This study aims to compare three different structured light scanner systems to generate accurate 3D human face models. Among these systems, the most dense and expensive one was denoted as the reference and the other two that were low cost and low resolution were compared according to the reference system. One female face and one male face were scanned with three light scanner systems. Point-cloud filtering, mesh generation, and hole-filling steps were carried out using a trial version of commercial software; moreover, the data evaluation process was realized using CloudCompare open-source software. Various filtering and mesh smoothing levels were applied on reference data to compare with other low-cost systems. Thus, the optimum reduction level of reference data was evaluated to continue further processes. The outcome of the presented study shows that low-cost structured light scanners have a great potential for 3D object modeling, including the human face. A considerable cheap structured light system has been used due to its capacity to obtain spatial and morphological information in the case study of 3D human face modeling. This study also discusses the benefits and accuracy of low-cost structured light systems.
ABSTRACT:Coastal monitoring plays a vital role in environmental planning and hazard management related issues. Since shorelines are fundamental data for environment management, disaster management, coastal erosion studies, modelling of sediment transport and coastal morphodynamics, various techniques have been developed to extract shorelines. Random Forest is one of these techniques which is used in this study for shoreline extraction.. This algorithm is a machine learning method based on decision trees. Decision trees analyse classes of training data creates rules for classification. In this study, Terkos region has been chosen for the proposed method within the scope of "TUBITAK Project (Project No: 115Y718) titled" Integration of Unmanned Aerial Vehicles for Sustainable Coastal Zone Monitoring Model -Three-Dimensional Automatic Coastline Extraction and Analysis: Istanbul-Terkos Example ". Random Forest algorithm has been implemented to extract the shoreline of the Black Sea where near the lake from LANDSAT-8 and GOKTURK-2 satellite imageries taken in 2015. The MATLAB environment was used for classification. To obtain land and waterbody classes, the Random Forest method has been applied to NIR bands of LANDSAT-8 (5 th band) and GOKTURK-2 (4 th band) imageries. Each image has been digitized manually and shorelines obtained for accuracy assessment. According to accuracy assessment results, Random Forest method is efficient for both medium and high resolution images for shoreline extraction studies.
Coastal management requires rapid, up-to-date, and correct information. Thus, the determination of coastal movements and its directions has primary importance for coastal managers. For monitoring the change of shorelines, remote sensing data, very high resolution aerial images and orthophoto maps are utilized for detections of change on shorelines. It is possible to monitor coastal changes by extracting the coastline from orthophoto maps. Along the Baltic Sea and Riga Gulf, Latvian coastline length is 496 km. It is rich of coastal resources and natural biodiversity. Around 120 km of coastline are affected by significant coastal changes caused by climate change, storms, erosion, human activities and other reasons and they must be monitored. In this study, an object-oriented approach has been proposed to detect shoreline and detect the changes by using 1:5000 scaled orthophoto maps of Riga-Latvia (3bands, R, G, and NIR) in the years of 2007 and 2013. As many of the authors have mentioned, object-oriented classification method can be more successful than the pixel-based methods especially for high resolution images to avoid mix-classification. In the presented study the eCognition object-oriented fuzzy image processing software has been used. The results were compared to the results derived from manual digitizing. Extracted and manually digitized shorelines have been divided in 5 m segments in x axis. The y coordinates of the new nodes were taken from the original ".dxf" file or computed by interpolation. Thus, the RMS errors of selected points were calculated.
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