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Color image segmentation guided by a color gradient network

机译:色彩梯度网络指导的彩色图像分割

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摘要

Existing region-growing segmentation algorithms are mainly based on a static similarity concept, where only homogeneity of pixels or textures within a region plays a role. Typical natural scenes, however, show strong continuous variations of color, presenting a different, dynamic order that is not captured by existing algorithms which will segment a sky with different intensities and hues of blues or an irregularly illuminated surface as a set of different regions. We present and validate empirically a new, extremely simple approach that shows very satisfying results when applied on such scenes, while not showing poorer performance than traditional methods when applied to standard region-growing problems.
机译:现有的区域增长分割算法主要基于静态相似性概念,其中仅区域内像素或纹理的同质性才起作用。但是,典型的自然场景表现出强烈的连续颜色变化,呈现出不同的动态顺序,而现有算法则无法捕获这种动态顺序,现有算法会将具有不同强度和蓝色调的天空或不规则照明的表面分割为一组不同的区域。我们提出并凭经验验证了一种新的,极其简单的方法,该方法在此类场景中应用时显示出非常令人满意的结果,而在应用于标准区域增长问题时,其性能不比传统方法差。

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