Abstract
Image understanding in the brain or a computer requires segmentation of observed images, i.e., their partition into different semantically-connected parts that each constitute one physical object. This task is fundamental for further processing and analysis of visual information and seems to be accomplished by the brain very easily. Nevertheless it is a very demanding challenge for computer algorithms.
In this article, we present a network of neuronal macrocolumns, which processes contour information by favoring closed contours. The connecting weights have been learned from real image sequences before. Then, segmentation is achieved on the basis of color, texture, and contour information.
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M. Wertheimer. Untersuchungen zur Lehre von der Gestalt II. Psychologische Forschung, 4:301-350, 1923.
Steven E. Palmer. Vision Science. MIT Press, 1999.
David Falk, Dieter Brill, and David Stork. Seeing the Light: Optics in Nature, Photography, Color, Vision, and Holography. John Wiley & Sons, New York, 1986.
David H. Hubel and Margaret S. Livingstone. Anatomy and the physiology of a color system in the primate visual cortex. Journal of Neuroscience, 4(1):309-356,1985.
Jörg Lücke, Christoph von der Malsburg, and Rolf P. Würtz. Macrocolumns as decision units. In José R. Dorronsoro, editor, Artificial Neural Networks ICANN 2002, Madrid, volume 2415 of LNCS, pages 57-62. Springer, 2002.
J. Lücke and C. von der Malsburg. Rapid processing and unsupervised learning in a model of the cortical macrocolumn. Neural Computation, 16(3):501-533, 2004.
Carsten Prodöhl, Rolf P. Würtz, and Christoph von der Malsburg. Learn- ing the gestalt rule of collinearity from object motion. Neural Computation, 15(8):1865-1896, 2003.
A. Artola, S. Bröcher, and W. Singer. Different voltage-dependent thresholds for inducing long-term depression and long-term potentiation in slices of rat visual cortex. Nature, 347:69-72, 1990.
S.A. Nene, S.K. Nayar, and H. Murase. Columbia object image library (COIL- 100). Technical Report CUCS-006-96, Columbia University, 1996.
Markus Lessmann. Konturenerkennung mit einem Modell kortikaler Makrokolumnen. Master’s thesis, Physics Dept., Univ. of Dortmund, Ger- many, January 2008.
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Lessmann, M., Würtz, R.P. (2008). Image Segmentation by a Network of Cortical Macrocolumns with Learned Connection Weights. In: Hinchey, M., Pagnoni, A., Rammig, F.J., Schmeck, H. (eds) Biologically-Inspired Collaborative Computing. BICC 2008. IFIP – The International Federation for Information Processing, vol 268. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09655-1_16
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DOI: https://doi.org/10.1007/978-0-387-09655-1_16
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