원문정보
보안공학연구지원센터(IJSIP)
International Journal of Signal Processing, Image Processing and Pattern Recognition
Vol.9 No.6
2016.06
pp.259-268
피인용수 : 0건 (자료제공 : 네이버학술정보)
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
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
저자정보
참고문헌
자료제공 : 네이버학술정보