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ITS » Undergraduate Theses » Teknik Informatika
Posted by aguss at 07/01/2009 20:04:49  •  16851 Views


IMPLEMENTASI TRIPLET MARKOV RANDOM FIELDS DALAM SEGMENTASI CITRA

IMAGE SEGMENTATION USING TRIPLET MARKOV RANDOM FIELDS

Author :
PRAPTIKORASTRI, DHANTI 




ABSTRAK

Model Hidden Markov Random Fields HMRF digunakan secara luas untuk menangani berbagai masalah dalam image processing. Keberhasilan model HMRF disebabkan oleh distribusi probabilitas kondisional proses yang tersembunyi tersebut yang bersifat Markovian sehingga memungkinkan dilakukannya teknik restorasi Bayesian seperti Maximum Posterior Mode MPM. Tugas Akhir ini mengimplementasikan model Hidden Markov Random Fields HMRF dan model Triplet Markov Random Fields TMRF untuk mengembalikan data citra asal dari data citra yang tersisipi noise derau. Dalam model HMRF terdapat sebuah proses X yang tersembunyi hidden. Proses X merupakan sebuah field Markov dan harus diestimasi dari field terobservasi Y yang tersisipi noise derau. Belakangan ini model HMRF digeneralisasikan menjadi model Pairwise Markov Random Fields PMRF di mana pasangan XY secara langsung diasumsikan bersifat Markovian. Model PMRF kemudian digeneralisasi lagi menjadi model Triplet Markov Random Fields TMRF. Dalam model TMRF XY adalah distribusi marginal dari field Markov XUY di mana U adalah sebuah proses tambahan auxiliary process. Hasil uji coba yang sudah dilakukan menunjukkan bahwa model TMRF menghasilkan citra terestorasi dengan tingkat kesalahan yang lebih rendah daripada citra hasil restorasi dengan model HMRF.


ABSTRACT

Hidden Markov Random Fields HMRF models are widely applied to various problems in image processing. The success of HMRF is mainly due to the fact that the conditional probability distribution of the hidden process with respect to the observed one remains Markovian which facilitates Bayesian restoration technique such as Maximum Posterior Mode MPM. In this Final Project Hidden Markov Random Fields HMRF model and Triplet Markov Random Fields TMRF model are implemented to restore the hidden process of interest image from its noisy version. In HMRF model the hidden process of interest X is a Markov field and must be estimated from its observable noisy version Y . HMRF have been recently generalized to Pairwise Markov Random Fields PMRF where one directly assumes the Markovianity of the pair XY . PMRF model have also been generalized to Triplet Markov Random Fields TMRF model in which the distribution of the pair XY is the marginal distribution of a Markov field XUY where U is an auxiliary process. The test results from the experiments show that the error rate of the image that has been restored by TMRF model is smaller than the error rate of the image that has been restored by HMRF model.



KeywordsHidden Markov Random Fields, Triplet Markov Random Fields, Maximum Posterior Mode
 
Subject:  Program komputer
Contributor
  1. Rully Soelaiman, S.Kom., M.Kom.
    Chastine Fatichah, S.Kom
Date Create: 07/01/2009
Type: Text
Format: pdf
Language: Indonesian
Identifier: ITS-Undergraduate-3100008031122
Collection ID: 3100008031122
Call Number: RSIf 006.42 Pra i


Source
Undergraduate Theses, Informatics Engineering, RSIf 006.42 Pra i, 2008

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