3,574 research outputs found
The Filament Sensor for Near Real-Time Detection of Cytoskeletal Fiber Structures
A reliable extraction of filament data from microscopic images is of high
interest in the analysis of acto-myosin structures as early morphological
markers in mechanically guided differentiation of human mesenchymal stem cells
and the understanding of the underlying fiber arrangement processes. In this
paper, we propose the filament sensor (FS), a fast and robust processing
sequence which detects and records location, orientation, length and width for
each single filament of an image, and thus allows for the above described
analysis. The extraction of these features has previously not been possible
with existing methods. We evaluate the performance of the proposed FS in terms
of accuracy and speed in comparison to three existing methods with respect to
their limited output. Further, we provide a benchmark dataset of real cell
images along with filaments manually marked by a human expert as well as
simulated benchmark images. The FS clearly outperforms existing methods in
terms of computational runtime and filament extraction accuracy. The
implementation of the FS and the benchmark database are available as open
source.Comment: 32 pages, 21 figure
Human Crowdsourcing Data for Indoor Location Applied to Ambient Assisted Living Scenarios
In the last decades, the rise of life expectancy has accelerated the demand for new technological
solutions to provide a longer life with improved quality. One of the major areas
of the Ambient Assisted Living aims to monitor the elderly location indoors. For this purpose,
indoor positioning systems are valuable tools and can be classified depending on the
need of a supporting infrastructure. Infrastructure-based systems require the investment
on expensive equipment and existing infrastructure-free systems, although rely on the
pervasively available characteristics of the buildings, present some limitations regarding
the extensive process of acquiring and maintaining fingerprints, the maps that store the
environmental characteristics to be used in the localisation phase. These problems hinder
indoor positioning systems to be deployed in most scenarios.
To overcome these limitations, an algorithm for the automatic construction of indoor
floor plans and environmental fingerprints is proposed. With the use of crowdsourcing
techniques, where the extensiveness of a task is reduced with the help of a large undefined
group of users, the algorithm relies on the combination ofmultiple sources of information,
collected in a non-annotated way by common smartphones. The crowdsourced data is
composed by inertial sensors, responsible for estimating the users’ trajectories, Wi-Fi
radio and magnetic field signals. Wi-Fi radio data is used to cluster the trajectories into
smaller groups, each corresponding to specific areas of the building. Distance metrics
applied to magnetic field signals are used to identify geomagnetic similarities between
different users’ trajectories. The building’s floor plan is then automatically created, which
results in fingerprints labelled with physical locations.
Experimental results show that the proposed algorithm achieved comparable floor
plan and fingerprints to those acquired manually, allowing the conclusion that is possible
to automate the setup process of infrastructure-free systems. With these results, this
solution can be applied in any fingerprinting-based indoor positioning system
Fingerabdruckswachstumvorhersage, Bildvorverarbeitung und Multi-level Judgment Aggregation
Im ersten Teil dieser Arbeit wird Fingerwachstum
untersucht und eine Methode zur Vorhersage von Wachstum
wird vorgestellt. Die Effektivität dieser Methode wird
mittels mehrerer Tests validiert. Vorverarbeitung von
Fingerabdrucksbildern wird im zweiten Teil behandelt
und neue Methoden zur Schätzung des Orientierungsfelds
und der Ridge-Frequenz sowie zur Bildverbesserung
werden vorgestellt: Die Line Sensor Methode zur
Orientierungsfeldschätzung, gebogene Regionen zur
Ridge-Frequenz-Schätzung und gebogene Gabor Filter zur
Bildverbesserung. Multi-level Jugdment Aggregation wird
eingefĂĽhrt als Design Prinzip zur Kombination mehrerer
Methoden auf mehreren Verarbeitungsstufen. SchlieĂźlich
wird Score Neubewertung vorgestellt, um Informationen
aus der Vorverarbeitung mit in die Score Bildung
einzubeziehen. Anhand eines Anwendungsbeispiels wird
die Wirksamkeit dieses Ansatzes auf den verfĂĽgbaren
FVC-Datenbanken gezeigt.Finger growth is studied in the first part of the
thesis and a method for growth prediction is presented.
The effectiveness of the method is validated in several
tests. Fingerprint image preprocessing is discussed in
the second part and novel methods for orientation field
estimation, ridge frequency estimation and image
enhancement are proposed: the line sensor method for
orientation estimation provides more robustness to
noise than state of the art methods. Curved regions are
proposed for improving the ridge frequency estimation
and curved Gabor filters for image enhancement. The
notion of multi-level judgment aggregation is
introduced as a design principle for combining
different methods at all levels of fingerprint image
processing. Lastly, score revaluation is proposed for
incorporating information obtained during preprocessing
into the score, and thus amending the quality of the
similarity measure at the final stage. A sample
application combines all proposed methods of the second
part and demonstrates the validity of the approach by
achieving massive verification performance improvements
in comparison to state of the art software on all
available databases of the fingerprint verification
competitions (FVC)
Multimodal Biometrics Enhancement Recognition System based on Fusion of Fingerprint and PalmPrint: A Review
This article is an overview of a current multimodal biometrics research based on fingerprint and palm-print. It explains the pervious study for each modal separately and its fusion technique with another biometric modal. The basic biometric system consists of four stages: firstly, the sensor which is used for enrolmen
Automated extraction of chemical structure information from digital raster images
Background: To search for chemical structures in research articles, diagrams or text representing molecules need to be translated to a standard chemical file format compatible with cheminformatic search engines. Nevertheless, chemical information contained in research articles is often referenced as analog diagrams of chemical structures embedded in digital raster images. To automate analog-to-digital conversion of chemical structure diagrams in scientific research articles, several software systems have been developed. But their algorithmic performance and utility in cheminformatic research have not been investigated. Results: This paper aims to provide critical reviews for these systems and also report our recent development of ChemReader -- a fully automated tool for extracting chemical structure diagrams in research articles and converting them into standard, searchable chemical file formats. Basic algorithms for recognizing lines and letters representing bonds and atoms in chemical structure diagrams can be independently run in sequence from a graphical user interface-and the algorithm parameters can be readily changed-to facilitate additional development specifically tailored to a chemical database annotation scheme. Compared with existing software programs such as OSRA, Kekule, and CLiDE, our results indicate that ChemReader outperforms other software systems on several sets of sample images from diverse sources in terms of the rate of correct outputs and the accuracy on extracting molecular substructure patterns. Conclusion: The availability of ChemReader as a cheminformatic tool for extracting chemical structure information from digital raster images allows research and development groups to enrich their chemical structure databases by annotating the entries with published research articles. Based on its stable performance and high accuracy, ChemReader may be sufficiently accurate for annotating the chemical database with links to scientific research articles.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90875/1/Saitou8.pd
Intelligent indexing of crime scene photographs
The Scene of Crime Information System's automatic image-indexing prototype goes beyond extracting keywords and syntactic relations from captions. The semantic information it gathers gives investigators an intuitive, accurate way to search a database of cases for specific photographic evidence. Intelligent, automatic indexing and retrieval of crime scene photographs is one of the main functions of SOCIS, our research prototype developed within the Scene of Crime Information System project. The prototype, now in its final development and evaluation phase, applies advanced natural language processing techniques to text-based image indexing and retrieval to tackle crime investigation needs effectively and efficiently
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