像素
计算机科学
边界(拓扑)
人工智能
图像分割
紧凑空间
分水岭
计算机视觉
分割
洪水(心理学)
同质性(统计学)
模式识别(心理学)
数学
机器学习
心理治疗师
心理学
纯数学
数学分析
作者
Ye Yuan,Zhiliang Zhu,Hai Yu,Wei Zhang
出处
期刊:IEEE transactions on image processing
[Institute of Electrical and Electronics Engineers]
日期:2020-01-01
卷期号:29: 7375-7388
被引量:24
标识
DOI:10.1109/tip.2020.3002078
摘要
Superpixels are widely used in computer vision applications, as they conserve the running costs of subsequent processing while preserving the original performance. In most of the existing algorithms, the boundary adherence and the compactness of superpixels are necessarily inter-inhibitive because the color/gradient information is balanced against the position constraints, and the set criteria define all pixels indiscriminately. In this paper, we present a two-phase superpixel segmentation method based on the watershed transformation. After designing a new approach for calculating the flooding priority, we propose a new strategy with two distinct criteria for global and local refinement of the boundary pixels. These criteria reduce the compromise between the boundary adherence and compactness. Unlike the indiscriminate standards, our method applies different treatments to pixels in different environments, preserving the color homogeneity in content-rich areas while improving the regularity of the superpixels in content-plain regions. The superior accuracy and computing time of our proposed method are verified in comparison experiments with several state-of-the-art methods.
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