Abstract
We design a probabilistic trajectory synthesis algorithm for generating time-varying sequences of geometric configuration data. The algorithm takes a set of observed samples (each may come from a different trajectory) and simulates the dynamic evolution of the patterns in O(n 2 logn) time. To synthesize geometric configurations with indistinct identities, we use the pair correlation function to summarize point distribution, and α-shapes to maintain topological shape features based on a fast persistence matching approach. We apply our method to build a computational model for the geometric transformation of the cone mosaic in retinitis pigmentosa — an inherited and currently untreatable retinal degeneration.
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Gu, C., Guibas, L., Kerber, M. (2014). Topology-Driven Trajectory Synthesis with an Example on Retinal Cell Motions. In: Brown, D., Morgenstern, B. (eds) Algorithms in Bioinformatics. WABI 2014. Lecture Notes in Computer Science(), vol 8701. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44753-6_24
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DOI: https://doi.org/10.1007/978-3-662-44753-6_24
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