서지주요정보
랜덤 필드 모델을 이용한 영상 모형화와 영상 분할에 관한 연구 = Image modeling and segmentation using random field models
서명 / 저자 랜덤 필드 모델을 이용한 영상 모형화와 영상 분할에 관한 연구 = Image modeling and segmentation using random field models / 김동우.
저자명 김동우 ; Kim, Dong-Woo
발행사항 [대전 : 한국과학기술원, 1997].
Online Access 원문보기 원문인쇄

소장정보

등록번호

8007540

소장위치/청구기호

학술문화관(문화관) 보존서고

DPH 97010

SMS전송

도서상태

이용가능

대출가능

반납예정일

초록정보

This paper investigated the problem of image modeling and segmentation using random field models. The paper presented the MLL(multi-level logistic) model, the auto-binomial model, and the GMRF(Gaussian Markov random field) model which are commonly used in image processing, and the characteristics of these models were examined. CM(Coding method), pseudo-likelihood maximization, and minimizing the sum of square errors were shown to estimate the parameters of the above models for various synthesized and natural texture images. For image segmentation, a doubly stochastic or hierarchical model was presented and applied to the segmentation of noisy images and textured images. This paper investigated the problem of phase transition phenomena appearing in the random field model-based image processing. The phase transition problem makes it difficult to realize images which consist of moderate-to-large scale regions. It also gives a negative effect on image segmentation and degrades segmentation performance. To solve the problems we proposed a Gibbs random field model whose energy function consists of interaction energy and magnetic energy between the neighbor pixels. It is shown that the proposed model can realize images having moderate-size clusters (or regions) and that the size of clusters can be controlled by the weighting factor between the two energy terms. The proposed model and the 2nd order neighborhood MLL model were applied to the segmenting binary and 4-level geometric images corrupted by additive Gaussian noise of three different levels. Both the models gave good results in the case of SNR=1. However, for the case of the very low SNR of 2/3 or 1/2, the proposed model turned out to be better.

서지기타정보

서지기타정보
청구기호 {DPH 97010
형태사항 ii, 84 p. : 삽도 ; 26 cm
언어 한국어
일반주기 저자명의 영문표기 : Dong-Woo Kim
지도교수의 한글표기 : 김수용
지도교수의 영문표기 : Soo-Yong Kim
수록잡지명 : "Stochastic Segmentation of Severely Degraded Images Using Gibbs Random Fields". Optical Review. Optical Society of Japan, vol. 3, no. 3, pp. 184-191 (1996)
학위논문 학위논문(박사) - 한국과학기술원 : 물리학과,
서지주기 참고문헌 : p. 77-84
주제 마코프 랜덤필드
깁스 랜덤필드
영상 분할
텍스춰 분할
상전이
Markov random field
Gibbs random field
Image segmentation
Texture segmentation
Simulated annealing
Phase transition
Multi-level logistic model
Auto-binomial model
Gaussian Markov random field
QR CODE qr code