Earticle

초록

영어

The mixture model is a commonly used approach for image segmentation. However, it doesn’t consider the spatial information. In order to overcome this disadvantage, several spatially constrained mixture models have been proposed. In this paper, these spatially constrained mixture models and their experimental results on synthetic and real world images are presented. These experimental results demonstrate that the spatially constrained mixture models can achieve competitive performance compared to the standard mixture model.

목차

Abstract
 1. Introduction
 2. A Review of Mixture Model-Based Methods for Image Segmentation
  2.1. Standard Mixture Model
  2.2. Spatially Variant Finite Mixture Model
  2.3. Class-Adaptive Spatially Finite Mixture Model
 3. Experiments
  3.1. Synthetic Images
  3.2. Real World Images
 4. Conclusion
 Acknowledgments
 References

키워드

저자

• Zhiyong Xiao [ Jiangnan University, Key Laboratory of Advanced Process Control for Light Industry, School of Internet of Things Engineering, Wuxi, China ]
• Yunhao Yuan [ Jiangnan University, Key Laboratory of Advanced Process Control for Light Industry, School of Internet of Things Engineering, Wuxi, China ]
• Jianjun Liu [ Jiangnan University, Key Laboratory of Advanced Process Control for Light Industry, School of Internet of Things Engineering, Wuxi, China ]
• Jinlong Yang [ Jiangnan University, Key Laboratory of Advanced Process Control for Light Industry, School of Internet of Things Engineering, Wuxi, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605