Sawing off the branch you are sitting on

This article argues that vowel reduction can be insightfully understood by reinterpreting openness as structural instead of melodic (i.e., mediated by an element). This allows for a unified account of various reduction phenomena in different languages and also extends to lenition in consonants. The proposal made here is couched within Government Phonology 2.0, a further development of Government Phonology

Does Turkish diss harmony?

This article presents a Government Phonology (GP) analysis of disharmonic words in Turkish. According to GP, phonology is exceptionless. Following this claim, I will argue that the generalisations intended to capture vowel harmony in Turkish had been stated in the wrong way, leading to disharmonic words as an artefact of a faulty analysis. Once this is remedied, the exceptions vanish, allowing for a unified treatment of harmonic and disharmonic words. This also takes into account further details of the Turkish vowel system which had not been incorporated in previous analyses, as well as distributional asymmetries between stems and suffixes

GP 2, and Putonghua too

The article illustrates some of the salient features of Government Phonology (GP) 2.0 by axiomatising (a subclass of) the set of possible Putonghua forms.We show that a phonological theory can profit by assuming that phonological representations are hierarchical, just like syntactic representations. A structural relation of c++command, a relative of the well-known c-command, is used heavily. The similarity with syntax is further underlined by the introduction of a phonological Binding Theory: illicit representations are prohibited by the LUxI Principles, the phonological counterpart of Principles A, B and C

Reconstrucción digital de estructuras de tejados históricos: desarrollo de un flujo de trabajo de análisis altamente automatizado

