Progressive Tree-like Curvilinear Structure Reconstruction with Structured Ranking Learning and Graph Algorithm

Seong-Gyun Jeong 1 Yuliya Tarabalka 2 Nicolas Nisse 3 Josiane Zerubia 1
2 TITANE - Geometric Modeling of 3D Environments
CRISAM - Inria Sophia Antipolis - Méditerranée
3 COATI - Combinatorics, Optimization and Algorithms for Telecommunications
COMRED - COMmunications, Réseaux, systèmes Embarqués et Distribués, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : We propose a novel tree-like curvilinear structure reconstruction algorithm based on supervised learning and graph theory. In this work we analyze image patches to obtain the local major orientations and the rankings that correspond to the curvilinear structure. To extract local curvi-linear features, we compute oriented gradient information using steerable filters. We then employ Structured Support Vector Machine for ordinal regression of the input image patches, where the ordering is determined by shape similarity to latent curvilinear structure. Finally, we progressively reconstruct the curvilinear structure by looking for geodesic paths connecting remote vertices in the graph built on the structured output rankings. Experimental results show that the proposed algorithm faithfully provides topological features of the curvilinear structures using minimal pixels for various datasets.
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Pré-publication, Document de travail
2016
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https://hal.inria.fr/hal-01414864
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Seong-Gyun Jeong, Yuliya Tarabalka, Nicolas Nisse, Josiane Zerubia. Progressive Tree-like Curvilinear Structure Reconstruction with Structured Ranking Learning and Graph Algorithm. 2016. 〈hal-01414864〉

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