A novel algorithm for image segmentation using time dependent interaction probabilities

  • R. Opara
  • F. Wörgötter
Poster Presentations 3 Sensory Processing
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1112)


For a consistent analysis of a visual scene the different features of an individual object have to be recognized as belonging together and separated from other objects and the background. Classical algorithms to segment a visual scene have an implicit representation of the image in the connection structure. We propose a new model that uses an image representation in the time domain, operating on stimulus dependent latencies. Such stimulus dependent temporal differences are observed in biological sensory systems. In our system they will be used to define the interaction probability between the different image parts. The gradually changing pattern of active image parts will thereby lead to the assignment of the different labels to different regions which leads to the segmentation of the scene.


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  1. 1.
    Burgi, P. Y. and Pun, T. Figure-ground separation: evidence for asynchronous processing in visual perception? Perception 20, 69, (1991).Google Scholar
  2. 2.
    McClurkin, J. W., Optican, L. M., Richmond, B. J. and Gawne, T. Concurrent processing and complexity of temporally encoded neural messages in visual perception. Science 253, 675–677, (1991).Google Scholar
  3. 3.
    Eckhorn, R., Frien, A., Bauer, R., Woelbern, T. & Kehr, H. High frequency (60–90 Hz) oscillations in primary visual cortex of awake monkey. NeuroReport, 4, 243–246 (1993).Google Scholar
  4. 4.
    Geman, D., Geman, S., Graffigne, C. & Dong, P. Boundary detection by constrained optimization. IEEE Trans. Pattern Analysis Machine Intelligence, 12(7), 609–628, (1990).Google Scholar
  5. 5.
    Gray, C.M., König, P., Engel, A.K. & Singer, W. Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties. Nature, 338, 334–337 (1989).Google Scholar
  6. 6.
    Hopfield, J. J. Pattern recognition computation using action potential timing for stimulus representation. Nature, 376, 33–36, (1995).Google Scholar
  7. 7.
    v.d. Malsburg, C. The correlation theory of brain function. Int. report 81-2, Dept. of Neurobiol. Max-Planck-Institute for Biophysical Chemistry, Göttingen, (1981).Google Scholar
  8. 8.
    Wörgötter, F., Opara, R., Funke, K. & Eysel, U. Utilizing latency for object recognition in real and artificial neural networks. NeuroReport, 7, 741–744 (1996).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • R. Opara
    • 1
  • F. Wörgötter
    • 1
  1. 1.Dept. of NeurophysiologyRuhr-UniversitätBochumGermany

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