Clustering segmentation fractal interpolation on nonlinear curves

Chih Chin Huang, Shu Chen Cheng, Yueh Min Huang

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


General fractal interpolation cannot deal with data concerning the features of closed curves, and the production or reconstruction of similar natural formations usually consumes a lot of resources and time, herein we propose Clustering Segmentation Fractal Interpolation (CSFI) to enhance the efficiency of data interpolation, especially for closed curves. Furthermore, we propose Affine-Distance Function Selection (ADFS) to reduce the iterations in order to achieve faster interpolation. Our series of experiments has demonstrated that the combination of the two methods increases the efficiency of CSFI by 57-97% and that of ADFS by 62-84%. The higher efficiency enhances the entire process with obvious effects.

Original languageEnglish
Pages (from-to)163-173
Number of pages11
Issue number2
Publication statusPublished - 2011 Jun

All Science Journal Classification (ASJC) codes

  • Modelling and Simulation
  • Geometry and Topology
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Clustering segmentation fractal interpolation on nonlinear curves'. Together they form a unique fingerprint.

Cite this