A novel thin elongated objects segmentation based on fuzzy connectedness and GMM learning

Zhu, Qingsong, Ye, Ricang, Shao, Ling, Li, Qi and Xie, Yaoqin (2013) A novel thin elongated objects segmentation based on fuzzy connectedness and GMM learning. In: ICIP 2013 - 20th IEEE International Conference on Image Processing, 15th - 18th September 2013, Melbourne, Australia.

Full text not available from this repository. (Request a copy)

Abstract

Extraction of thin elongated objects from natural images is an important task in many computer vision applications such as image segmentation, object detection. Extensive approaches attempt to solve this issue with region features or prior knowledge, causing local minimum or short cut path. In this paper, we propose a semi-automatic method for the extraction of thin elongated objects. Given the input image, we manually label some foreground/background pixels as training samples. We use Guassian mixture model (GMM) to model background and extract object. We compute fuzzy affinity based on G-MM and take the framework of fuzzy connectedness (FC) to obtain fuzzy connected component. To obtain better result, we use adaptive components for GMM. Qualitative and quantitative comparisons show that our method outperforms many classical algorithms in terms of accuracy.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	GMM, fuzzy connectedness, image segmentation, thin elongated objects
Subjects:	G400 Computer Science
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Paul Burns
Date Deposited:	16 Jun 2015 13:03
Last Modified:	13 Oct 2019 00:36
URI:	http://nrl.northumbria.ac.uk/id/eprint/22951

Downloads