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- W2076760935 abstract "In this study we propose a model-driven codebook generation method used to assign probability scores to pixels in order to represent underlying local shapes they reside in. In the first version of the symbol library we limited ourselves to photometric and similarity transformations applied on eight prototypical shapes of flat plateau , ramp, valley, ridge, circular and elliptic respectively pit and hill and used randomized decision forest as the statistical classifier to compute shape class ambiguity of each pixel. We achieved ~90% accuracy in identification of known objects from alternate views. For recognition of unknown objects, we could not outperform texture, global and local shape methods, but only color-based method in average, however we could outperform existing methods for three individual categories by exploiting multispectral analysis and multiscale approach. We present a progress plan to be accomplished as a future work to improve the proposed approach further." @default.
- W2076760935 created "2016-06-24" @default.
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- W2076760935 date "2014-03-07" @default.
- W2076760935 modified "2023-09-23" @default.
- W2076760935 title "Symbolic feature detection for image understanding" @default.
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- W2076760935 doi "https://doi.org/10.1117/12.2040783" @default.
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