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On the Depth of Gestalt Hierarchies in Common Imagery

: Michaelsen, Eckart


Bimbo, A. del:
Pattern Recognition. ICPR International Workshops and Challenges. Proceedings. Pt.V : Virtual Event, January 10-15, 2021
Cham: Springer Nature, 2021 (Lecture Notes in Computer Science 12665)
ISBN: 978-3-030-68820-2 (Print)
ISBN: 978-3-030-68821-9 (Online)
ISBN: 978-3-030-68822-6
International Conference on Pattern Recognition (ICPR) <25, 2021, Online>
Conference Paper
Fraunhofer IOSB ()
hierarchical pattern composition; gestalt laws; perceptual grouping

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.