Symbolic feature detection for image understanding
Bildiriler > Tam Metin Bildiri
Yazarlar: Sinem Aslan, Ceyhun Burak Akgül, Bülent Sankur
SPIE Proceedings, , , Uluslararası, Tam Metin Bildiri, Scopus, cilt 9024, ss 902406-902406
DOI: 10.1117/12.2040783
Özet
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 achieved90% accuracy in identification of known objects from alternate views, however, we could not outperform texture, global and local shape methods, but only color-based method in recognition of unknown objects. We present a progress plan to be accomplished as a future work to improve the proposed approach further.