scholarly journals A Fully Automated Three-Stage Procedure for Spatio-Temporal Leaf Segmentation with Regard to the B-Spline-Based Phenotyping of Cucumber Plants

2020 ◽  
Vol 13 (1) ◽  
pp. 74
Author(s):  
Corinna Harmening ◽  
Jens-André Paffenholz
Keyword(s):  
Measurement Techniques ◽  
Point Clouds ◽  
Plant Phenotyping ◽  
Data Sets ◽  
Temporal Segmentation ◽  
B Spline ◽  
Crop Science ◽  
Spline Surfaces ◽  
Spatio Temporal ◽  
The Impact

Plant phenotyping deals with the metrological acquisition of plants in order to investigate the impact of environmental factors and a plant’s genotype on its appearance. Phenotyping methods that are used as standard in crop science are often invasive or even destructive. Due to the increase of automation within geodetic measurement systems and with the development of quasi-continuous measurement techniques, geodetic techniques are perfectly suitable for performing automated and non-invasive phenotyping and, hence, are an alternative to standard phenotyping methods. In this contribution, sequentially acquired point clouds of cucumber plants are used to determine the plants’ phenotypes in terms of their leaf areas. The focus of this contribution is on the spatio-temporal segmentation of the acquired point clouds, which automatically groups and tracks those sub point clouds that describe the same leaf. The application on example data sets reveals a successful segmentation of 93% of the leafs. Afterwards, the segmented leaves are approximated by means of B-spline surfaces, which provide the basis for the subsequent determination of the leaf areas. In order to validate the results, the determined leaf areas are compared to results obtained by means of standard methods used in crop science. The investigations reveal consistency of the results with maximal deviations in the determined leaf areas of up to 5%.

scholarly journals On the impact of correlations on the congruence test: a bootstrap approach

2020 ◽  
Vol 55 (3) ◽  
pp. 495-513 ◽  
Author(s):  
Kermarrec Gaël ◽  
Kargoll Boris ◽  
Alkhatib Hamza
Keyword(s):  
Statistical Significance ◽  
Spline Approximation ◽  
Point Clouds ◽  
Test Statistics ◽  
B Spline ◽  
Mode Decomposition ◽  
Spline Surfaces ◽  
Bootstrap Approach ◽  
The Impact ◽  
Congruence Test

AbstractThe detection of deformation is one of the major tasks in surveying engineering. It is meaningful only if the statistical significance of the distortions is correctly investigated, which often underlies a parametric modelization of the object under consideration. So-called regression B-spline approximation can be performed for point clouds of terrestrial laser scanners, allowing the setting of a specific congruence test based on the B-spline surfaces. Such tests are known to be strongly influenced by the underlying stochastic model chosen for the observation errors. The latter has to be correctly specified, which includes accounting for heteroscedasticity and correlations. In this contribution, we justify and make use of a parametric correlation model called the Matérn model to approximate the variance covariance matrix (VCM) of the residuals by performing their empirical mode decomposition. The VCM obtained is integrated into the computation of the congruence test statistics for a more trustworthy test decision. Using a real case study, we estimate the distribution of the test statistics with a bootstrap approach, where no parametric assumptions are made about the underlying population that generated the random sample. This procedure allows us to assess the impact of neglecting correlations on the critical value of the congruence test, highlighting their importance.

scholarly journals Laser Scanner–Based Deformation Analysis Using Approximating B-Spline Surfaces

2021 ◽  
Vol 13 (18) ◽  
pp. 3551
Author(s):  
Corinna Harmening ◽  
Christoph Hobmaier ◽  
Hans Neuner
Keyword(s):  
Rigid Body ◽  
Laser Scanning ◽  
Body Movement ◽  
Control Point ◽  
Measurement Techniques ◽  
Point Clouds ◽  
Deformation Analysis ◽  
Body Movements ◽  
B Spline ◽  
Spline Surfaces

