Institut für Raumbezogene Informations- und Messtechnik
Hochschule Mainz - University of Applied Sciences

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Ponciano, Jean-Jacques

Projekte

2016

The KnowDIP project aims at the conception of a framework for an automatic object detection in unstructured and heterogeneous data. This framework uses a representation of human knowledge in order to improve the flexibility, the accuracy, and the…

Nachrichten

2019

Die Verteidigung seiner Dissertation Mitte November an der Universität Saint-Etienne markierte für Jean-Jacques Ponciano den erfolgreichen Abschluss seiner D…

Mit zwei Vorträgen war das i3mainz auf den 18. Oldenburger 3D-Tagen vom 6. bis 7. Februar 2018 vertreten.

Jean-Jacques Ponciano stellte einige Resultat…

2017

Auf dem 16. Mainzer Wissenschaftsmarkt am 9. und 10. September 2017, zu dem die Mainzer Wissenschaftsallianz unter dem Motto Mensc…

Bei der Konferenz WEBIST 2017, der 13th International Conference on Web Information Systems and Technologies, die vom 25.-27. Apri…

Publikationen

2019

Automatic Detection of Objects in 3D Point Clouds Based on Exclusively Semantic Guided Processes

2019

Ponciano, Jean-Jacques, Trémeau, Alain, Boochs, Frank

PDF

ISPRS International Journal of Geo-Information
<p>In the domain of computer vision, object recognition aims at detecting and classifying objects in data sets. Model-driven approaches are typically constrained through their focus on either a specific type of data, a context (indoor, outdoor) or a set of objects. Machine learning-based approaches are more flexible but also constrained as they need annotated data sets to train the learning process. That leads to problems when this data is not available through the specialty of the application field, like archaeology, for example. In order to overcome such constraints, we present a fully semantic-guided approach. The role of semantics is to express all relevant knowledge of the representation of the objects inside the data sets and of the algorithms which address this representation. In addition, the approach contains a learning stage since it adapts the processing according to the diversity of the objects and data characteristics. The semantic is expressed via an ontological model and uses standard web technology like SPARQL queries, providing great flexibility. The ontological model describes the object, the data and the algorithms. It allows the selection and execution of algorithms adapted to the data and objects dynamically. Similarly, processing results are dynamically classified and allow for enriching the ontological model using SPARQL construct queries. The semantic formulated through SPARQL also acts as a bridge between the knowledge contained within the ontological model and the processing branch, which executes algorithms. It provides the capability to adapt the sequence of algorithms to an individual state of the processing chain and makes the solution robust and flexible. The comparison of this approach with others on the same use case shows the efficiency and improvement this approach brings.</p>

Connected Semantic Concepts as a Base for Optimal Recording and Computer-Based Modelling of Cultural Heritage Objects

2019

Jean-Jacques Ponciano, Ashish Karmacharya, Stefanie Wefers, Philipp Atorf, Frank BOOCHS

Structural Analysis of Historical Constructions
<p>3D and spectral digital recording of cultural heritage monuments is a common activity for their documentation, preservation, conservation management, and reconstruction. Recent developments in 3D and spectral technologies have provided enough flexibility in selecting one technology over another, depending on the data content and quality demands of the data application. Each technology has its own pros/cons, suited perfectly to some situations and not to others. They are mostly unknown to humanities experts, besides having a limited understanding of the data requirements demanded by the research question. These are often left to technical experts who again have a limited understanding of cultural heritage requirements. A common point of view has to be achieved through interdisciplinary discussions. Such agreements need to be documented for their future references and re-uses. We present a method based on semantic concepts that not only documents the semantic essence of such discussions, but also uses it to infer a guidance mechanism that recommends technologies/technical process to generate the required data based on individual needs. Experts&#39; knowledge is represented explicitly through a knowledge representation that allows machines to manage and infer recommendations. First, descriptive semantics guide end users to select the optimal technology/technologies for recording data. Second, structured knowledge controls the processing chain extracting and classifying objects contained in the acquired data. Circumstantial situations during object recording and the behaviour of the technologies in that situation are taken into account. We will explain the approach as such and give results from tests at a CH object.</p>

Identification and classification of objects in 3D point clouds based on a semantic concept

2019

Ponciano, Jean-Jacques, Boochs, Frank, Trémeau, Alain

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2017

Automatic Integration of Spatial Data into the Semantic Web

2017

Claire Prudhomme, Timo Homburg, Jean-Jacques Ponciano, F Boochs, Roxin, Ana, Cruz, Christophe

WebIST 2017
<p><span class="foldable-text" data-reactid="122" id="yui_3_14_1_1_1505396336545_1222">For several years, many researchers tried to semantically integrate geospatial datasets into the semantic web. Although, there are many general means of integrating interconnected relational datasets (e.g. R2RML), importing schema-less relational geospatial data remains a major challenge in the semantic web community. In our project SemGIS we face significant importation challenges of schema-less geodatasets, in various data formats without relations to the semantic web. We therefore developed an automatic process of semantification for aforementioned data using among others the geometry of spatial objects. We combine Natural Language processing with geographic and semantic tools in order to extract semantic information of spatial data into a local ontology linked to existing semantic web resources. For our experiments, we used LinkedGeoData and Geonames ontologies to link semantic spatial information and compared links with DBpedia and Wikidata for other types of information. The aim of our experiments presented in this paper, is to examine the feasibility and limits of an automated integration of spatial data into a semantic knowledge base and to assess its correctness according to different open datasets. Other ways to link these open datasets have been applied and we used the different results for evaluating our automatic approach.</span></p>

2016

Detection and classification of railway switches in point clouds of the German railway system

2016

Jean-Jacques Ponciano, Claire Prudhomme, F Boochs

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Knowledge based Object Detection in Images and Point clouds

2016

Jean-Jacques Ponciano, F Boochs, A. Trémeau

Molas 2016