TY - GEN
T1 - A fast two-step marker-controlled watershed image segmentation method
AU - Han, Xianwei
AU - Fu, Yili
AU - Zhang, Haifeng
PY - 2012
Y1 - 2012
N2 - A fast two-step marker-controlled watershed image segmentation method in CIELAB color space is presented in this paper. We choose a number of seed points distributed nearly uniformly as the makers to perform the first marker watershed segmentation step, and obtain superpixels of the input image. These markers have the minimal gradient in a 3 x 3 neighborhood, which is able to avoid placing them at an edge and to reduce the chances of choosing a noise pixel. After superpixels segmentation, we do not adopt the traditional region merging strategies based on the different features of the adjacent regions, but cluster the superpixels in a 5-D space composed of Lab color vector and the position coordinates of the superpixels to resolve the over-segmentation problem, which saves a lot of computation time. Experiments on various types of images demonstrate that our algorithm is faster than many other segmentation algorithms and very suitable for real-time applications.
AB - A fast two-step marker-controlled watershed image segmentation method in CIELAB color space is presented in this paper. We choose a number of seed points distributed nearly uniformly as the makers to perform the first marker watershed segmentation step, and obtain superpixels of the input image. These markers have the minimal gradient in a 3 x 3 neighborhood, which is able to avoid placing them at an edge and to reduce the chances of choosing a noise pixel. After superpixels segmentation, we do not adopt the traditional region merging strategies based on the different features of the adjacent regions, but cluster the superpixels in a 5-D space composed of Lab color vector and the position coordinates of the superpixels to resolve the over-segmentation problem, which saves a lot of computation time. Experiments on various types of images demonstrate that our algorithm is faster than many other segmentation algorithms and very suitable for real-time applications.
KW - clustering
KW - image segmentation
KW - marker-controlled watershed
KW - superpixels segmentation
UR - https://www.scopus.com/pages/publications/84867594401
U2 - 10.1109/ICMA.2012.6284337
DO - 10.1109/ICMA.2012.6284337
M3 - 会议稿件
AN - SCOPUS:84867594401
SN - 9781467312776
T3 - 2012 IEEE International Conference on Mechatronics and Automation, ICMA 2012
SP - 1375
EP - 1380
BT - 2012 IEEE International Conference on Mechatronics and Automation, ICMA 2012
T2 - 2012 9th IEEE International Conference on Mechatronics and Automation, ICMA 2012
Y2 - 5 August 2012 through 8 August 2012
ER -