[EN] Planning on adaptive reuse, maintenance and restoration of historic timber structuresrequiresextensive architectural and structural analysis of the actual condition. Current methods for a modellingof roof constructions consist of several manual steps including the time-consuming dimensional modelling. The continuous development of terrestrial laser scanners increases the accuracy, comfort and speed of the surveying work inroof constructions. Resultingpoint clouds enabledetailed visualisation of theconstructionsrepresented by single points or polygonal meshes, but in fact donot containinformation about the structural system and the beam elements. The developed workflow containsseveral processing steps on the point cloud dataset. The most important among them arethenormal vector computation, the segmentation of points to extract planarfaces, a classification of planarsegmentsto detect the beam side facesand finally theparametric modelling of the beams on the basis of classified segments. Thisenablesa highly automated transitionfrom raw point cloud data to a geometric model containing beams of the structural system. The geometric model,as well as additional information about the structural properties of involved wooden beams and their joints,is necessaryinput for a furtherstructural modellingof timber constructions. The results of the workflow confirm that the proposed methods work well for beams with a rectangularcross-section and minor deformations. Scan shadows and occlusionof beamsby additional installationsor interlockingbeamsdecreases the modelling performance, but in generala high level ofaccuracy and completeness isachieved ata high degree of automation.[ES] Las estructuras históricas de madera requieren un análisis arquitectónico y estructural exhaustivo de su condición real en aras de planificar la reutilización flexible, el mantenimiento y la restauración. Los métodos actuales que modelan las construcciones de cubiertas pasan por aplicar varias etapas en modo manual, que incluye el lento modelado dimensional. El desarrollo continuo de escáneres láser terrestres aumenta la exactitud, la comodidad y la velocidad del trabajo topográfico en construcciones de tejados. Las nubes de puntos resultantes permiten la visualización detallada de las construcciones representadas por puntos o mallas poligonales, pero de hecho no contienen información sobre el sistema estructural y los elementos del travesaño. El flujo de trabajo desarrollado contiene varias etapas de procesamiento en el conjunto de datos de la nube de puntos. Los más importantes son el cálculo del vector normal, la segmentación de puntos que extraen caras planas, la clasificación de segmentos planos que detectan las caras laterales del travesaño y, finalmente, el modelado paramétrico de los travesaños en función de los segmentos clasificados. Esto permite una transición altamente automatizada de los datos de la nube de puntos brutos a un modelo geométrico que contiene los travesaños del sistema estructural. El modelo geométrico, así como la información adicional sobre las propiedades estructurales de las vigas de madera involucradas y de sus juntas, es información necesaria de entrada para el modelado estructural eventual de las construcciones de madera. Los resultados del flujo de trabajo confirman que los métodos propuestos funcionan bien en travesaños que presentan secciones transversales rectangulares y deformaciones menores. Las sombras en los escaneados y las oclusiones de los travesaños a partir de instalaciones adicionales o vigas entrelazados disminuye el rendimiento del modelado, pero en general se logra un nivel de exactitud e integridad elevado con un alto grado de automatización.Pöchtrager, M.; Styhler-Aydın, G.; Döring-Williams, M.; Pfeifer, N. (2018). Digital reconstruction of historic roof structures: developing a workflow for a highly automated analysis. Virtual Archaeology Review. 9(19):21-33. doi:10.4995/var.2018.8855SWORD2133919Attene, M., & Spagnuolo, M. (2000). Automatic surface reconstruction from point sets in space. Computer Graphics Forum, 19(3), 457-465. doi:10.1111/1467-8659.00438Baik, A., Yaagoubi, R., & Boehm, J. (2015). Integration of Jeddah historical BIM and 3D GIS for documentation and restoration of historical monument. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-5/W7, 29-34. doi:10.5194/isprsarchives-XL-5-W7-29-2015Bassier, M., Hadjidemetriou, G., Vergauwen, M., Van Roy, N., & Verstrynge, E. (2016). Implementation of Scan-to-BIM and FEM for the Documentation and Analysis of Heritage Timber Roof Structures. In M. Ioannides, E. Fink, A. Moropoulou, M. Hagedorn-Saupe, A. Fresa, G. Liestøl, . . . P. Grussenmeyer (Ed.), Digital Heritage. Progress in Cultural Heritage: Documentation, Preservation, and Protection. EuroMed 2016 (pp. 79-90). Springer, Cham. doi:10.1007/978-3-319-48496-9_7Besl, P., & McKay, N. (1992). A method for registration of 3D Shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14, 239-254. doi:10.1109/34.121791Chida, A., & Masuda, H. (2016). Reconstruction of polygonal prisms from point-clouds of engineering facilities. Journal of Computational Design and Engineering, 3(4), 322-329. doi:10.1016/j.jcde.2016.05.003Dore, C., & Murphy, M. (2017). Current state of the art historic building information modelling. