Literature DB >> 18276310

Texture synthesis via a noncausal nonparametric multiscale Markov random field.

R Paget1, I D Longstaff.   

Abstract

Our noncausal, nonparametric, multiscale, Markov random field (MRF) model is capable of synthesizing and capturing the characteristics of a wide variety of textures, from the highly structured to the stochastic. We use a multiscale synthesis algorithm incorporating local annealing to obtain larger realizations of texture visually indistinguishable from the training texture.

Year:  1998        PMID: 18276310     DOI: 10.1109/83.679446

Source DB:  PubMed          Journal:  IEEE Trans Image Process        ISSN: 1057-7149            Impact factor:   10.856


  4 in total

1.  High-throughput detection of prostate cancer in histological sections using probabilistic pairwise Markov models.

Authors:  James P Monaco; John E Tomaszewski; Michael D Feldman; Ian Hagemann; Mehdi Moradi; Parvin Mousavi; Alexander Boag; Chris Davidson; Purang Abolmaesumi; Anant Madabhushi
Journal:  Med Image Anal       Date:  2010-04-29       Impact factor: 8.545

2.  Class-specific weighting for Markov random field estimation: application to medical image segmentation.

Authors:  James P Monaco; Anant Madabhushi
Journal:  Med Image Anal       Date:  2012-07-16       Impact factor: 8.545

3.  Texture Analysis and Synthesis of Malignant and Benign Mediastinal Lymph Nodes in Patients with Lung Cancer on Computed Tomography.

Authors:  Tuan D Pham; Yuzuru Watanabe; Mitsunori Higuchi; Hiroyuki Suzuki
Journal:  Sci Rep       Date:  2017-02-24       Impact factor: 4.379

4.  Comparative Analysis of the Performance of Complex Texture Clustering Driven by Computational Intelligence Methods Using Multiple Clustering Models.

Authors:  Jincheng Zhou; Dan Wang; Lei Ling; Mingjiang Li; Khin-Wee Lai
Journal:  Comput Intell Neurosci       Date:  2022-09-29
  4 in total

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