Due to the increased use of areal measurement techniques, such as laser scanning in geodetic monitoring tasks, areal analysis strategies have considerably gained in importance over the last decade. Although a variety of approaches that quasi-continuously model deformations are already proposed in the literature, there are still a multitude of challenges to solve. One of the major interests of engineering geodesy within monitoring tasks is the detection of absolute distortions with respect to a stable reference frame. Determining distortions and simultaneously establishing the joint geodetic datum can be realised by modelling the differences between point clouds acquired in different measuring epochs by means of a rigid body movement that is superimposed by distortions. In a previous study, we discussed the possibility of estimating these rigid body movements from the control points of B-spline surfaces approximating the acquired point clouds. Alternatively, we focus on estimating them by means of constructed points on B-spline surfaces in this study. This strategy has the advantage of larger redundancy compared to the control point–based strategy. Furthermore, the strategy introduced allows for the detection of rigid body movements between point clouds of different epochs and for the simultaneous localisation of areas in which the rigid body movement is superimposed by distortions. The developed approach is based on B-spline models of epoch-wise acquired point clouds, the surface parameters of which define point correspondences on different B-spline surfaces. Using these point correspondences, a RANSAC-approach is used to robustly estimate the parameters of the rigid body movement. The resulting consensus set initially defines the non-distorted areas of the object under investigation, which are extended and statistically verified in a second step. The developed approach is applied to simulated data sets, revealing that distorted areas can be reliably detected and that the parameters of the rigid body movement can be precisely and accurately determined by means of the strategy.

scholarly journals How Significant Are Differences Obtained by Neglecting Correlations When Testing for Deformation: A Real Case Study Using Bootstrapping with Terrestrial Laser Scanner Observations Approximated by B-Spline Surfaces

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3640 ◽  
Author(s):  
Kermarrec ◽  
Paffenholz ◽  
Alkhatib
Keyword(s):  
Stochastic Model ◽  
Statistical Tests ◽  
Point Clouds ◽  
Least Square ◽  
Test Statistics ◽  
B Spline ◽  
3D Point Clouds ◽  
Spline Surfaces ◽  
Propagation Law ◽  
The Impact

B-spline surfaces possess attractive properties such as a high degree of continuity or the local support of their basis functions. One of the major applications of B-spline surfaces in engineering geodesy is the least-square (LS) fitting of surfaces from, e.g., 3D point clouds obtained from terrestrial laser scanners (TLS). Such mathematical approximations allow one to test rigorously with a given significance level the deformation magnitude between point clouds taken at different epochs. Indeed, statistical tests cannot be applied when point clouds are processed in commonly used software such as CloudCompare, which restrict the analysis of deformation to simple deformation maps based on distance computation. For a trustworthy test decision and a resulting risk management, the stochastic model of the underlying observations needs, however, to be optimally specified. Since B-spline surface approximations necessitate Cartesian coordinates of the TLS observations, the diagonal variance covariance matrix (VCM) of the raw TLS measurements has to be transformed by means of the error propagation law. Unfortunately, this procedure induces mathematical correlations, which can strongly affect the chosen test statistics to analyse deformation, if neglected. This may lead potentially to rejecting wrongly the null hypothesis of no-deformation, with risky and expensive consequences. In this contribution, we propose to investigate the impact of mathematical correlations on test statistics, using real TLS observations from a bridge under load. As besides TLS, a highly precise laser tracker (LT) was used, the significance of the difference of the test statistics when the stochastic model is misspecified can be assessed. However, the underlying test distribution is hardly tractable so that only an adapted bootstrapping allows the computation of trustworthy p-values. Consecutively, the extent to which heteroscedasticity and mathematical correlations can be neglected or simplified without impacting the test decision is shown in a rigorous way, paving the way for a simplification based on the intensity model.

