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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.
A new neural network approach to stereovision is presented, which is able to fuse the left and right stereo images into the cyclopean view of the scene during disparity calculations. Fusion and disparity calculations are achieved within a single network structure by utilizing coherence detection among independently working, simple disparity estimators; the resulting dense disparity maps display hyper-acuity...