<> "The repository administrator has not yet configured an RDF license."^^ . <> . . "Interactive Segmentation, Uncertainty and Learning"^^ . "Interactive segmentation is an important paradigm in image processing. To minimize the number of user interactions (“seeds”) required until the result is correct, the computer should actively query the human for input at the most critical locations, in analogy to active learning. These locations are found by means of suitable uncertainty measures. I propose various such measures for the watershed cut algorithm along with a theoretical analysis of some of their properties in Chapter 2.\r\nFurthermore, real-world images often admit many different segmentations that have nearly the same quality according to the underlying energy function. The diversity of these solutions may be a powerful uncertainty indicator.\r\nIn Chapter 3 the crucial prerequisite in the context of\r\nseeded segmentation with minimum spanning trees (i.e. edge-weighted watersheds) is provided. Specifically, it is shown how to efficiently enumerate the k smallest spanning trees that result in different segmentations.\r\nFurthermore, I propose a scheme that allows to partition an image into a previously unknown number of segments, using only minimal supervision in terms of a few must-link and cannot-link annotations. The algorithm presented in Chapter 4 makes no use of regional data terms, learning instead what constitutes a likely boundary between segments. Since boundaries are only implicitly specified through cannot-link constraints, this is a hard and nonconvex latent\r\nvariable problem. This problem is adressed in a greedy fashion using a randomized decision tree on features associated with interpixel edges. I propose to use a structured purity criterion during tree construction and also show how a backtracking strategy can be used to prevent the greedy search from ending up in poor local optima.\r\nThe problem of learning a boundary classifier from sparse user annotations is also considered in Chapter 5. Here the problem is mapped to a multiple instance learning task where positive bags consist of paths on a graph that cross a segmentation boundary and negative bags consist\r\nof paths inside a user scribble.\r\nMultiple instance learning is also the topic of Chapter 6. Here I propose a multiple instance learning algorithm based on randomized decision trees. Experiments on the typical benchmark data sets show that this model’s prediction performance is clearly better than earlier tree based\r\nmethods, and is only slightly below that of more expensive methods.\r\nFinally, a flow graph based computation library is discussed in Chapter 7. The presented\r\nlibrary is used as the backend in a interactive learning and segmentation toolkit and supports a\r\nrich set of notification mechanisms for the interaction with a graphical user interface.\r\n"^^ . "2014" . . . . . . . "Christoph-Nikolas"^^ . "Straehle"^^ . "Christoph-Nikolas Straehle"^^ . . . . . . "Interactive Segmentation, Uncertainty and Learning (PDF)"^^ . . . "t.pdf"^^ . . . "Interactive Segmentation, Uncertainty and Learning (Other)"^^ . . . . . . "lightbox.jpg"^^ . . . "Interactive Segmentation, Uncertainty and Learning (Other)"^^ . . . . . . "preview.jpg"^^ . . . "Interactive Segmentation, Uncertainty and Learning (Other)"^^ . . . . . . "medium.jpg"^^ . . . "Interactive Segmentation, Uncertainty and Learning (Other)"^^ . . . . . . "small.jpg"^^ . . . "Interactive Segmentation, Uncertainty and Learning (Other)"^^ . . . . . . "indexcodes.txt"^^ . . "HTML Summary of #17423 \n\nInteractive Segmentation, Uncertainty and Learning\n\n" . "text/html" . . . "004 Informatik"@de . "004 Data processing Computer science"@en . .