IEEE Trans Image Process - Ridge network detection in crumpled paper via graph density maximization.

Tópicos

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Resumo

Crumpled sheets of paper tend to exhibit a specific and complex structure, which is described by physicists as ridge networks. Existing literature shows that the automation of ridge network detection in crumpled paper is very challenging because of its complex structure and measuring distortion. In this paper, we propose to model the ridge network as a weighted graph and formulate the ridge network detection as an optimization problem in terms of the graph density. First, we detect a set of graph nodes and then determine the edge weight between each pair of nodes to construct a complete graph. Next, we define a graph density criterion and formulate the detection problem to determine a subgraph with maximal graph density. Further, we also propose to refine the graph density by including a pairwise connectivity into the criterion to improve the connectivity of the detected ridge network. Our experimental results show that, with the density criterion, our proposed method effectively automates the ridge network detection.

Resumo Limpo

crumpl sheet paper tend exhibit specif complex structur describ physicist ridg network exist literatur show autom ridg network detect crumpl paper challeng complex structur measur distort paper propos model ridg network weight graph formul ridg network detect optim problem term graph densiti first detect set graph node determin edg weight pair node construct complet graph next defin graph densiti criterion formul detect problem determin subgraph maxim graph densiti also propos refin graph densiti includ pairwis connect criterion improv connect detect ridg network experiment result show densiti criterion propos method effect autom ridg network detect

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