Image segmentation using a hierarchical student's‐ t mixture model-Reference-Cited by-同舟云学术

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Image segmentation using a hierarchical student's‐ t mixture model

Published:2017-09-25 Issue:11 Volume:11 Page:1094-1102
ISSN:1751-9667
Container-title:IET Image Processing
language:en
Short-container-title:IET Image Processing

Author:

Kong Lingcheng¹,Zhang Hui¹,Zheng Yuhui¹,Chen Yunjie²,Zhu Jiezhong³,Wu Qingming M. Jonathan⁴

Affiliation:

1. School of Computer & Software, Nanjing University of Information Science & TechnologyNanjingPeople's Republic of China

2. School of Mathematics & Statistics, Nanjing University of Information Science & TechnologyNanjingPeople's Republic of China

3. Binjiang College, Nanjing University of Information Science & TechnologyNanjingPeople's Republic of China

4. Department of Electrical and Computer EngineeringUniversity of WindsorONCanadaN9B 3P4

Funder

National Natural Science Foundation of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-ipr.2017.0407

Reference31 articles.

1. A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters

2. A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data

3. Segmentation of M-FISH Images for Improved Classification of Chromosomes With an Adaptive Fuzzy C-means Clustering Algorithm

4. Image segmentation by generalized hierarchical fuzzy C-means algorithm

5. Nearness to local subspace algorithm for subspace and motion segmentation;Aldroubi A.;IEEE Signal Process. Lett.,2012

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