ORASSYLL: Object Recognition with Autonomously Learned and Sparse Symbolic Representations Based on Metrically Organized Local Line Detectors

@article{Krger2000ORASSYLLOR,
  title={ORASSYLL: Object Recognition with Autonomously Learned and Sparse Symbolic Representations Based on Metrically Organized Local Line Detectors},
  author={Norbert Kr{\"u}ger and Gabriele Peters},
  journal={Computer Vision and Image Understanding},
  year={2000},
  volume={77},
  pages={48-77}
}
We introduce an object recognition and localization system in which objects are represented as a sparse and spatially organized set of local (bent) line segments. The line segments correspond to binarized Gabor wavelets or banana wavelets, which are bent and stretched Gabor wavelets. These features can be metrically organized, the metric enables an e cient learning of object representations. It is essential for learning that only corresponding local areas are compared with each other, i.e., the… CONTINUE READING
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