A connectivity solution for extraction of thin objects

Antonelli, Marco; Dellepiane, S.; Vernazza, G.; Novelli, Lorena

doi:10.1109/icip.2004.1419457

Cited by 1 publication

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Candemir [9] improved the foreground segmentation by integrating the statistical significance measure into the graph energy minimization. For fuzzy connectedness technique, Antonelli [1] proposed a progressive scan mechanism which derived from fuzzy connectedness theory to extract thin structures. But this method may easily lead to bad path.…”

Section: Introductionmentioning

confidence: 99%

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

Zhu

Shao

et al. 2013

2013 IEEE International Conference on Image Processing

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%