L1-NORM FITTING OF ELLIPTIC PARABOLOIDS WITH PRIOR INFORMATION FOR ENHANCED CONIFEROUS TREE LOCALIZATION IN ALS POINT CLOUDS

Airborne laser scanning (ALS) is an established tool for deriving various tree characteristics in forests. In some applications, an accurate pointwise estimate of the tree position is required. For dense data acquired by TLS or UAV-mounted scanners, this can be achieved by locating the stem, whose center coordinates are then used for deriving the planimetric tree position. However, in case of standard ALS data this is often not an option due to the low probability of obtaining stem hits in operational scenarios of forest mapping campaigns. This paper presents an alternative, indirect approach where the tree position is approximated as the center of a quadric surface which best represents the tree crown shape. The study targets coniferous trees due to their distinct crown shape which may be approximated by an elliptic paraboloid. It is assumed that individual tree point clusters are given and the task is to find the tree center for each cluster. We first consider the general problem of fitting an elliptic paraboloid with a known axis and an L1 residual norm error criterion, which is more robust to outliers compared to least-squares fitting. We formulate this problem as a quadratically constrained quadratic program (QCQP), and show how prior knowledge on the crown shape and center position can be incorporated. Next, a computationally simpler problem is considered where the paraboloid semiaxis lengths are constrained to be equal, and a corresponding linear program is constructed. Experiments on ALS datasets of forest plots from Bavaria, Germany and Oregon, USA reveal that a reduction in median tree position error of up to 20% can be attained compared to both least-squares fitting and other baseline techniques, resulting in an absolute error of ca. 22 cm on both datasets

Cyclodextrin modulation of gallic acid in vitro antibacterial activity

The substitution of large spectrum antibiotics for natural bioactive molecules (especially polyphenolics) for the treatment of wound infections has come into prominence in the pharmaceutical industry. However, the use of such molecules depends on their stability during environmental stress and on their ability to reach the action site without losing biological properties. The application of cyclodextrins as a vehicle for polyphenolics protection has been documented and appears to enhance the properties of bioactive molecules. Therefore, the encapsulation of gallic acid, an antibacterial agent with low stability, by -cyclodextrin, (2-hydroxy) propyl--cyclodextrin and methyl--cyclodextrin, was investigated. Encapsulation by -cyclodextrin was confirmed for pH 3 and 5, with similar stability parameters. The (2-hydroxy) propyl--cyclodextrin and methyl--cyclodextrin interactions with gallic acid were only confirmed at pH 3. Among the three cyclodextrins, better gallic acid encapsulation were observed for (2-hydroxy) propyl--cyclodextrin, followed by -cyclodextrin and methyl--cyclodextrin. The effect of cyclodextrin encapsulation on the gallic acid antibacterial activity was also analysed. The antibacterial activity of the inclusion complexes was investigated here for the first time. According to the results, encapsulation of gallic acid by (2-hydroxy) propyl--cyclodextrin seems to be a viable option for the treatment of skin and soft tissue infections, since this inclusion complex has good stability and antibacterial activity.The authors are grateful for the FCT Strategic Project PEst-OE/EQB/LA0023/2013 and the Project "BioHealth-Biotechnology and Bioengineering approaches to improve health quality", Ref. NORTE-07-0124-FEDER-000027, co-funded by the "Programa Operacional Regional do Norte" (ON.2-O Novo Norte), QREN, FEDER. The authors also acknowledge the project "Consolidating Research Expertise and Resources on Cellular and Molecular Biotechnology at CEB/IBB", Ref. FCOMP-01-0124-FEDER-027462. This work is, also, funded by FEDER funds through the Operational Programme for Competitiveness Factors-COMPETE and National Funds through FCT-Foundation for Science and Technology under the project PEst-C/CTM/UI0264/2011. Additionally, the authors would like to thank the FCT for the grant for E. Pinho (SFRH/BD/62665/2009)

FREE SHAPE CONTEXT DESCRIPTORS OPTIMIZED WITH GENETIC ALGORITHM FOR THE DETECTION OF DEAD TREE TRUNKS IN ALS POINT CLOUDS

