Seeded region growing: an extensive and comparative study
Pattern Recognition Letters
Traffic object detections and its action analysis
Pattern Recognition Letters
Image segmentation by unsupervised sparse clustering
Pattern Recognition Letters
Computers and Industrial Engineering
Image segmentation by a contrario simulation
Pattern Recognition
Vectorial scale-based fuzzy-connected image segmentation
Computer Vision and Image Understanding
Connectivity-based local adaptive thresholding for carotid artery segmentation using MRA images
Image and Vision Computing
Region growing with automatic seeding for semantic video object segmentation
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
Computer Vision and Image Understanding
Color image segmentation using adaptive unsupervised clustering approach
Applied Soft Computing
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Proposes a simple, yet general and powerful, region-growing framework for image segmentation. The region-growing process is guided by regional feature analysis; no parameter tuning or a priori knowledge about the image is required. To decide if two regions should be merged, instead of comparing the difference of region feature means with a predefined threshold, the authors adaptively assess region homogeneity from region feature distributions. This results in an algorithm that is robust with respect to various image characteristics. The merge criterion also minimizes the number of merge rejections and results in a fast region-growing process that is amenable to parallelization