scholarly journals MULTIVARIATE MATHEMATICAL MORPHOLOGY FOR DCE-MRI IMAGE ANALYSIS IN ANGIOGENESIS STUDIES

2014 ◽  
Vol 34 (1) ◽  
pp. 1 ◽  
Author(s):  
Guillaume Noyel ◽  
Jesus Angulo ◽  
Dominique Jeulin ◽  
Daniel Balvay ◽  
Charles-André Cuenod
Keyword(s):  
Noise Reduction ◽  
Data Sets ◽  
Computer Aided Detection ◽  
Segmentation Method ◽  
Temporal Segmentation ◽  
Dce Mri ◽  
Contrast Enhanced ◽  
Key Points ◽  
Multivariate Images ◽  
Spatio Temporal

We propose a new computer aided detection framework for tumours acquired on DCE-MRI (Dynamic Contrast Enhanced Magnetic Resonance Imaging) series on small animals. To perform this approach, we consider DCE-MRI series as multivariate images. A full multivariate segmentation method based on dimensionality reduction, noise filtering, supervised classification and stochastic watershed is explained and tested on several data sets. The two main key-points introduced in this paper are noise reduction preserving contours and spatio temporal segmentation by stochastic watershed. Noise reduction is performed in a special way to select factorial axes of Factor Correspondence Analysis in order to preserves contours. Then a spatio-temporal approach based on stochastic watershed is used to segment tumours. The results obtained are in accordance with the diagnosis of the medical doctors.

Choosing the optimal number of B-spline control points (Part 2: Approximation of surfaces and applications)

2017 ◽  
Vol 11 (1) ◽  
Author(s):  
Corinna Harmening ◽  
Hans Neuner
Keyword(s):  
Data Analysis ◽  
Laser Scanner ◽  
Point Clouds ◽  
Optimal Number ◽  
Deformation Analysis ◽  
Control Points ◽  
Trial And Error ◽  
B Spline ◽  
B Splines ◽  
Spline Surfaces

AbstractFreeform surfaces like B-splines have proven to be a suitable tool to model laser scanner point clouds and to form the basis for an areal data analysis, for example an areal deformation analysis.A variety of parameters determine the B-spline's appearance, the B-spline's complexity being mostly determined by the number of control points. Usually, this parameter type is chosen by intuitive trial-and-error-procedures.In [The present paper continues these investigations. If necessary, the methods proposed in [The application of those methods to B-spline surfaces reveals the datum problem of those surfaces, meaning that location and number of control points of two B-splines surfaces are only comparable if they are based on the same parameterization. First investigations to solve this problem are presented.

scholarly journals Estimating Control Points for B-Spline Surfaces Using Fully Populated Synthetic Variance–Covariance Matrices for TLS Point Clouds

2021 ◽  
Vol 13 (16) ◽  
pp. 3124
Author(s):  
Jakob Raschhofer ◽  
Gabriel Kerekes ◽  
Corinna Harmening ◽  
Hans Neuner ◽  
Volker Schwieger
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
Covariance Matrices ◽  
Geometric Modelling ◽  
Deformation Analysis ◽  
Control Points ◽  
B Splines ◽  
Spline Surfaces ◽  
Standard Deviations ◽  
The Impact

A flexible approach for geometric modelling of point clouds obtained from Terrestrial Laser Scanning (TLS) is by means of B-splines. These functions have gained some popularity in the engineering geodesy as they provide a suitable basis for a spatially continuous and parametric deformation analysis. In the predominant studies on geometric modelling of point clouds by B-splines, uncorrelated and equally weighted measurements are assumed. Trying to overcome this, the elementary errors theory is applied for establishing fully populated covariance matrices of TLS observations that consider correlations in the observed point clouds. In this article, a systematic approach for establishing realistic synthetic variance–covariance matrices (SVCMs) is presented and afterward used to model TLS point clouds by B-splines. Additionally, three criteria are selected to analyze the impact of different SVCMs on the functional and stochastic components of the estimation results. Plausible levels for variances and covariances are obtained using a test specimen of several dm—dimension. It is used to identify the most dominant elementary errors under laboratory conditions. Starting values for the variance level are obtained from a TLS calibration. The impact of SVCMs with different structures and different numeric values are comparatively investigated. Main findings of the paper are that for the analyzed object size and distances, the structure of the covariance matrix does not significantly affect the location of the estimated surface control points, but their precision in terms of the corresponding standard deviations. Regarding the latter, properly setting the main diagonal terms of the SVCM is of superordinate importance compared to setting the off-diagonal ones. The investigation of some individual errors revealed that the influence of their standard deviation on the precision of the estimated parameters is primarily dependent on the scanning distance. When the distance stays the same, one-sided influences on the precision of the estimated control points can be observed with an increase in the standard deviations.

