186,386 research outputs found
Conférence : Imagerie virtuelle et archéologie par P. Grussenmeyer et Ch. Kremer (Mercredi 16 avril, 18h, MSH)
No full textLe Pôle Archéologique Universitaire (HISCANT-MA - EA 1132) organise une conférence mercredi 16 avril 2014 à 18h00 - Salle des Actes (G 04) IMAGERIE VIRTUELLE ET ARCHÉOLOGIE À PARTIR DES EXEMPLES DU CHÂTEAU D'HUGSTEIN (68) ET DE L'EGLISE DE DUGNY (55) Animée par Pierre GRUSSENMEYER Professeur à l'INSA de Strasbourg (département Génie Civil-Topographie) et responsable du laboratoire de Photogrammétrie et Charles KRAEMER Ingénieur de recherche à l'Université de Lorraine (Pôle Archéologique Un..
Conférence : Imagerie virtuelle et archéologie par P. Grussenmeyer et Ch. Kremer (Mercredi 16 avril, 18h, MSH)
No full textLe Pôle Archéologique Universitaire (HISCANT-MA - EA 1132) organise une conférence mercredi 16 avril 2014 à 18h00 - Salle des Actes (G 04) IMAGERIE VIRTUELLE ET ARCHÉOLOGIE À PARTIR DES EXEMPLES DU CHÂTEAU D'HUGSTEIN (68) ET DE L'EGLISE DE DUGNY (55) Animée par Pierre GRUSSENMEYER Professeur à l'INSA de Strasbourg (département Génie Civil-Topographie) et responsable du laboratoire de Photogrammétrie et Charles KRAEMER Ingénieur de recherche à l'Université de Lorraine (Pôle Archéologique Un..
Synthetic data generation and testing for the semantic segmentation of heritage buildings
No full textTowards Semantic Photogrammetry: Generating Semantically Rich Point Clouds from Architectural Close-Range Photogrammetry
No full textDevelopments in the field of artificial intelligence have made great strides in the field of automatic semantic segmentation, both in the 2D (image) and 3D spaces. Within the context of 3D recording technology it has also seen application in several areas, most notably in creating semantically rich point clouds which is usually performed manually. In this paper, we propose the introduction of deep learning-based semantic image segmentation into the photogrammetric 3D reconstruction and classification workflow. The main objective is to be able to introduce semantic classification at the beginning of the classical photogrammetric workflow in order to automatically create classified dense point clouds by the end of the said workflow. In this regard, automatic image masking depending on pre-determined classes were performed using a previously trained neural network. The image masks were then employed during dense image matching in order to constraint the process into the respective classes, thus automatically creating semantically classified point clouds as the final output. Results show that the developed method is promising, with automation of the whole process feasible from input (images) to output (labelled point clouds). Quantitative assessment gave good results for specific classes e.g., building facades and windows, with IoU scores of 0.79 and 0.77 respectively
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Appropriate Similarity Measures for Author Cocitation Analysis
No full textWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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