@phdthesis{Sun2014,
  author    = {Sun, Kaipeng},
  title     = {Six Degrees of Freedom Object Pose Estimation with Fusion Data from a Time-of-flight Camera and a Color Camera},
  isbn      = {978-3-923959-97-6},
  doi       = {10.25972/OPUS-10508},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-105089},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2014},
  abstract  = {Object six Degrees of Freedom (6DOF) pose estimation is a fundamental problem in many practical robotic applications, where the target or an obstacle with a simple or complex shape can move fast in cluttered environments. In this thesis, a 6DOF pose estimation algorithm is developed based on the fused data from a time-of-flight camera and a color camera. The algorithm is divided into two stages, an annealed particle filter based coarse pose estimation stage and a gradient decent based accurate pose optimization stage. In the first stage, each particle is evaluated with sparse representation. In this stage, the large inter-frame motion of the target can be well handled. In the second stage, the range data based conventional Iterative Closest Point is extended by incorporating the target appearance information and used for calculating the accurate pose by refining the coarse estimate from the first stage. For dealing with significant illumination variations during the tracking, spherical harmonic illumination modeling is investigated and integrated into both stages. The robustness and accuracy of the proposed algorithm are demonstrated through experiments on various objects in both indoor and outdoor environments. Moreover, real-time performance can be achieved with graphics processing unit acceleration.},
  subject      = {Mustererkennung},
  language  = {en}
}
@phdthesis{Solanki2013,
  author    = {Solanki, Narendra},
  title     = {Novelty choice in Drosophila melanogaster},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-103219},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2013},
  abstract  = {This study explores novelty choice, a behavioral paradigm for the investigation of visual pattern recognition and learning of the fly Drosophila melanogaster in the flight simulator. Pattern recognition in novelty choice differs significantly from pattern recognition studied by heat conditioning, although both paradigms use the same test. Out of the four pattern parameters that the flies can learn in heat conditioning, novelty choice can be shown for height (horizontal bars differing in height), size and vertical compactness but not for oblique bars oriented at +/- 45°. Upright and inverted Ts [differing in their centers of gravity (CsOG) by 13°] that have been extensively used for heat conditioning experiments, do not elicit novelty choice. In contrast, horizontal bars differing in their CsOG by 13° do elicit novelty choice; so do the Ts after increasing their CsOG difference from 13° to 23°. This indicates that in the Ts the heights of the CsOG are not the only pattern parameters that matter for the novelty choice behavior. The novelty choice and heat conditioning paradigms are further differentiated using the gene rutabaga (rut) coding for a type 1 adenylyl cyclase. This protein had been shown to be involved in memory formation in the heat conditioning paradigm. Novelty choice is not affected by mutations in the rut gene. This is in line with the finding that dopamine, which in olfactory learning is known to regulate Rutabaga via the dopamine receptor Dumb in the mushroom bodies, is dispensable for novelty choice. It is concluded that in novelty choice the Rut cAMP pathway is not involved. Novelty choice requires short term working memory, as has been described in spatial orientation during locomotion. The protein S6KII that has been shown to be involved in visual orientation memory in walking flies is found here to be also required for novelty choice. As in heat conditioning the central complex plays a major role in novelty choice. The S6KII mutant phenotype for height can be rescued in some subsets of the ring neurons of the ellipsoid body. In addition the finding that the ellipsoid body mutants ebo678 and eboKS263 also show a mutant phenotype for height confirm the importance of ellipsoid body for height novelty choice. Interestingly some neurons in the F1 layer of the fan-shaped body are necessary for height novelty choice. Furthermore, different novelty choice phenotypes for different pattern parameters are found with and without mushroom bodies. Mushroom bodies are required in novelty choice for size but they are dispensable for height and vertical compactness. This special circuit requirement for the size parameter in novelty choice is found using various means of interference with mushroom body function during development or adulthood.},
  subject      = {Taufliege},
  language  = {en}
}
@misc{Hoehn2006,
  type      = {Master Thesis},
  author    = {H{\"o}hn, Winfried},
  title     = {Mustererkennung in Fr{\"u}hdrucken},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-30429},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2006},
  abstract  = {No abstract available},
  subject      = {Mustererkennung},
  language  = {de}
}
@phdthesis{Ernst1999,
  author    = {Ernst, Roman},
  title     = {Visuelle Mustererkennung und Parameterextraktion bei Drosophila melanogaster},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-1156},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {1999},
  abstract  = {In operanten Konditionierungsexperimenten im Flugsimulator werden vier Parameter gefunden die Drosophila melanogaster aus visuellen Mustern extrahieren kann: Musterfl{\"a}che, vertikale Position des Musterschwerpunkts, Verteiltheit und Musterausrichtung in horizontaler und vertikaler Richtung. Es ist nicht auszuschliessen, dass die Fliege weitere Musterparameter extrahieren kann. Spontane Musterpr{\"a}ferenzen und konditionierte Pr{\"a}ferenzen zeigen unterschiedliche Zusammenh{\"a}nge mit den Musterparametern. Aus r{\"a}umlich getrennten Musterelementen zusammengesetzte Muster werden von der Fliege wie ein Gesamtmuster behandelt. Retinaler Transfer wird auch bei der Pr{\"a}sentation von Mustern an zwei verschiedenen vertikalen Trainingspositionen nicht beobachtet. Muster werden generalisiert, wenn die Schwerpunkte korrespondierender Muster zwischen Training und Test ungef{\"a}hr an der gleichen Position liegen aber keine retinale {\"U}berlappung von Trainings- und Testmustern besteht. Retinotopie des Musterged{\"a}chtnisses liegt in diesem Fall nicht auf der Ebene der Bildpunkte, jedoch m{\"o}glicherweise auf der Ebene des Parameters 'Musterschwerpunkt' vor. Fliegen k{\"o}nnen nicht trainiert werden bestimmte Musterpaare zu diskriminieren die sich nur durch die vertikale Position ihres Musterschwerpunktes unterscheiden. Dennoch bevorzugen sie beim Lerntest mit anderen Mustern mit korrespondierenden Schwerpunktspositionen die zuvor nicht bestrafte Schwerpunktsposition. F{\"u}r die Modellierung der Extraktion von Musterschwerpunkt und Musterfl{\"a}che wird ein einfaches k{\"u}nstliches neuronales Filter pr{\"a}sentiert, dessen Architektur auf einem Berechnungsalgorithmus f{\"u}r den gemeinsamen Schwerpunkt mehrerer Teilelemente beruht.},
  subject      = {Taufliege},
  language  = {de}
}