scholarly journals Deformation Analysis Using B-Spline Surface with Correlated Terrestrial Laser Scanner Observations—A Bridge Under Load

2020 ◽  
Vol 12 (5) ◽  
pp. 829 ◽  
Author(s):  
Gaël Kermarrec ◽  
Boris Kargoll ◽  
Hamza Alkhatib
Keyword(s):  
Laser Scanner ◽  
Point Clouds ◽  
Testing Procedure ◽  
Local Deformation ◽  
Measurement Noise ◽  
Deformation Analysis ◽  
Terrestrial Laser Scanner ◽  
B Spline ◽  
Set Up ◽  
The Impact

The choice of an appropriate metric is mandatory to perform deformation analysis between two point clouds (PC)—the distance has to be trustworthy and, simultaneously, robust against measurement noise, which may be correlated and heteroscedastic. The Hausdorff distance (HD) or its averaged derivation (AHD) are widely used to compute local distances between two PC and are implemented in nearly all commercial software. Unfortunately, they are affected by measurement noise, particularly when correlations are present. In this contribution, we focus on terrestrial laser scanner (TLS) observations and assess the impact of neglecting correlations on the distance computation when a mathematical approximation is performed. The results of the simulations are extended to real observations from a bridge under load. Highly accurate laser tracker (LT) measurements were available for this experiment: they allow the comparison of the HD and AHD between two raw PC or between their mathematical approximations regarding reference values. Based on these results, we determine which distance is better suited in the case of heteroscedastic and correlated TLS observations for local deformation analysis. Finally, we set up a novel bootstrap testing procedure for this distance when the PC are approximated with B-spline surfaces.

scholarly journals Pheno4D: A spatio-temporal dataset of maize and tomato plant point clouds for phenotyping and advanced plant analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256340
Author(s):  
David Schunck ◽  
Federico Magistri ◽  
Radu Alexandru Rosu ◽  
André Cornelißen ◽  
Nived Chebrolu ◽  
...  
Keyword(s):  
High Resolution ◽  
3D Reconstruction ◽  
Point Clouds ◽  
Training Data ◽  
Plant Phenotyping ◽  
Growth Stages ◽  
Plant Analysis ◽  
Rigid Registration ◽  
3D Point Clouds ◽  
Spatio Temporal

Understanding the growth and development of individual plants is of central importance in modern agriculture, crop breeding, and crop science. To this end, using 3D data for plant analysis has gained attention over the last years. High-resolution point clouds offer the potential to derive a variety of plant traits, such as plant height, biomass, as well as the number and size of relevant plant organs. Periodically scanning the plants even allows for performing spatio-temporal growth analysis. However, highly accurate 3D point clouds from plants recorded at different growth stages are rare, and acquiring this kind of data is costly. Besides, advanced plant analysis methods from machine learning require annotated training data and thus generate intense manual labor before being able to perform an analysis. To address these issues, we present with this dataset paper a multi-temporal dataset featuring high-resolution registered point clouds of maize and tomato plants, which we manually labeled for computer vision tasks, such as for instance segmentation and 3D reconstruction, providing approximately 260 million labeled 3D points. To highlight the usability of the data and to provide baselines for other researchers, we show a variety of applications ranging from point cloud segmentation to non-rigid registration and surface reconstruction. We believe that our dataset will help to develop new algorithms to advance the research for plant phenotyping, 3D reconstruction, non-rigid registration, and deep learning on raw point clouds. The dataset is freely accessible at https://www.ipb.uni-bonn.de/data/pheno4d/.

