This thesis systematically derives an innovative approach, called FOMFE, for fingerprint ridge orientation modeling based on 2D Fourier expansions, and explores possible applications of FOMFE to various aspects of a fingerprint identification system. Compared with existing proposals, FOMFE does not require prior knowledge of the landmark singular points (SP) at any stage of the modeling process. This salient feature makes it immune from false SP detections and robust in terms of modeling ridge topology patterns from different typological classes. The thesis provides the motivation of this work, thoroughly reviews the relevant literature, and carefully lays out the theoretical basis of the proposed modeling approach. This is followed by a detailed exposition of how FOMFE can benefit fingerprint feature analysis including ridge orientation estimation, singularity analysis, global feature characterization for a wide variety of fingerprint categories, and partial fin gerprint identification. The proposed methods are based on the insightful use of theory from areas such as Fourier analysis of nonlinear dynamic systems, analytical operators from differential calculus in vector fields, and fluid dynamics. The thesis has conducted extensive experimental evaluation of the proposed methods on benchmark data sets, and drawn conclusions about strengths and limitations of these new techniques in comparison with state-of-the-art approaches. FOMFE and the resulting model-based methods can significantly improve the computational efficiency and reliability of fingerprint identification systems, which is important for indexing and matching fingerprints at a large scale.

Digital image archiving urgently requires advanced techniques for more efficient storage and retrieval methods because of the increasing amount of digital. Although JPEG supply systems to compress image data efficiently, the problems of how to organize the image database structure for efficient indexing and retrieval, how to index and retrieve image data from DCT compressed domain and how to interpret image data semantically are major obstacles for further development of digital image database system. In content-based image, image analysis is the primary step to extract useful information from image databases. The difficulty in content-based image retrieval is how to summarize the low-level features into high-level or semantic descriptors to facilitate the retrieval procedure. Such a shift toward a semantic visual data learning or detection of semantic objects generates an urgent need to link the low level features with semantic understanding of the observed visual information. To solve such a 'semantic gap' problem, an efficient way is to develop a number of classifiers to identify the presence of semantic image components that can be connected to semantic descriptors. Among various semantic objects, the human face is a very important example, which is usually also the most significant element in many images and photos. The presence of faces can usually be correlated to specific scenes with semantic inference according to a given ontology. Therefore, face detection can be an efficient tool to annotate images for semantic descriptors. In this thesis, a paradigm to process, analyze and interpret digital images is proposed. In order to speed up access to desired images, after accessing image data, image features are presented for analysis. This analysis gives not only a structure for content-based image retrieval but also the basic units ii for high-level semantic image interpretation. Finally, images are interpreted and classified into some semantic categories by semantic object detection categorization algorithm.

Es wird eine Konkordanz zwischen den beiden Klassifikationssystemen RVK und DDC für das Fach Psychologie vorgestellt. Eine im Vorfeld durchgeführte Befragung ergab, dass der Bedarf bei den Bibliotheksverbünden vorhanden ist. Bislang wurde aber der hohe individuelle Aufwand gescheut. Hintergrund für das Erarbeiten der Konkordanz war die Übernahme des Fachgebietes durch die Referentin. Für Bibliothekskataloge weltweit spielen Konkordanzen bei der Indexierung und beim Retrieval eine Rolle. Gegenüberstellungen zwischen verschiedenen Systemen sind essenzielle Vorraussetzungen für das Semantic Web. In der Kombination verschiedener Erschließungsverfahren wie z.B. social tagging Klassifizierung und normierter Sacherschließung können Konkordanzen dazu beitragen, in intelligenten, selbstlernenden Datenbanken zu stetig wachsenden, mit zusätzlichen Informationen angereicherten und verlinkten Datenpräsentation zu kommen. Für eine Weiterentwicklung bietet sich die Verknüpfung von Begriffen und Informationen an, die unter dem Begriff „Ontology learning“ beschrieben werden. Im Vorfeld der Konkordanzerstellung musste festgelegt werden, welche Klassifikation die Basis bildet, auf die das andere System abgebildet wird. Ebenso war zu prüfen, ob eine strukturierte Gegenüberstellung machbar ist. Kann die unterschiedliche Hierarchietiefe zwischen der RVK und sinnvoll abgebildet werden? Es ist denkbar, die Konkordanz in kollaborativer Zusammenarbeit mit anderen Bibliotheken weiterzuentwickeln sowie Crosswalks zu Thesauri und Metadaten zu schaffen. In der anschließenden Diskussion gibt es die Möglichkeit, den erreichten Stand nach inhaltlichen und formalen Aspekten zu bewerten und künftige Anwendungsmöglichkeiten zu erörtern. Dazu zählen u.a. Fragen der automatischen Konkordanzerstellung mittels statistischer Verfahren als auch die technischen Lösungsansätze des Inputs von Konkordanzen in Ontologien. In der Verzahnung von automatischen statistischen Verfahren und manuell erstellten Korrelationen sind weitere Synergien denkbar, die diskutiert werden können.

Digital image archiving urgently requires advanced techniques for more efficient storage and retrieval methods because of the increasing amount of digital. Although JPEG supply systems to compress image data efficiently, the problems of how to organize the image database structure for efficient indexing and retrieval, how to index and retrieve image data from DCT compressed domain and how to interpret image data semantically are major obstacles for further development of digital image database system. In content-based image, image analysis is the primary step to extract useful information from image databases. The difficulty in content-based image retrieval is how to summarize the low-level features into high-level or semantic descriptors to facilitate the retrieval procedure. Such a shift toward a semantic visual data learning or detection of semantic objects generates an urgent need to link the low level features with semantic understanding of the observed visual information. To solve such a -semantic gap¿ problem, an efficient way is to develop a number of classifiers to identify the presence of semantic image components that can be connected to semantic descriptors. Among various semantic objects, the human face is a very important example, which is usually also the most significant element in many images and photos. The presence of faces can usually be correlated to specific scenes with semantic inference according to a given ontology. Therefore, face detection can be an efficient tool to annotate images for semantic descriptors. In this thesis, a paradigm to process, analyze and interpret digital images is proposed. In order to speed up access to desired images, after accessing image data, image features are presented for analysis. This analysis gives not only a structure for content-based image retrieval but also the basic units ii for high-level semantic image interpretation. Finally, images are interpreted and classified into some semantic categories by semantic object detection categorization algorithm.

This article provides an overview of the first BioASQ challenge, a competition on large-scale biomedical semantic indexing and question answering (QA), which took place between March and September 2013. BioASQ assesses the ability of systems to semantically index very large numbers of biomedical scientific articles, and to return concise and user-understandable answers to given natural language questions by combining information from biomedical articles and ontologies.