Reconstructing geometrically consistent tree structures from noisy images

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Abstract

We present a novel approach to fully automated reconstruction of tree structures in noisy 2D images. Unlike in earlier approaches, we explicitly handle crossovers and bifurcation points, and impose geometric constraints while optimizing a global cost function. We use manually annotated retinal scans to evaluate our method and demonstrate that it brings about a very substantial improvement. © 2010 Springer-Verlag.

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APA

Türetken, E., Blum, C., González, G., & Fua, P. (2010). Reconstructing geometrically consistent tree structures from noisy images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6361 LNCS, pp. 291–299). https://doi.org/10.1007/978-3-642-15705-9_36

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