Abstract
In this work we present a novel method for detecting multiple objects of interest in one image, when the only available information about these objects are their shape and color. To solve this task we use a global optimal variational approach based on total variation. The presented energy functional can be minimized locally due its convex formulation. To improve the runtime of our algorithm we show how this approach can be scheduled in parallel.Our algorithm works fully automatically and does not need any user interaction. In experiments we show the capabilities in non-artificial images, e.g. aerial or bureau images.
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Schikora, M. (2009). Global Optimal Multiple Object Detection Using the Fusion of Shape and Color Information. In: Cremers, D., Boykov, Y., Blake, A., Schmidt, F.R. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2009. Lecture Notes in Computer Science, vol 5681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03641-5_33
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DOI: https://doi.org/10.1007/978-3-642-03641-5_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03640-8
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