Abstract
A non-linear classifier is adopted in this chapter to represent the best view for 3D molecule of a protein onto the 2D screen plane. The classifier receives as inputs visual as well as semantic features and actually model the entropy needed to display with high performance the protein. The visual descriptors have been extracted in our case using the OpenCV tookit of the Intel Corporation, while the semantic information includes additional knowledge for the protein. Finally, an XML –based middleware is used to embed complex computer vision algorithms into the contemporary protein viewers which allow only limited transformations on the protein data structure. Experimental results on real-life protein molecules are presented to demonstrate the outperformance of the proposed algorithm.
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Doulamis, N., Chronis, E., Miaoulis, G., Plemenos, D. (2010). Personalized View Selection of 3D Molecular Proteins. In: Plemenos, D., Miaoulis, G. (eds) Intelligent Computer Graphics 2010. Studies in Computational Intelligence, vol 321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15690-8_11
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