Abstract
A segmentation scheme based on tracing objects and borders through scale space is proposed. Scale space allows to create a hierarchical representation of input data which can be used to tessellate input space into objects with closed and orientable borders. For analyzing the structure of scale space, a neural network approach using synchronizing neural oscillators is proposed.
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© 1995 Springer-Verlag Berlin Heidelberg
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Henkel, R.D. (1995). Segmentation in scale space. In: Hlaváč, V., Šára, R. (eds) Computer Analysis of Images and Patterns. CAIP 1995. Lecture Notes in Computer Science, vol 970. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60268-2_278
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DOI: https://doi.org/10.1007/3-540-60268-2_278
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