In this paper, a new family of shape descriptors called Free Shape Contexts (FSC) is introduced to generalize the existing 3D Shape Contexts. The FSC introduces more degrees of freedom than its predecessor by allowing the level of complexity to vary between its parts. Also, each part of the FSC has an associated activity state which controls whether the part can contribute a feature value. We describe a method of evolving the FSC parameters for the purpose of creating highly discriminative features suitable for detecting specific objects in sparse point clouds. The evolutionary process is built on a genetic algorithm (GA) which optimizes the parameters with respect to cross-validated overall classification accuracy. The GA manipulates both the structure of the FSC and the activity flags, allowing it to perform an implicit feature selection alongside the structure optimization by turning off segments which do not augment the discriminative capabilities. We apply the proposed descriptor to the problem of detecting single standing dead tree trunks from ALS point clouds. The experiment, carried out on a set of 285 objects, reveals that an FSC optimized through a GA with manually tuned recombination parameters is able to attain a classification accuracy of 84.2%,yielding an increase of 4.2 pp compared to features derived from eigenvalues of the 3D covariance matrix. Also, we address the issue of automatically tuning the GA recombination meta-parameters. For this purpose, a fuzzy logic controller (FLC) which dynamically adjusts the magnitude of the recombination effects is co-evolved with the FSC parameters in a two-tier evolution scheme. We find that it is possible to obtain an FLC which retains the classification accuracy of the manually tuned variant, thereby limiting the need for guessing the appropriate meta-parameter values

Semantic labelling of ultra dense MLS point clouds in urban road corridors based on fusing CRF with shape priors

ISPRS Geospatial Week 2017, 18 - 22 September 20172017-2018 > Academic research: refereed > Publication in refereed journal201802 bcrcVersion of RecordPublishe

L1-norm fitting of elliptic paraboloids with prior information for enhanced coniferous tree localization in ALS point clouds

4th ISPRS Geospatial Week 2019, Netherlands, 10-14 June 2019201908 bcmaVersion of RecordPublishe

Vertical orientation correction of uav image-based point clouds using statistical modeling of gable roof geometry

4th ISPRS Geospatial Week 2019, Netherlands, 10-14 June 2019201908 bcmaVersion of RecordPublishe

Mapping individual tree species and vitality along urban road corridors with LiDAR and imaging sensors : point density versus view perspective

201812 bcrcVersion of RecordPublishe

In the danger zone : U-net driven quantile regression can predict high-risk SARS-CoV-2 regions via pollutant particulate matter and satellite imagery

In Proceedings of the 37th International Conference on Machine Learning, 13-18 July 2020, Vienna, Austria, PMLR 108, 2020.202305 bckwAccepted ManuscriptSelf-fundedPublishe

SEMANTIC LABELLING OF ULTRA DENSE MLS POINT CLOUDS IN URBAN ROAD CORRIDORS BASED ON FUSING CRF WITH SHAPE PRIORS

In this paper, a labelling method for the semantic analysis of ultra-high point density MLS data (up to 4000 points/m2) in urban road corridors is developed based on combining a conditional random field (CRF) for the context-based classification of 3D point clouds with shape priors. The CRF uses a Random Forest (RF) for generating the unary potentials of nodes and a variant of the contrastsensitive Potts model for the pair-wise potentials of node edges. The foundations of the classification are various geometric features derived by means of co-variance matrices and local accumulation map of spatial coordinates based on local neighbourhoods. Meanwhile, in order to cope with the ultra-high point density, a plane-based region growing method combined with a rule-based classifier is applied to first fix semantic labels for man-made objects. Once such kind of points that usually account for majority of entire data amount are pre-labeled; the CRF classifier can be solved by optimizing the discriminative probability for nodes within a subgraph structure excluded from pre-labeled nodes. The process can be viewed as an evidence fusion step inferring a degree of belief for point labelling from different sources. The MLS data used for this study were acquired by vehicle-borne Z+F phase-based laser scanner measurement, which permits the generation of a point cloud with an ultra-high sampling rate and accuracy. The test sites are parts of Munich City which is assumed to consist of seven object classes including impervious surfaces, tree, building roof/facade, low vegetation, vehicle and pole. The competitive classification performance can be explained by the diverse factors: e.g. the above ground height highlights the vertical dimension of houses, trees even cars, but also attributed to decision-level fusion of graph-based contextual classification approach with shape priors. The use of context-based classification methods mainly contributed to smoothing of labelling by removing outliers and the improvement in underrepresented object classes. In addition, the routine operation of a context-based classification for such high density MLS data becomes much more efficient being comparable to non-contextual classification schemes