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Ruping S.,Fraunhofer Institute for Intelligent Analysis and Information Systems
Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz | Year: 2015

Healthcare is one of the business fields with the highest Big Data potential. According to the prevailing definition, Big Data refers to the fact that data today is often too large and heterogeneous and changes too quickly to be stored, processed, and transformed into value by previous technologies. The technological trends drive Big Data: business processes are more and more executed electronically, consumers produce more and more data themselves − e.g. in social networks − and finally ever increasing digitalization. Currently, several new trends towards new data sources and innovative data analysis appear in medicine and healthcare. From the research perspective, omics-research is one clear Big Data topic. In practice, the electronic health records, free open data and the “quantified self” offer new perspectives for data analytics. Regarding analytics, significant advances have been made in the information extraction from text data, which unlocks a lot of data from clinical documentation for analytics purposes. At the same time, medicine and healthcare is lagging behind in the adoption of Big Data approaches. This can be traced to particular problems regarding data complexity and organizational, legal, and ethical challenges. The growing uptake of Big Data in general and first best-practice examples in medicine and healthcare in particular, indicate that innovative solutions will be coming. This paper gives an overview of the potentials of Big Data in medicine and healthcare. © 2015, Springer-Verlag Berlin Heidelberg.

Evangelidis G.D.,French Institute for Research in Computer Science and Automation | Bauckhage C.,Fraunhofer Institute for Intelligent Analysis and Information Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence | Year: 2013

This paper addresses the problem of video alignment. We present efficient approaches that allow for spatiotemporal alignment of two sequences. Unlike most related works, we consider independently moving cameras that capture a 3D scene at different times. The novelty of the proposed method lies in the adaptation and extension of an efficient information retrieval framework that casts the sequences as an image database and a set of query frames, respectively. The efficient retrieval builds on the recently proposed quad descriptor. In this context, we define the 3D Vote Space (VS) by aggregating votes through a multiquerying (multiscale) scheme and we present two solutions based on VS entries; a causal solution that permits online synchronization and a global solution through multiscale dynamic programming. In addition, we extend the recently introduced ECC image-alignment algorithm to the temporal dimension that allows for spatial registration and synchronization refinement with subframe accuracy. We investigate full search and quantization methods for short descriptors and we compare the proposed schemes with the state of the art. Experiments with real videos by moving or static cameras demonstrate the efficiency of the proposed method and verify its effectiveness with respect to spatiotemporal alignment accuracy. © 1979-2012 IEEE.

Frintrop S.,University of Bonn | Rome E.,Fraunhofer Institute for Intelligent Analysis and Information Systems | Christensen H.I.,Georgia Institute of Technology
ACM Transactions on Applied Perception | Year: 2010

Based on concepts of the human visual system, computational visual attention systems aim to detect regions of interest in images. Psychologists, neurobiologists, and computer scientists have investigated visual attention thoroughly during the last decades and profited considerably from each other. However, the interdisciplinarity of the topic holds not only benefits but also difficulties: Concepts of other fields are usually hard to access due to differences in vocabulary and lack of knowledge of the relevant literature. This article aims to bridge this gap and bring together concepts and ideas from the different research areas. It provides an extensive survey of the grounding psychological and biological research on visual attention as well as the current state of the art of computational systems. Furthermore, it presents a broad range of applications of computational attention systems in fields like computer vision, cognitive systems, and mobile robotics. We conclude with a discussion on the limitations and open questions in the field. © 2010 ACM.

Muhlenbein H.,Fraunhofer Institute for Intelligent Analysis and Information Systems
Adaptation, Learning, and Optimization | Year: 2012

Estimation of Distribution Algorithms (EDAs) have been proposed as an extension of genetic algorithms.We assume that the function to be optimized is additively decomposed (ADF). The interaction graph of the ADF function is used to create exact or approximate factorizations of the Boltzmann distribution. Convergence of the algorithmMN-GIBBS is proven.MN-GIBBS uses a Markov network easily derived from the ADF and Gibbs sampling. We discuss different variants of Gibbs sampling. We show that a good approximation of the true distribution is not necessary, it suffices to use a factorization where the global optima have a large enough probability. This explains the success of EDAs in practical applications using Bayesian networks. © Springer-Verlag Berlin Heidelberg 2012.

Baum D.,Fraunhofer Institute for Intelligent Analysis and Information Systems
Speech Communication | Year: 2012

We investigate how a speaker's preference for specific topics can be used for speaker identification. In domains like broadcast news or parliamentary speeches, speakers have a field of expertise they are associated with. We explore how topic information for a segment of speech, extracted from an automatic speech recognition transcript, can be employed to identify the speaker. Two methods for modelling topic preferences are compared: implicitly, based on speaker-characteristic keywords, and explicitly, by using automatically derived topic models to assign topics to the speech segments. In the keyword-based approach, the segments' tf-idf vectors are classified with Support Vector Machine speaker models. For the topic-model-based approach, a domain-specific topic model is used to represent each segment as a mixture of topics; the speakers' score is derived from the Kullback-Leibler divergence between the topic mixtures of their training data and of the segment. The methods were tested on political speeches given in German parliament by 235 politicians. We found that topic cues do carry speaker information, as the topic-model-based system yielded an equal error rate (EER) of 16.3%. The topic-based approach combined well with a spectral baseline system, improving the EER from 8.6% for the spectral to 6.2% for the fused system. © 2012 Elsevier B.V. All rights reserved.

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