Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Image processing for plastic surgery planning

This thesis presents some image processing tools for plastic surgery planning. In particular, it presents a novel method that combines local and global context in a probabilistic relaxation framework to identify cephalometric landmarks used in Maxillofacial plastic surgery. It also uses a method that utilises global and local symmetry to identify abnormalities in CT frontal images of the human body. The proposed methodologies are evaluated with the help of several clinical data supplied by collaborating plastic surgeons

Ontology learning from folksonomies.

Chen, Wenhao.Thesis (M.Phil.)--Chinese University of Hong Kong, 2010.Includes bibliographical references (p. 63-70).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Ontologies and Folksonomies --- p.1Chapter 1.2 --- Motivation --- p.3Chapter 1.2.1 --- Semantics in Folksonomies --- p.3Chapter 1.2.2 --- Ontologies with basic level concepts --- p.5Chapter 1.2.3 --- Context and Context Effect --- p.6Chapter 1.3 --- Contributions --- p.6Chapter 1.4 --- Structure of the Thesis --- p.8Chapter 2 --- Background Study --- p.10Chapter 2.1 --- Semantic Web --- p.10Chapter 2.2 --- Ontology --- p.12Chapter 2.3 --- Folksonomy --- p.14Chapter 2.4 --- Cognitive Psychology --- p.17Chapter 2.4.1 --- Category (Concept) --- p.17Chapter 2.4.2 --- Basic Level Categories (Concepts) --- p.17Chapter 2.4.3 --- Context and Context Effect --- p.20Chapter 2.5 --- F1 Evaluation Metric --- p.21Chapter 2.6 --- State of the Art --- p.23Chapter 2.6.1 --- Ontology Learning --- p.23Chapter 2.6.2 --- Semantics in Folksonomy --- p.26Chapter 3 --- Ontology Learning from Folksonomies --- p.28Chapter 3.1 --- Generating Ontologies with Basic Level Concepts from Folksonomies --- p.29Chapter 3.1.1 --- Modeling Instances and Concepts in Folksonomies --- p.29Chapter 3.1.2 --- The Metric of Basic Level Categories (Concepts) --- p.30Chapter 3.1.3 --- Basic Level Concepts Detection Algorithm --- p.31Chapter 3.1.4 --- Ontology Generation Algorithm --- p.34Chapter 3.2 --- Evaluation --- p.35Chapter 3.2.1 --- Data Set and Experiment Setup --- p.35Chapter 3.2.2 --- Quantitative Analysis --- p.36Chapter 3.2.3 --- Qualitative Analysis --- p.39Chapter 4 --- Context Effect on Ontology Learning from Folksonomies --- p.43Chapter 4.1 --- Context-aware Basic Level Concepts Detection --- p.44Chapter 4.1.1 --- Modeling Context in Folksonomies --- p.44Chapter 4.1.2 --- Context Effect on Category Utility --- p.45Chapter 4.1.3 --- Context-aware Basic Level Concepts Detection Algorithm --- p.46Chapter 4.2 --- Evaluation --- p.47Chapter 4.2.1 --- Data Set and Experiment Setup --- p.47Chapter 4.2.2 --- Result Analysis --- p.49Chapter 5 --- Potential Applications --- p.54Chapter 5.1 --- Categorization of Web Resources --- p.54Chapter 5.2 --- Applications of Ontologies --- p.55Chapter 6 --- Conclusion and Future Work --- p.57Chapter 6.1 --- Conclusion --- p.57Chapter 6.2 --- Future Work --- p.59Bibliography --- p.6