@inproceedings{SchirmerKleinerOsburg, author = {Schirmer, Ulrike and Kleiner, Florian and Osburg, Andrea}, title = {Objektive Oberfl{\"a}chenbewertung von (P)SCC-Sichtbeton mittels automatisierter Analyse von Bilddaten}, series = {Tagung Bauchemie der GDCH-Fachgruppe Bauchemie, 30. September - 2. Oktober 2019 in Aachen}, booktitle = {Tagung Bauchemie der GDCH-Fachgruppe Bauchemie, 30. September - 2. Oktober 2019 in Aachen}, publisher = {Gesellschaft Deutscher Chemiker}, isbn = {978-3-947197-13-2}, doi = {10.25643/bauhaus-universitaet.4510}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20211004-45104}, pages = {8}, abstract = {Sichtbeton ist aufgrund seiner Vielf{\"a}ltigkeit in der Formgebung eines der am meisten verbreiteten Gestaltungsmittel der modernen Architektur und optimal f{\"u}r neue Bauweisen sowie steigende Anforderungen an das Erscheinungsbild {\"o}ffentlicher Bauwerke geeignet. Die Herstellung qualitativ hochwertiger Sichtbetonoberfl{\"a}chen h{\"a}ngt im hohen Maße von den Wechselwirkungen zwischen Beton und Trennmittel, zwischen Trennmittel und Schalmaterial, sowie von der Applikationsart und -menge des Trennmittels ab. In Laborversuchen wurden diese Einfl{\"u}sse auf die Sichtbetonoberfl{\"a}chen eines polymermodifizierten selbstverdichtenden Betons (PSCC) im Vergleich zu einem herk{\"o}mmlichen selbstverdichtenden Beton (SCC) untersucht. Im Rahmen dieser Arbeiten wurde eine Methode zur Beurteilung der Sichtbetonqualit{\"a}t entwickelt, mit welcher Ausschlusskriterien, wie maximale Porosit{\"a}t und Gleichm{\"a}ßigkeit, objektiv und automatisiert bestimmt werden k{\"o}nnen. Ver{\"a}nderungen dieser Werte durch Witterungseinfl{\"u}sse ließen zudem erste R{\"u}ckschl{\"u}sse auf die Dauerhaftigkeit der Sichtbetonoberfl{\"a}chen zu.}, subject = {Sichtbeton}, language = {de} } @article{SaqlaiGhaniKhanetal., author = {Saqlai, Syed Muhammad and Ghani, Anwar and Khan, Imran and Ahmed Khan Ghayyur, Shahbaz and Shamshirband, Shahaboddin and Nabipour, Narjes and Shokri, Manouchehr}, title = {Image Analysis Using Human Body Geometry and Size Proportion Science for Action Classification}, series = {Applied Sciences}, volume = {2020}, journal = {Applied Sciences}, number = {volume 10, issue 16, article 5453}, publisher = {MDPI}, address = {Basel}, doi = {10.3390/app10165453}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200904-42322}, pages = {24}, abstract = {Gestures are one of the basic modes of human communication and are usually used to represent different actions. Automatic recognition of these actions forms the basis for solving more complex problems like human behavior analysis, video surveillance, event detection, and sign language recognition, etc. Action recognition from images is a challenging task as the key information like temporal data, object trajectory, and optical flow are not available in still images. While measuring the size of different regions of the human body i.e., step size, arms span, length of the arm, forearm, and hand, etc., provides valuable clues for identification of the human actions. In this article, a framework for classification of the human actions is presented where humans are detected and localized through faster region-convolutional neural networks followed by morphological image processing techniques. Furthermore, geometric features from human blob are extracted and incorporated into the classification rules for the six human actions i.e., standing, walking, single-hand side wave, single-hand top wave, both hands side wave, and both hands top wave. The performance of the proposed technique has been evaluated using precision, recall, omission error, and commission error. The proposed technique has been comparatively analyzed in terms of overall accuracy with existing approaches showing that it performs well in contrast to its counterparts.}, subject = {Bildanalyse}, language = {en} } @article{PatzeltErfurtLudwig, author = {Patzelt, Max and Erfurt, Doreen and Ludwig, Horst-Michael}, title = {Quantification of cracks in concrete thin sections considering current methods of image analysis}, series = {Journal of Microscopy}, volume = {2022}, journal = {Journal of Microscopy}, number = {Volume 286, Issue 2}, doi = {10.1111/jmi.13091}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20220811-46754}, pages = {154 -- 159}, abstract = {Image analysis is used in this work to quantify cracks in concrete thin sections via modern image processing. Thin sections were impregnated with a yellow epoxy resin, to increase the contrast between voids and other phases of the concrete. By the means of different steps of pre-processing, machine learning and python scripts, cracks can be quantified in an area of up to 40 cm2. As a result, the crack area, lengths and widths were estimated automatically within a single workflow. Crack patterns caused by freeze-thaw damages were investigated. To compare the inner degradation of the investigated thin sections, the crack density was used. Cracks in the thin sections were measured manually in two different ways for validation of the automatic determined results. On the one hand, the presented work shows that the width of cracks can be determined pixelwise, thus providing the plot of a width distribution. On the other hand, the automatically measured crack length differs in comparison to the manually measured ones.}, subject = {Beton}, language = {en} } @article{FischerSteinhage1997, author = {Fischer, A. and Steinhage, V.}, title = {Ein modellbasiertes Konzept zur st{\"a}dteplanerischen Kartierung durch digitale Bildanalyse}, doi = {10.25643/bauhaus-universitaet.487}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20111215-4870}, year = {1997}, abstract = {There is an increasing need for 3D building extraction from aerial images for various applications such astown planning, environmental- and property-related studies. Aerial images usually reveal on one hand a certain amount of information not relevant for the given task of building extraction like vegetation, cars etc. On the other hand there is a loss of relevant information due to occlusions, low contrasts or disadvantageous perspectives. Therefore a promising concept for automated building reconstruction must incorporate a suffciantly complete model of the objects of interest. We propose a model-based approach to 3D building extraction from aerial images which reveals a tight coupling between a generic 3D object model and an explicit 2D image model. The generic object model employes domain specific volumetric primitives (i. e. building part models) and combination schemes. To cover the gap between 3D object models and 2D image data the image model is employed to predict the projective building appearences in aerial images. We present a strategy for a model-based building extraction based on the recognition-by-components principle and show first experimental results derived from international test sets}, subject = {Stadtplanung}, language = {de} }