计算机科学
分水岭
人工智能
计算机视觉
同质性(统计学)
模式识别(心理学)
机器学习
作者
Zhongwen Hu,Qin Zou,Qingquan Li
标识
DOI:10.1109/icip.2015.7350818
摘要
As a pre-processing tool, superpixel algorithms have been popular used in many computer-vision applications. High efficiency is a desired property of superpixel algorithms, especially in real-time vision systems. In this paper, a novel high-efficient superpixel algorithm is developed based on the watershed algorithm, namely the spatial-constrained watershed (SCoW). SCoW performs watersheding in a marker-controlled manner, with a set of evenly placed markers. To align superpixel boundaries to image edges, an edge-preserving scheme is embedded into the SCoW which makes a balance between the homogeneity and the compactness. Without any complex computing, the proposed superpixel algorithm is found to produce high quality superpixels as traditional superpixel algorithms, while holding much higher efficiency.
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