Abstract
Apart from machine learning and knowledge engineering, there is a third way of challenging machine vision – the Gestalt law school. In an interdisciplinary effort between psychology and cybernetics, compositionality in perception has been studied for at least a century along these lines. Hierarchical compositions of parts and aggregates are possible in this approach. This is particularly required for high-quality high-resolution imagery becoming more and more common, because tiny details may be important as well as large-scale interdependency over several thousand pixels distance. The contribution at hand studies the depth of Gestalt-hierarchies in a typical image genre – the group picture – exemplarily, and outlines technical means for their automatic extraction. The practical part applies bottom-up hierarchical Gestalt grouping as well as top-down search focusing, listing as well success as failure. In doing so, the paper discusses exemplarily the depth and nature of such compositions in imagery relevant to human beings.
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Michaelsen, E. (2021). On the Depth of Gestalt Hierarchies in Common Imagery. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12665. Springer, Cham. https://doi.org/10.1007/978-3-030-68821-9_3
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DOI: https://doi.org/10.1007/978-3-030-68821-9_3
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