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2/W5, 185-192. doi:10.5194/isprsarchives-XLII-2-W5-185-2017Dorninger, P., Nothegger, C., & Rasztovits, S. (2013). Efficient 3-D documentation of Neptune fountain in the park of Schönbrunn palace at millimeter scale. Proceedings XXIV International CIPA Symposium, ISPRS Annals, II, 5/W1, 103-108. doi:10.5194/isprsannals-II-5-W1-103-2013Eßer, G., Styhler-Aydın, G., & Hochreiner, G. (2016a). Construction history and structural assessment of historic roofs - An interdisciplinary approach. In K. Van Balen, & E. Verstrynge (Eds.), Structural analysis of historical constructions. Anamnesis, diagnosis, therapy, controls (pp. 790-795). London, GB.Eßer, G., Styhler-Aydın, G., & Hochreiner, G. (2016b). The historic roof structures of the Vienna Hofburg: An innovative interdisciplinary approach in architectural sciences laying ground for structural modeling. In J. Eberhardsteiner, W. Winter, A. Fadai, & M. Pöll (Eds.), WCTE 2016. World conference on timber engineering (pp. 3039-3047). Wien, Austria.Fischler, M., & Bolles, R. (1981). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6), 381-395. doi:10.1145/358669.358692Glira, P., Pfeifer, N., Briese, C., & Ressl, C. (2015). A Correspondence Framework for ALS Strip Adjustments based on Variants of the ICP Algorithm. Photogrammetrie, Fernerkundung, Geoinformation, 4, 275-289. doi:10.1127/pfg/2015/0270Hochreiner, G., Eßer, G., & Styhler-Aydın, G. (2016). Modern timber engineering methods in the context of historical timber structures. In J. Eberhardsteiner, W. Winter, A. Fadai, & M. Pöll (Eds.), WCTE 2016. World conference on timber engineering (pp. 4830-4838). Wien, Austria.Hoppe, H., DeRose, T., Duchamp, T., McDonald, J., & Stuetzle, W. (1992). Surface reconstruction from unorganized points. SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques. ACM SIGGRAPH Computer Graphics, 26(2), 71-78. doi:10.1145/142920.134011International Organization for Standardization. (2016). Industrial automation systems and integration -- Product data representation and exchange -- Part 21: Implementation methods: Clear text encoding of the exchange Structure. ISO/DIS Standard No. 10303-21. Retrieved from https://www.iso.org/standard/63141.html.Jung, J., Hong, S., Jeong, S., Kim, S., Cho, H., Hong, S., & Heo, J. (2014). Productive modeling for development of asbuilt BIM of existing indoor structures. Automation in Construction, 42, 68-77. doi:10.1016/j.autcon.2014.02.021Kazhdan, M., Bolitho, M., & Hoppe, H. (2006). Poisson surface reconstruction. Symposium on Geometry Processing (pp. 61-70). The Eurographics Association. doi:10.2312/SGP/SGP06/061-070Lee, J., Son, H., Kim, C., & Kim, C. (2013). Skeleton-based 3-D reconstruction of as-built pipelines from laser-scan data. Automation in Reconstruction, 35, 199-207. doi:10.1061/9780784412343.0031Li, W., Goodchild, M., & Church, R. (2013). An efficient measure of compactness for two-dimensional shapes and its application in regionalization problems. International Journal of Geographical Information Science, 1227-1250. doi:10.1080/13658816.2012.752093Nothegger, C., & Dorninger, P. (2009). 3D filtering of high-resolution terrestrial laser scanner point clouds for cultural heritage documentation. Photogrammetrie, Fernerkundung, Geoinformation, 1, 53-63. doi:10.1127/0935-1221/2009/0006Pfeifer, N., & Winterhalder, D. (2004). Modelling of tree cross sections from terrestrial laser scanning data with free-form curves. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 36(8/W2), 76-81.Pfeifer, N., Mandlburger, G., Otepka, J., & Karel, W. (2014). OPALS - A framework for Airborne Laser Scanning data analysis. Computers, Environment and Urban Systems, 45, 125-136. doi:10.1016/j.compenvurbsys.2013.11.002Pöchtrager, M., Styhler-Aydın, G., Döring-Williams, M., & Pfeifer, N. (2017). Automated Reconstruction of Historic Roof Structures from Point Clouds - Development and Examples. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-2-W2, 195-202. doi:10.5194/isprs-annals-IV-2-W2-195-2017Rabbani, T., Dijkman, S., Van den Heuvel, F., & Vosselman, G. (2007). An integrated approach for modelling and global registration of point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 61(6), 355-370. doi:10.1016/j.isprsjprs.2006.09.006Raumonen, P., Kaasalainen, M., Åkerblom, M., Kaasalainen, S., Kaartinen, H., Vastaranta, M., . . . Lewis, P. (2013). Fast automatic precision tree models from terrestrial laser scanner data. Remote Sensing, 5(2), 491-520. doi:10.3390/rs5020491Stylianidis, E., & Remondino, F. (2016). 3D Recording, Documentation and Management of Cultural Heritage. Caithness, UK: Whittles Publishing.Thies, M., Pfeifer, N., Winterhalder, D., & Gorte, B. (2004). Three-dimensional reconstruction of stems for assessment of taper, sweep and lean based on laser scanning of standing trees. Scandinavian Journal of Forest Research, 19(6), 571-581. doi:10.1080/02827580410019562Thomson, C., & Boehm, J. (2015). Automatic geometry generation from point clouds for BIM. Remote Sensing, 7(9), 11753-11775. doi:10.3390/rs70911753Vosselman, G., & Maas, H.-G. (2010). Airborne and Terrestrial Laser Scanning. Caithness, UK: Whittles Publishing.Wang, D., Hollaus, M., Puttonen, E., & Pfeifer, N. (2016). Automatic and self-adaptive stem reconstruction in landslide-affected forests. Remote Sensing, 8(12), p. 974. doi:10.3390/rs8120974Wang, D., Kankare, V., Puttonen, E., Hollaus, M., & Pfeifer, N. (2016). Reconstructing stem cross section shapes from terrestrial laser scanning. IEEE Geoscience and Remote Sensing Letters, 14(2), 272-276. doi:10.1109/LGRS.2016.2638738Xiong, X., Adan, A., Akinci, B., & Huber, D. (2013). Automatic creation of semantically rich 3D building models from laser scanner data. Automation in Construction, 31, S. 325-337. doi:10.1016/j.autcon.2012.10.006Yang, X., Koehl, M., & Grussenmeyer, P. (2017). Parametric modelling of as-built beam framed structure in BIM environment. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2/W3, 651-657. doi:10.5194/isprs-archives-XLII-2-W3-651-2017Zhang, R., & Zakhor, A. (2014). Automatic identification of window regions on indoor point clouds using LiDAR and cameras. Applications of Computer Vision (WACV), 2014 IEEE Winter Conference, 107-114. doi:10.1109/WACV.2014.683611

