Unsupervised Image Segmentation Using Adaptive Fragmentation in Parallel MRF-Based Windows Followed by Bayesian Clustering

スポンサーリンク

概要

• 論文の詳細を見る

• The approach presented in this paper was intended for extending conventional Markov random field (MRF) models to a more practical problem: the unsupervised and adaptive segmentation of gray-level images. The "unsupervised" segmentation means that all the model parameters, including the number of image classes, are unknown and have to be estimated from the observed image. In addition, the "adaptive" segmentation means that both the region distribution and the image feature within a region are all location-dependent and their corresponding parameters must be estimated from location to location. We estimated local parameters independently from multiple small windows under the assumption that an observed image consists of objects with smooth surfaces, no texture. Due to this assumption, the intensity of each region is a slowly varying function plus noise, and the conventional homogeneous hidden MRF (HMRF) models are appropriate for these windows. In each window, we employed the EM algorithm for maximum-likelihood (ML) parameter estimation, and then, the estimated parameters were used for "maximizer of the posterior marginals" (MPM) segmentation. To keep continuous segments between windows, a scheme for combining window fragments was proposed. The scheme comprises two parts: the programming of windows and the Bayesian merging of window fragments. Finally, a remerging procedure is used as post processing to remove the over-segmented small regions that possibly exist after the Bayesian merging; Since the final segments are obtained from merging, the number of image classes is automatically determined. The use of multiple parallel windows makes our algorithm to be suitable for parallel implementation. The experimental results of real-world images showed that the surfaces (objects) consistent with our reasonable model assumptions were all correctly segmented as connected regions.

• 社団法人電子情報通信学会の論文
• 1997-11-25

著者

• Chieu Bin-chang

  The Department Of Electronic Engineering National Taiwan Institute Of Technology

• HO Ken-Chung

  the Department of Electronic Engineering, National Taiwan Institute of Technology

• Ho Ken-chung

  The Department Of Electronic Engineering National Taiwan Institute Of Technology:the Chienkuo Jr. Co

関連論文

• Minimax Design of Two-Dimensional FIR Linear-Phase Quincunx Filter Banks Satisfying Perfect Reconstruction
• Unsupervised Image Segmentation Using Adaptive Fragmentation in Parallel MRF-Based Windows Followed by Bayesian Clustering
• A Note on a User Friendly Remote Authentication Scheme with Smart Cards(Information Security)

スポンサーリンク