently extract ground surface points with better than 97% accuracy

Suspension of Friday and Daily Congregational Prayers during Pandemic: A Juristic Maqasidic Study: التوقف عن صلاة الجمعة والجماعات في زمن الوباء: دراسة فقهية مقاصدية

This study analyses the rulings of Islamic Shari'ah regarding the suspension of daily and weekly congregational prayers during COVID-19 or any other situation like this. Since saving lives from any harm is one of the major objectives of Sharīʿah, it prefers performing prayers at home instead of going to the mosque for prayer in congregation during COVID-19 pandemic. Descriptive method has been followed in this article through incorporating the opinions of Muslim scholars and some international fiqh academies regarding the suspension of congregational prayers during COVID-19. The study, primarily, reveals Sharīʿah rulings for three issues of congressional prayers during any pandemic or emergency situation. They are: firstly, the ruling for Friday and daily congregational prayers in the mosque during COVID-19; secondly, ruling for attending Friday and congregational daily prayers for those who are already affected; thirdly, ruling for attending Friday and daily congregational prayers in the mosques by following preventive measures. http://aif-doi.org/IJFUS/05020

Robust methods for feature extraction from mobile laser scanning 3D point clouds

Three dimensional point cloud data obtained from mobile laser scanning systems commonly contain outliers. In the presence of outliers most of the currently used methods such as principal component analysis for point cloud processing and feature extraction produce inaccurate and unreliable results. This paper investigates the problems of outliers, and explores advantages of recently introduced statistically robust methods for automatic robust feature extraction. The robust algorithms outperform classical methods and show distinct advantages over well-known robust methods such as RANSAC in terms of accuracy and robustness. This paper shows the importance and advantages of several recently introduced robust statistics based algorithms for (i) planar surface fitting, (ii) surface normal estimation, (iii) edge detection, and (iv) segmentation. Experimental results for real mobile laser scanning point cloud data consisting of planar and non-planar complex objects surfaces show the proposed robust methods are more accurate and robust. The robust algorithms have potential for surface reconstruction, 3D modelling, registration, and quality control for point cloud data

Identification of multiple influential observations in logistic regression

The identification of influential observations in logistic regression has drawn a great deal of attention in recent years. Most of the available techniques like Cook's distance and difference of fits (DFFITS) are based on single-case deletion. But there is evidence that these techniques suffer from masking and swamping problems and consequently fail to detect multiple influential observations. In this paper, we have developed a new measure for the identification of multiple influential observations in logistic regression based on a generalized version of DFFITS. The advantage of the proposed method is then investigated through several well-referred data sets and a simulation study.generalized DFFITS, generalized Studentized Pearson residual, generalized weight, high leverage point, influential observation, masking, outlier, swamping,