On the Typology of Inflection Class Systems

Inflectional classes are a property of the ideal inflecting-fusional language type. Thus strongly inflecting languages have the most complex vertical and horizontal stratification of hierarchical tree structures. Weakly inflecting languages which also approach the ideal isolating type or languages which also approach the agglutinating type have much shallower structures. Such properties follow from principles of Natural Morphology and from the distinction of the descendent hierarchy of macroclasses, classes, subclasses, subsubclasses etc. and homogeneous microclasses. The main languages of illustration are Latin, Lithuanian, Russian, German, French, Finnish, Hungarian and Turkis

On the Typology of Inflection Class Systems

Inflectional classes are a property of the ideal inflecting-fusional language type. Thus strongly inflecting languages have the most complex vertical and horizontal stratification of hierarchical tree structures. Weakly inflecting languages which also approach the ideal isolating type or languages which also approach the agglutinating type have much shallower structures. Such properties follow from principles of Natural Morphology and from the distinction of the descendent hierarchy of macroclasses, classes, subclasses, subsubclasses etc. and homogeneous microclasses. The main languages of illustration are Latin, Lithuanian, Russian, German, French, Finnish, Hungarian and Turkish

Segmentation of Huge Point Clouds using Region Growing

Zusammenfassung in englischer SpracheAbweichender Titel nach Übersetzung der Verfasserin/des VerfassersDie immer größer werdenden Datenmengen von 3D-Punktwolken, gewonnen durch Airborne Laserscanning, terrestrisches Laserscanning und Image Matching, ermöglichen eine Vielzahl unterschiedlichster Berechnungen und Datenanalysen. Die Anwendungsgebiete reichen von Monitoring-Aufgaben (z.B. Bauwerksüberwachung, Monitoring von Hangrutschungen, etc.) über archäologische Auswertungen und Vegetationskartierung bis hin zu 3D-Stadtmodellierung. Für Berechnungen auf Datensätzen mit vielen Millionen von Punkten wird dabei von den Algorithmen eine hohe Effizienz hinsichtlich der Laufzeit gefordert. Das Verfahren der Segmentierung liefert für Punktwolken eine Gruppierung von gleichartigen Punkten anhand eines Homogenitätskriteriums. Diese Gruppeninformation ermöglicht einen effizienten Zugriff auf Punkte mit gleichen Eigenschaften. Die Segmentierung ist damit einer der ersten Schritte in der Prozessierungskette vieler Anwendungen. Die vorliegende Arbeit stellt ein Konzept für eine Segmentierung von großen Punktwolken mit Seeded Region Growing vor. Da die Verarbeitungseinheit nicht beliebig große Datensätze in den Arbeitsspeicher einlesen kann, müssen diese in kleinere Einheiten aufgeteilt werden. Die Punktwolke wird in rechteckige Teilpunktwolken (Kacheln) ohne Überlappungsbereich unterteilt. Die mosaikartig zusammengesetzten Kacheln werden unabhängig voneinander segmentiert. Dadurch wird eine parallele Prozessierung der Kacheln - auf mehrere Threads verteilt - ermöglicht. Anschließend werden benachbarte gleichartige Segmente aus den Teilpunktwolken zusammengeführt. Wie diese Arbeit zeigt sind die Ergebnisse der Segmentierung nicht von der Größe der Teilpunktwolke sondern hauptsächlich vom Homogenitätskriterium abhängig. Die Punktwolke kann dadurch in Kacheln mit beliebiger Größe unterteilt werden, um die Laufzeit und den Speicherplatzbedarf der Segmentierung zu optimieren.The growing amount of 3D point cloud data obtained by airborne laser scanning, terrestrial laser scanning and image matching, allows a variety of different calculations and data analysis. Applications range from monitoring tasks (e.g. structural health monitoring, landslides monitoring, etc.), archaeological evaluations, vegetation mapping to 3D city modeling. For processing huge data sets with millions of points highly efficient algorithms are required. Segmentation of point cloud data provides a grouping of points based on a similarity criterion. This group information enables efficient access to points with the same properties. Thus segmentation is one of the first steps within the processing chain of many applications. This thesis presents a concept for segmentation of large point clouds with Seeded Region Growing. Since the processing unit can not read huge dataset into main memory, the data must be divided into smaller parts. The point cloud is divided into rectangular non-overlapping parts (tiles). The tiles are then processed independently within the segmentation. This allows parallel computation by distributing tiles to multiple processing threads. Afterwards adjacent segments from different tiles are merged. As it is shown the results of the segmentation do not depend on the tile size, but are mainly influenced by the similarity criterion. The point cloud can thus be divided into arbitrary tiles to optimize for processing speed and memory footprint.6