scholarly journals MONITORING THE SPATIO-TEMPORAL TRAJECTORY OF URBAN AREA HOTSPOTS IN WUHAN, CHINA USING TIME-SERIES NIGHTTIME LIGHT IMAGES

2019 ◽  
Vol XLII-4/W20 ◽  
pp. 71-76
Author(s):  
Y. L. Ruan ◽  
Y. H. Zou
Keyword(s):  
Urban Area ◽  
Human Activities ◽  
Urban Expansion ◽  
Data Sets ◽  
Threshold Method ◽  
Expansion Pattern ◽  
Nighttime Light ◽  
Spatio Temporal ◽  
Urban Extent ◽  
The Impact

Abstract. Urban area hotspots can be considered as an ideal representation of spatial heterogeneity of human activities within a city, which is susceptible to regional urban expansion pattern pattern. However, in previous studies most researchers focused on extracting urban extent, leaving the interior variation of nighttime radiance intensity poorly explored. With the help of multi-source data sets such as DMSP/OLS (NTL), LST and NDVI, we proposed an applicable framework to identify and monitor the spatiotemporal trajectory of polycentric urban area hotspots. Firstly, the original NTL dataset were calibrated to reduce inconsistency and discontinuity. And we integrated NTL, LST as well as NDVI and established an urban index TVANUI capturing the approximate urban extents. Secondly, multi-resolution segmentation algorithm, neighborhood statistics analysis and a local-optimized threshold method were employed to get more precise urban extent with an overall accuracy above 85% and a Kappa above 0.70. Thirdly, the urban extents were utilized as masks to get corresponding radiance intensity from calibrated NTL. Finally, we established the Gaussian volume model for each cluster and the resulting parameters were used to quantitatively depict hotspot features (i.e., intensity, morphology and centroid dynamics). All the identified urban hotspot showed our framework could successfully capture polycentric urban hotspots, whose fitting coefficients were over 0.7. The spatiotemporal trajectory of hotspot powerfully revealed the impact of the regional urban growth pattern and planning strategies on human activities in the city of Wuhan. This study provides important insights for further studies on the relationship between the regional urbanization and human activities.

scholarly journals SPATIO-TEMPORAL DYNAMICS ALONG THE TERRAIN GRADIENT OF DIVERSE LANDSCAPE

2014 ◽  
Vol 22 (1) ◽  
pp. 50-63 ◽  
Author(s):  
T. V. Ramachandra ◽  
Settur Bharath ◽  
Aithal Bharath
Keyword(s):  
Land Cover ◽  
Environmental Changes ◽  
Temporal Dynamics ◽  
Demographic Data ◽  
Coastal Region ◽  
Data Sets ◽  
Lulc Change ◽  
Multi Temporal ◽  
Spatio Temporal ◽  
The Impact

Land use (LU) land cover (LC) information at a temporal scale illustrates the physical coverage of the Earth’s terrestrial surface according to its use and provides the intricate information for effective planning and management activities. LULC changes are stated as local and location specifc, collectively they act as drivers of global environmental changes. Understanding and predicting the impact of LULC change processes requires long term historical restorations and projecting into the future of land cover changes at regional to global scales. The present study aims at quantifying spatio temporal landscape dynamics along the gradient of varying terrains presented in the landscape by multi-data approach (MDA). MDA incorporates multi temporal satellite imagery with demographic data and other additional relevant data sets. The gradient covers three different types of topographic features, planes; hilly terrain and coastal region to account the signifcant role of elevation in land cover change. The seasonality is another aspect to be considered in the vegetation dominated landscapes; variations are accounted using multi seasonal data. Spatial patterns of the various patches are identifed and analysed using landscape metrics to understand the forest fragmentation. The prediction of likely changes in 2020 through scenario analysis has been done to account for the changes, considering the present growth rates and due to the proposed developmental projects. This work summarizes recent estimates on changes in cropland, agricultural intensifcation, deforestation, pasture expansion, and urbanization as the causal factors for LULC change.

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