Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

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  • Publication
    Self-organizing maps and Gestalt organization as components of an advanced system for remotely sensed data: An example with thermal hyper-spectra
    ( 2016)
    Michaelsen, Eckart
    The thermal hyper-spectral data provided for research by IEEE-GRS/Telops in 2014 give an interesting example for combining such spectral information with perceptual grouping according to Gestalt laws in the geographic plane. Self-organizing maps are used for unsupervised learning. By watershed segmentation and subsequent merging on the map certain classes are found automatically. Back-projection of these regions to the geographic plane reveals considerable coincidence with classes of human interest such as vegetation, building roofs, and roads, respectively. The partial ground-truth provided with the data by IEEE-GRS/Telops allows the estimation of quantitative recognition accuracies. Human observers assign such meaning to the back-projected segments relying mainly on their perceptual grouping capabilities - roads appear as elongated stripes organized in a net, buildings come as blobs in organized patterns of repetitive rows and mirror-symmetry, and subsequently the rest is inferred as probably being vegetated. The automatic Gestalt grouping presented in this work follows the rules of Gestalt algebra. Gestalt hierarchies of depth three can be instantiated on the building class in accordance with human perception. Interesting feed-back possibilities are proposed from the perceptual grouping to the interpretation of the segments on the self-organizing map and further on to the assignment of meaning to the spectra. Again the ground-truth is used to estimate the gain quantitatively.
  • Publication
    Stochastic reasoning for structural pattern recognition: An example from image-based UAV navigation
    ( 2014)
    Michaelsen, Eckart
    ;
    Meidow, Jochen orcid-logo
    This paper reports on the statistical embedding of a structural pattern recognition system into the autonomous navigation of an unmanned aerial vehicle (UAV). A rule-based system is used for the recognition of visual landmarks such as bridges in aerial views. In principle, rule-based systems can be designed and coded with no training data at hand, but a sound interpretation and utilization of the achieved results needs statistical inference and representative data sets of sufficient coverages. Flying a UAV with an experimental system is expensive, risky, and legally questionable. Therefore, we chose a virtual globe as a camera simulator providing arbitrary amounts of training and test data. The expected positions of landmarks in the aerial views are modeled by mixture models representing inliers, outliers, and intermediate forms which stem from similar structures appearing frequently in the vicinity of landmarks. The parameters of the corresponding likelihood functions are estimated by the Expectation-Maximization method. Using these estimates, we carry out tests and compare the results for heuristic, pessimistic, optimistic, and Bayesian decision rationales. This performance evaluation reveals the superiority of the Bayesian approach.