Automatic image segmentation by integrating color-edge extraction and seeded region growing

By Jianping Fan, David K. Y. Yau, Ahmed K. Elmagarmid, Walid G. Aref. IEEE Transactions on Image Processing, 2001

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Piecewise Flat Embedding for Image Segmentation (2018, methodology): “It is also important for low-level operations, such as object extraction [3], image compositing [4] [5] and spatially varying photo enhancement [6].”
Hierarchical, model‐based merging of multiple fragments for improved three‐dimensional segmentation of nuclei (2005, background): “Hybrid techniques combine region and boundary criteria (14,15).”
Unsupervised extraction of visual attention objects in color images (2006, methodology): “The set of all unassigned adjacent pixels of , , could be simply modeled by [2]”
Seeded region growing: an extensive and comparative study (2005, methodology): “However, the SRG algorithm also suffers from the problems of automatic seed generation and pixel sorting orders for labeling (Mehnert and Jackway, 1997; Fan et al., 2001a).”
Automatic seeded region growing for color image segmentation (2005, methodology): “[24], color quantization is applied to form the classmap, so that some details of color information may be lost; however, we use the original color value.”