A NEW STOCHASTIC IMAGE MODEL BASED ON MARKOV RANDOM FIELDS AND ITS APPLICATION TO TEXTURE MODELING

被引：0
|
作者：
Yousefi, Siamak [1 ]
Kehtarnavaz, Nasser [1 ]
机构：
[1] Univ Texas Dallas, Dept Elect Engn, Richardson, TX 75080 USA
来源：
2011 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING | 2011年
关键词：
Stochastic image models; Markov random field; image joint density function; texture modeling; SEGMENTATION;
D O I：
暂无
中图分类号：
O42 [声学];
学科分类号：
070206 ; 082403 ;
摘要：
Stochastic image modeling based on conventional Markov random fields is extensively discussed in the literature. A new stochastic image model based on Markov random fields is introduced in this paper which overcomes the shortcomings of the conventional models easing the computation of the joint density function of images. As an application, this model is used to generate texture patterns. The lower computational complexity and easily controllable parameters of the model makes it a more useful model as compared to the conventional Markov random field-based models.
引用
收藏
页码：1285 / 1288
页数：4
相关论文
共 50 条
  • [1] Computationally Tractable Stochastic Image Modeling Based on Symmetric Markov Mesh Random Fields
    Yousefi, Siamak
    Kehtarnavaz, Nasser
    Cao, Yan
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2013, 22 (06) : 2192 - 2206
  • [2] Texture image modeling and parameter estimation based on Gibbs random fields
    Suwa, M
    Sugimoto, S
    (SYSID'97): SYSTEM IDENTIFICATION, VOLS 1-3, 1998, : 501 - 506
  • [3] Dynamic Markov random fields for stochastic modeling of visual attention
    Kimura, Akisato
    Pang, Derek
    Takeuchi, Tatsuto
    Yamato, Junji
    Kashino, Kunio
    19TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1-6, 2008, : 340 - 344
  • [4] On the Asymptotic Variances of Gaussian Markov Random Field Model Hyperparameters in Stochastic Image Modeling
    Levada, Alexandre L. M.
    Mascarenhas, Nelson D. A.
    Tannus, Alberto
    19TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1-6, 2008, : 3358 - 3361
  • [5] A New Conception of Image Texture and Remote Sensing Image Segmentation Based on Markov Random Field
    GONG Yan1
    Geo-spatial Information Science, 2010, 13 (01) : 16 - 23
  • [6] A New Conception of Image Texture and Remote Sensing Image Segmentation Based on Markov Random Field
    Gong Yan
    Shu Ning
    Li Jili
    Lin Liqun
    Li Xue
    GEO-SPATIAL INFORMATION SCIENCE, 2010, 13 (01) : 16 - 23
  • [7] Markov random fields model and applications to image processing
    Smii, Boubaker
    AIMS MATHEMATICS, 2022, 7 (03): : 4459 - 4471
  • [8] Manifold Guided Composite of Markov Random Fields for Image Modeling
    Lin, Dahua
    Fisher, John
    2012 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2012, : 2176 - 2183
  • [9] Textured image segmentation using multiresolution Markov Random Fields and a two-component texture model
    Li, CT
    Wilson, R
    SCIA '97 - PROCEEDINGS OF THE 10TH SCANDINAVIAN CONFERENCE ON IMAGE ANALYSIS, VOLS 1 AND 2, 1997, : 425 - 430
  • [10] Ray Markov Random Fields for Image-Based 3D Modeling: Model and Efficient Inference
    Liu, Shubao
    Cooper, David B.
    2010 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2010, : 1530 - 1537
12345