Seeded region growing: an extensive and comparative study
By Jianping Fan, Guihua Zeng, Mathurin Body, Mohand-Said Hacid. Pattern Recognition Letters, 2005
319 citations recorded by the source corpus.
Seeded region growing (SRG) algorithm is very attractive for semantic image segmentation by involving high-level knowledge of image components in the seed selection procedure. However, the SRG algorithm also suffers from the problems of pixel sorting orders for labeling and automatic seed selection. An obvious way to improve the SRG algorithm is to provide more effective pixel labeling technique and automate the process of seed selection. To provide such a framework, we design an automatic SRG algorithm, along with a boundary-oriented parallel pixel labeling technique and an automatic seed selection method. Moreover, a seed tracking algorithm is proposed for automatic moving object extraction. The region seeds, which are located inside the temporal change mask, are selected for generating the regions of moving objects. Experimental evaluation shows good performances of our technique on a relatively large variety of images without the need of adjusting parameters.
Subfields: Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/2b7779d70fb1a3d90243fc6bfd843a2ab8c6b9fa
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Citations in the corpus
- Medical Image Segmentation Methods, Algorithms, and Applications (2014, methodology): “First, data are divided into k clusters and then the mean for each cluster will be calculated [28,43,62].”
- Imaging and image processing in porous media research (2008, background): “[51] provides a recent comparative study of seeded region growing methods.”