Estimasi Citra Kedalaman Dengan Conditional Random Field (CRF) dan Structured Support Vector Machine (SSVM)

  • Muhammad Rachmadi
  • Derry Alamsyah STMIK Global Informatika MDP

Abstract

Autonomous UAVs typically require systems capable of mapping (segmenting) the region of a colored image. Regional segmentation will be used to determine the existence of obstacles. Segmentation in an image initiated by grouping a number of pixels by color and proximity, with superpixel. Furthermore, the characteristics of each segmented region were extracted using Principal Component Analysis (PCA). Sequential segregation is a process of introducing a region that requires chain information obtained from the introduction before and after it called stochastic process. Solving this is done using Structured Support Vector Machines (SSVM). Meanwhile, to determine the weight in SSVM required a Conditional Random Field (CRF) model. The results show a good accuracy in the segmentation of the region that is 71%.

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Published
2017-12-04
How to Cite
RACHMADI, Muhammad; ALAMSYAH, Derry. Estimasi Citra Kedalaman Dengan Conditional Random Field (CRF) dan Structured Support Vector Machine (SSVM). Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), [S.l.], v. 1, n. 3, p. 198 - 203, dec. 2017. ISSN 2580-0760. Available at: <http://jurnal.iaii.or.id/index.php/RESTI/article/view/64>. Date accessed: 18 dec. 2017. doi: https://doi.org/10.29207/resti.v1i3.64.
Section
Technology Information Article